A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

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Angle rigid formations can simultaneously undergo translational, rotational, and scaling maneuvering, therefore combining the maneuvering capabilities of both distance and bearing rigid formations. However, maneuvering angle rigid formations in 2D or 3D with global convergence guarantees is shown to be a challenging problem in the existing literature even when relative position measurements are available. Motivated by angle-induced linear equations in 2D triangles and 3D tetrahedra, this paper aims to solve this challenging problem in both 2D and 3D under a leader-follower framework. For the 2D case where the leaders have constant velocity, by using local relative position and velocity measurements, a formation maneuvering law is designed for the followers governed by double-integrator dynamics. When the leaders have time-varying velocity, a sliding mode formation maneuvering law is proposed by using the same measurements. For the 3D case, to establish an angle-induced linear equation for each tetrahedron, we assume that all the followers' coordinate frames share a common Z direction. Then, a formation maneuvering law is proposed for the followers to globally maneuver Z-weakly angle rigid formations in 3D. The extension to Lagrangian agent dynamics and the construction of the desired rigid formations by using the minimum number of angle constraints are also discussed. Simulation examples are provided to validate the effectiveness of the proposed algorithms.
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Due to the non-standardization and complexity of the farmland environment, it is always a huge challenge for tractors to achieve fully autonomy (work at Self-driving mode) all the time in agricultural industry. Whereas, when tractors work in the Tele-driving (or Remote driving) mode, the operators are prone to fatigue because they need to concentrate for long periods of time. In response to these, a dual-mode control strategy was proposed to integrate the advantages of both approaches, i.e., by combing Self-driving at most of the time with Tele-driving under special (complex and hazardous) conditions through switching control method. First, the state switcher was proposed, which is used for smooth switching the driving modes according to different working states of a tractor. Then, the state switching control law and the corresponding subsystem tracking controllers were designed. Finally, the effectiveness and superiority of the dual-mode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly, stably, and efficiently between the two driving modes automatically. The average control accuracy has been improved by 20% and 15% respectively, compared to the conventional Tele-driving control and Self-driving control with low-precision navigation. In conclusion, the proposed dual-mode control method can not only satisfy the operation in the complex and changeable farmland environment, but also free drivers from high-intensity and fatiguing work. This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.
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Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces. While evolutionary algorithms are good at solving small-scale multi-objective optimization problems, they are criticized for low efficiency in converging to the optimums of LSMOPs. By contrast, mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems, but they have difficulties in finding diverse solutions for LSMOPs. Currently, how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored. In this paper, a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method. On the one hand, conjugate gradients and differential evolution are used to update different decision variables of a set of solutions, where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front. On the other hand, objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions, and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent. In comparison with state-of-the-art evolutionary algorithms, mathematical programming methods, and hybrid algorithms, the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.
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Recent years have witnessed the increasing popularity of mobile and networking devices, as well as social networking sites, where users engage in a variety of activities in the cyberspace on a daily and real-time basis. While such systems provide tremendous convenience and enjoyment for users, malicious usages, such as bullying, cruelty, extremism, and toxicity behaviors, also grow noticeably, and impose significant threats to individuals and communities. In this paper, we review computational approaches for cyberbullying and cyberviolence detection, in order to understand two major factors: 1) What are the defining features of online bullying users, and 2) How to detect cyberbullying and cyberviolence. To achieve the goal, we propose a user-activities-content (UAC) triangular view, which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate content. Accordingly, we categorize cyberbully features into three main categories: 1) User centered features, 2) Content centered features, and 3) Activity centered features. After that, we review methods for cyberbully detection, by taking supervised, unsupervised, transfer learning, and deep learning, etc., into consideration. The UAC centered view provides a coherent and complete summary about features and characteristics of online users (their activities), approaches to detect bullying users (and malicious content), and helps defend cyberspace from bullying and toxicity.
, Available online  , doi: 10.1109/JAS.2022.105701
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, Available online  , doi: 10.1109/JAS.2022.105518
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Multi-label learning deals with objects associated with multiple class labels, and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance. Since each class might possess its own characteristics, the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning, where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations. As a representative approach, LIFT generates label-specific features by conducting clustering analysis. However, its performance may be degraded due to the inherent instability of the single clustering algorithm. To improve this, a novel multi-label learning approach named SENCE (stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble) is proposed, which stabilizes the generation process of label-specific features via clustering ensemble techniques. Specifically, more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization (EM) algorithm. Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
, Available online  , doi: 10.1109/JAS.2022.105527
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, Available online  , doi: 10.1109/JAS.2022.105668
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The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework. The merits of the proposed MORF come from the following two components: A tree-wise weighting net (TWW-Net) and a grouped feature selection (GFS) module. First, the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree. Hence, all the trees possess varying weights, which is helpful for alleviating the tree-wise prediction variance. Second, the GFS module enables a dynamic forest rather than a fixed one that was previously used, allowing for random feature perturbation. During training, we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix. Experimental results on two medical image classification datasets with ordinal labels, i.e., LIDC-IDRI and Breast Ultrasound datasets, demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
, Available online  , doi: 10.1109/JAS.2022.105713
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Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared to their real counterparts in speech enhancement, image and signal processing. Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs. Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals, this area of study will grow and expect the arrival of some effective improvements in the future. Therefore, there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs. In this paper, we discuss and summarize the recent advances based on their learning algorithms, activation functions, which is the most challenging part of building a CVNN, and applications. Besides, we outline the structure and applications of complex-valued convolutional, residual and recurrent neural networks. Finally, we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.
, Available online  , doi: 10.1109/JAS.2022.105692
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The core task of tracking control is to make the controlled plant track a desired trajectory. The traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps increases. In this paper, a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control problem. Unlike the regulator problem, the iterative value function of tracking control problem cannot be regarded as a Lyapunov function. A novel stability analysis method is developed to guarantee that the tracking error converges to zero. The discounted iterative scheme under the new cost function for the special case of linear systems is elaborated. Finally, the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.
, Available online  , doi: 10.1109/JAS.2022.105695
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Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA’s efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.
, Available online  , doi: 10.1109/JAS.2022.105680
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Signed networks refer to a class of network systems including not only cooperative but also antagonistic interactions among nodes. Due to the existence of antagonistic interactions in signed networks, the agreement of nodes may not be established, instead of which disagreement behaviors generally emerge. This paper reviews several different disagreement behaviors in signed networks under the single-integrator linear dynamics, where two classes of topologies, namely, the static topology and the dynamic topology, are considered. For the static signed networks with the adjacency weights as (time-varying) scalars, we investigate the convergence behaviors and the fluctuation behaviors with respect to fixed topologies and switching topologies, respectively, and give some brief introductions on the disagreement behaviors of general time-varying signed networks. Correspondingly, several classes of behavior analysis approaches are also provided. For the dynamic signed networks with the adjacency weights as transfer functions or linear time-invariant systems, we show the specific descriptions and characteristics of them such that the disagreement behaviors can be obtained by resorting to the derived static signed graphs. Furthermore, we give their applications to the behavior analysis of static signed networks in the presence of high-order dynamics or communication delays.
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Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers. In this work, a novel two-layer reinforcement learning behavioral control (RLBC) method is proposed to reduce such dependence by trial-and-error learning. Specifically, in the upper layer, a reinforcement learning mission supervisor (RLMS) is designed to learn the optimal mission priority. Compared with existing mission supervisors, the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks. In the lower layer, a reinforcement learning controller (RLC) is designed to learn the optimal control policy. Compared with existing behavioral controllers, the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption. All error signals are proved to be semi-globally uniformly ultimately bounded (SGUUB). Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers, respectively.
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Accurate remaining useful life (RUL) prediction is important in industrial systems. It prevents machines from working under failure conditions, and ensures that the industrial system works reliably and efficiently. Recently, many deep learning based methods have been proposed to predict RUL. Among these methods, recurrent neural network (RNN) based approaches show a strong capability of capturing sequential information. This allows RNN based methods to perform better than convolutional neural network (CNN) based approaches on the RUL prediction task. In this paper, we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN, which reduces their performances. Additionally, the capacity of capturing sequential information is highly affected by the receptive field of CNN, which is neglected by existing CNN based methods. To solve these problems, we propose a series of new CNNs, which show competitive results to RNN based methods. Compared with RNN, CNN processes the input signals in parallel so that the temporal sequence is not easily determined. To alleviate this issue, a position encoding scheme is developed to enhance the sequential information encoded by a CNN. Hence, our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods. Extensive experiments are conducted on the C-MAPSS dataset, where our PE-Net shows state-of-the-art performance.
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For a class of high-order nonlinear multi-agent systems with input hysteresis, an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated. The major properties of the proposed control scheme are: 1) According to the different hysteresis input characteristics of each agent in the multi-agent system, a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value. 2) A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system. By constructing state constraint control strategy for the hysteretic multi-agent system, it ensures that all the states of the system are always maintained within a predetermined range. 3) The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance, and ensures that the input signal transmitted between agents is the quantization value, and the introduced quantizer is implemented under the condition that only its sector bound property is required. The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded. The StarSim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.
, Available online  , doi: 10.1109/JAS.2022.105710
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, Available online  , doi: 10.1109/JAS.2022.105512
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In the conventional robust optimization (RO) context, the uncertainty is regarded as residing in a predetermined and fixed uncertainty set. In many applications, however, uncertainties are affected by decisions, making the current RO framework inapplicable. This paper investigates a class of two-stage RO problems that involve decision-dependent uncertainties. We introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem. A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling. The computational tractability, robust feasibility and optimality, and convergence performance of the proposed algorithm are guaranteed with theoretical proof. Four motivating application examples that feature the decision-dependent uncertainties are provided. Finally, the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.
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, Available online  , doi: 10.1109/JAS.2022.105707
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, Available online  , doi: 10.1109/JAS.2022.105533
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This work proposes a novel proportional-derivative (PD)-type state-dependent Riccati equation (SDRE) approach with iterative learning control (ILC) augmentation. On the one hand, the PD-type control gains could adopt many useful available criteria and tools of conventional PD controllers. On the other hand, the SDRE adds nonlinear and optimality characteristics to the controller, i.e., increasing the stability margins. These advantages with the ILC correction part deliver a precise control law with the capability of error reduction by learning. The SDRE provides a symmetric-positive-definite distributed nonlinear suboptimal gain K(x) for the control input law u = –R–1(x)BT(x)K(x)x. The sub-blocks of the overall gain R–1(x)BT(x)K(x), are not necessarily symmetric positive definite. A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as u = –KSP(x)e–KSD(x)ė. The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems; and presents guaranteed uniform boundedness in finite-time between learning loops. The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation (SDDRE) to manipulate the final time. The SDDRE expresses a differential equation with a final boundary condition, which imposes a constraint on time that could be used for finite-time control. So, the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool. The learning rules benefit from the gradient descent method for both regulation and tracking cases. One of the advantages of this approach is a guaranteed-stability even from the first loop of learning. A mechanical manipulator, as an illustrative example, was simulated for both regulation and tracking problems. Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.
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With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.
, Available online  , doi: 10.1109/JAS.2022.105674
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In this paper, a novel finite-time distributed identification method is introduced for nonlinear interconnected systems. A distributed concurrent learning-based discontinuous gradient descent update law is presented to learn uncertain interconnected subsystems’ dynamics. The concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems. The state information of neighboring interconnected subsystems is acquired through direct communication. The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed. As a byproduct of this Lyapunov analysis, easy-to-check rank conditions on data stored in the distributed memories of subsystems are obtained, under which finite-time stability of the distributed identifier is guaranteed. These rank conditions replace the restrictive persistence of excitation (PE) conditions which are hard and even impossible to achieve and verify for interconnected subsystems. Finally, simulation results verify the effectiveness of the presented distributed method in comparison with the other methods.
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The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered learning-based optimization (LBO) algorithms to solve the VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches. We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms. Finally, we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.
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, Available online  , doi: 10.1109/JAS.2022.105599
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Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.
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This paper investigates the problem of path tracking control for autonomous ground vehicles (AGVs), where the input saturation, system nonlinearities and uncertainties are considered. Firstly, the nonlinear path tracking system is formulated as a linear parameter varying (LPV) model where the variation of vehicle velocity is taken into account. Secondly, considering the noise effects on the measurement of lateral offset and heading angle, an observer-based control strategy is proposed, and by analyzing the frequency domain characteristics of the derivative of desired heading angle, a finite frequency H index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error. Thirdly, sufficient conditions are derived to guarantee robust H performance of the path tracking system, and the calculation of observer and controller gains is converted into solving a convex optimization problem. Finally, simulation examples verify the advantages of the control method proposed in this paper.
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This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs. The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees. This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree. A distributed adaptive pole placement control scheme is developed, which consists of a distributed observer and an adaptive pole placement control law. It is shown that under the proposed distributed adaptive control scheme, all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically. The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.
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In multi-agent reinforcement learning (MARL), the behaviors of each agent can influence the learning of others, and the agents have to search in an exponentially enlarged joint-action space. Hence, it is challenging for the multi-agent teams to explore in the environment. Agents may achieve suboptimal policies and fail to solve some complex tasks. To improve the exploring efficiency as well as the performance of MARL tasks, in this paper, we propose a new approach by transferring the knowledge across tasks. Differently from the traditional MARL algorithms, we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of task-specific weights. Then, we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further use. Finally, once the weights for target tasks are available, it will be easier to get a well-performed policy to explore in the target domain. Hence, the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned previously. We evaluate the proposed algorithm on two challenging MARL tasks: cooperative box-pushing and non-monotonic predator-prey. The experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.
, Available online  , doi: 10.1109/JAS.2022.105602
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Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video. However, how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge. In this paper, we propose a novel model of structured sparse-coding-based key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error. To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows. The rows enforced by the nonconvex group log-regularizer become zero or nonzero, leading to the learning of the structured sparse coefficient matrix. To solve the nonconvex problems due to the log-regularizer, the difference of convex algorithm (DCA) is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm, which can be directly obtained through the proximal operator. Therefore, an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed, which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error. Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most SumMe videos compared to the state-of-the-art methods. Furthermore, the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to SMRS and an 8% increase compared to SC-det on the VSUMM dataset.
, Available online  , doi: 10.1109/JAS.2022.105530
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High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.
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Recently, Multimodal Multiobjective Optimization Problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solutions diversity. In this paper, we propose a new multiobjective fireworks algorithm for them, which is able to balance exploitation and exploration in the decision space. We first extend a latest single-objective fireworks algorithm to handle MMOPs. Then we make improvements by incorporating an adaptive strategy and special archive guidance into it, where special archives are established for each firework, and two strategies (i.e., explosion and random strategies) are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives. Finally, we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems. Experimental results show that the proposed algorithm is superior to compared algorithms in solving them. Also, its runtime is less than its peers'.
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This paper suggests the use of zonotopes for the design of watermark signals. The proposed approach exploits the recent analogy found between stochastic and zonotopic-based estimators to propose a deterministic counterpart to current approaches that study the replay attack in the context of stationary Gaussian processes. In this regard, the zonotopic analogous case where the control loop is closed based on the estimates of a Zonotopic Kalman Filter (ZKF) is analyzed. This formulation allows to propose a new performance metric that is related to the Frobenius norm of the prediction zonotope. Hence, the steady-state operation of the system can be related with the size of the minimal Robust Positive Invariant set of the estimation error. Furthermore, analogous expressions concerning the impact that a zonotopic/Gaussian watermark signal has on the system operation are derived. Finally, a novel zonotopically bounded watermark signal that ensures the attack detection by causing the residual vector to exit the healthy residual set during the replay phase of the attack is introduced. The proposed approach is illustrated in simulation using a quadruple-tank process.
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This paper studies the connectivity-maintaining consensus of multi-agent systems. Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption, a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved. In this paper, communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus. The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller, so there is more freedom to develop an appropriate control strategy for achieving consensus. For the multi-agent systems with this novel communication management, a predictive control based strategy is developed for achieving consensus. Simulation results indicate the effectiveness and advantages of our scheme.
, Available online  , doi: 10.1109/JAS.2022.105665
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The attitude tracking operations of an on-orbit spacecraft with degraded performance exhibited by potential actuator uncertainties (including failures and misalignments) can be extraordinarily challenging. Thus, the control law development for the attitude tracking task of spacecraft subject to actuator (namely reaction wheel) uncertainties is addressed in this paper. More specially, the attitude dynamics model of the spacecraft is firstly established under actuator failures and misalignment (without a small angle approximation operation). Then, a new non-singular sliding manifold with fixed time convergence and anti-unwinding properties is proposed, and an adaptive sliding mode control (SMC) strategy is introduced to handle actuator uncertainties, model uncertainties and external disturbances simultaneously. Among this, an explicit misalignment angles range that could be treated herein is offered. Lyapunov-based stability analyses are employed to verify that the reaching phase of the sliding manifold is completed in finite time, and the attitude tracking errors are ensured to converge to a small region of the closest equilibrium point in fixed time once the sliding manifold enters the reaching phase. Finally, the beneficial features of the designed controller are manifested via detailed numerical simulation tests.
, Available online  , doi: 10.1109/JAS.2022.105524
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This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
, Available online  , doi: 10.1109/JAS.2022.105536
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As one of the typical applications of connected vehicles (CVs), the vehicle platoon control technique has been proven to have the advantages of reducing emissions, improving traffic throughout and driving safety. In this paper, a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures. By separating neighboring information interaction from local dynamics control, the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer, which helps address the heterogeneity in vehicle parameters and structures. Within the proposed framework, an observer is designed for following vehicles to observe the leading vehicle’s states using neighboring communication, while a tracking controller is designed to track the observed leading vehicle using local feedback control. Closed-loop stability in the absence and presence of communication time delay is analyzed, and the observer is further extended to a finite time convergent one to address string stability under general communication topology. Numerical simulation and field experiment verify the effectiveness of the proposed method.
, Available online  , doi: 10.1109/JAS.2022.105605
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For constrained linear parameter varying (LPV) systems, this survey comprehensively reviews the literatures on output feedback robust model predictive control (OFRMPC) over the past two decades from the aspects on motivations, main contributions, and the related techniques. According to the types of state observer systems and scheduling parameters of LPV systems, different kinds of OFRMPC approaches are summarized and compared. The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated. The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given. Key issues on OFRMPC optimizations for LPV systems are discussed. Furthermore, the future research directions on OFRMPC for LPV systems are suggested.
, Available online  , doi: 10.1109/JAS.2022.105545
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This paper investigates adaptive containment control for a class of fractional-order multi-agent systems (FOMASs) with time-varying parameters and disturbances. By using the bounded estimation method, the difficulty generated by the time-varying parameters and disturbances is overcome. The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control. Meanwhile, in order to eliminate the effect of filter errors, a novel distributed error compensating scheme is constructed, in which only the local information from the neighbor agents is utilized. Then, a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders. Based on the extension of Barbalat's lemma to fractional-order integrals, it can be proven that the containment errors and the compensating signals have asymptotic convergence. Finally, three simulation examples are given to show the feasibility and effectiveness of the proposed control method.
, Available online
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This paper develops a fully distributed hybrid control framework for distributed constrained optimization problems. The individual cost functions are non-differentiable and convex. Based on hybrid dynamical systems, we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization methods. The proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump set. With the semistability theory of hybrid dynamical systems, the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different solutions. Numerical simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.
, Available online
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This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization (TLPIO) for unmanned combat aerial vehicles (UCAVs) in dogfight engagements. Firstly, a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform. Secondly, a 3-degrees-of-freedom (3-DOF) aircraft model is used as a maneuvering command generator, and the expanded elemental maneuver library is designed, so that the aircraft state reachable set can be obtained. Then, the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats. Finally, a key point is that the objective function to be optimized is designed using the game mixed strategy, and the optimal mixed strategy is obtained by TLPIO. Significantly, the proposed TLPIO does not initialize the population randomly, but adopts the transfer learning method based on Kullback-Leibler (KL) divergence to initialize the population, which improves the search accuracy of the optimization algorithm. Besides, the convergence and time complexity of TLPIO are discussed. Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO. In the simulation of air combat, three initial scenarios are set, namely, opposite, offensive and defensive conditions. The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.
, Available online
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, Available online  , doi: 10.1109/JAS.2022.105425
Abstract:
This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.
, Available online  , doi: 10.1109/JAS.2022.105542
Abstract:
This paper focuses on the distributed cooperative learning (DCL) problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs. Compared with the previous DCL works based on undirected graphs, two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric, and the derived weight error systems exist n-step delays. Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying (LTV) systems with different phenomena including the nonsymmetric Laplacian matrix and time delays. Subsequently, an adaptive neural network (NN) control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law. Then, by using two novel lemmas on the extended exponential convergence of LTV systems, estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced. The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the “mod” function and proper time series. A simulation comparison is shown to demonstrate the validity of the proposed DCL method.
, Available online
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In recent visual tracking research, correlation filter (CF) based trackers become popular because of their high speed and considerable accuracy. Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter. However, the related studies are insufficient. By exploring the potential of trackers in these two aspects, a novel adaptive padding correlation filter with feature group fusion (APCF) is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework. In the tracker, three feature groups are fused by use of the weighted sum of the normalized response maps, to alleviate the risk of drift caused by the extreme change of single feature. Moreover, to improve the adaptive ability of padding for the filter training of different object shapes, the best padding is selected from the preset pool according to tracking precision over the whole video, where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames. The sequence features include three traditional features and eight newly constructed features. Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.
, Available online  , doi: 10.1109/JAS.2022.105440
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This paper is concerned with order-preserved preset-time cooperative control of multi-agent systems with directed graphs. A novel monotone system-based approach is proposed to preserve the initial order of agents while guaranteeing the preset-time state agreement. Specifically, three different distributed controllers together with sufficient conditions are designed to realize leaderless consensus, leader-following consensus, and containment control, respectively. The proposed controllers facilitate preset-time deployment of agents in practical scenarios with collision avoidance requirement. Comparison studies through a numerical example are carried out to illustrate the effectiveness of the proposed controllers.
, Available online  , doi: 10.1109/JAS.2022.105521
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Weighted vertex cover (WVC) is one of the most important combinatorial optimization problems. In this paper, we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks. We first model the WVC problem as a general game on weighted networks. Under the framework of a game, we newly define several cover states to describe the WVC problem. Moreover, we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums (SNEs) of the game. Then, we propose a game-based asynchronous algorithm (GAA), which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time. Subsequently, we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms, termed the improved game-based asynchronous algorithm (IGAA), in which we prove that it can obtain a better solution to the WVC problem than using a the GAA. Finally, numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
, Available online  , doi: 10.1109/JAS.2022.105437
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During the last three decades, evolutionary algorithms (EAs) have shown superiority in solving complex optimization problems, especially those with multiple objectives and non-differentiable landscapes. However, due to the stochastic search strategies, the performance of most EAs deteriorates drastically when handling a large number of decision variables. To tackle the curse of dimensionality, this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions. The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution, and provides a fast clustering method to highly reduce the dimensionality of the search space. More importantly, all the operations related to the decision variables only contain several matrix calculations, which can be directly accelerated by GPUs. While existing EAs are capable of handling fewer than 10 000 real variables, the proposed algorithm is verified to be effective in handling 1 000 000 real variables. Furthermore, since the proposed algorithm handles the large number of variables via accelerated matrix calculations, its runtime can be reduced to less than 10% of the runtimes of existing EAs.
, Available online  , doi: 10.1109/JAS.2022.105515
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Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples. To solve this problem, we propose a multi-modal domain adaptive variational autoencoder (MMDA-VAE) method, which learns shared cross-domain latent representations of the multi-modal data. Our method builds a multi-modal variational autoencoder (MVAE) to project the data of multiple modalities into a common space. Through adversarial learning and cycle-consistency regularization, our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge. Extensive experiments are conducted on two public datasets, SEED and SEED-IV, and the results show the superiority of our proposed method. Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
, Available online  , doi: 10.1109/JAS.2022.105509
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This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles (ASVs) under switching interaction topologies. For the target to be tracked, only its position can be measured/received by some of the ASVs, and its velocity is unavailable to all the ASVs. A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs' dynamics. Accordingly, a novel kinematic controller is designed, which takes full advantage of known information and avoids the approximation of some virtual control vectors. Moreover, a disturbance observer is presented to estimate unknown time-varying environmental disturbance. Furthermore, a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target. It enables each ASV to adjust its forces and moments according to the received information from its neighbors. The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.
, Available online  , doi: 10.1109/JAS.2022.105500
Abstract:
Estimating an accurate six-degree-of-freedom (6-DoF) pose from correspondences with outliers remains a critical issue to 3D rigid registration. Random sample consensus (RANSAC) and its variants are popular solutions to this problem. Although there have been a number of RANSAC-fashion estimators, two issues remain unsolved. First, it is unclear which estimator is more appropriate to a particular application. Second, the impacts of different sampling strategies, hypothesis generation methods, hypothesis evaluation metrics, and stop criteria on the overall estimators remain ambiguous. This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation. The objective is to thoroughly compare estimators in the RANSAC family, and evaluate the effects of each key stage on the eventual 6-DoF pose estimation performance. Experiments have been carried out on four standard datasets with different application scenarios, data modalities, and nuisances. They provide us with input correspondence sets with a variety of inlier ratios, spatial distributions, and scales. Based on the experimental results, we summarize remarkable outcomes and valuable findings, so as to give practical instructions to real-world applications, and highlight current bottlenecks and potential solutions in this research realm.
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, Available online
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Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities’ similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities’ similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.
, Available online  , doi: 10.1109/JAS.2022.105422
Abstract:
This paper addresses the problems of input-to-state stabilization and integral input-to-state stabilization for a class of nonlinear impulsive delayed systems subject to exogenous disturbances. Since the information of plant’s states, time delays, and exogenous disturbances is often hard to be obtained, the key design challenge, which we resolve, is the construction of a state observer-based controller. For this purpose, we firstly propose a corresponding observer which is independent of time delays and exogenous disturbances to reconstruct (or estimate) the plant’s states. And then based on the observations, we establish an observer-based control design for the plant to achieve the input-to-state stability (ISS) and integral-ISS (iISS) properties. With the help of the comparison principle and average impulse interval approach, some sufficient conditions are presented, and moreover, two different linear matrix inequalities (LMIs) based criteria are proposed to design the gain matrices. Finally, two numerical examples and their simulations are given to show the effectiveness of our theoretical results.
, Available online  , doi: 10.1109/JAS.2022.105428
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This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players’ objective functions. Through Lyapunov stability analysis, it is proven that the players’ actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.
, Available online
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Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multiinstantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.
, Available online  , doi: 10.1109/JAS.2022.105413
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The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability, the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information. This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.
, Available online  , doi: 10.1109/JAS.2020.1003408
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A classic kind of researches about the operational safety criterion for dynamic systems with barrier function can be roughly summarized as functional relationship, denoted by $\oplus$ , between the barrier function and its first derivative for time $t$ , where $\oplus$ can be “=”, “ $<$ ”, or “ $>$ ”, etc. This article draws on the form of the stable condition expression for finite time stability to formulate a novel kind of relaxed safety judgement criteria called exponential-alpha safety criteria. Moreover, we initially explore to use the control barrier function under exponential-alpha safety criteria to achieve the control for the dynamic system operational safety. In addition, derived from the actual process systems, we propose multi-hypersphere methods which are used to construct barrier functions and improved them for three types of special spatial relationships between the safe state set and the unsafe state set, where both of them can be spatially divided into multiple subsets. And the effectiveness of the proposed safety criteria are demonstrated by simulation examples.
, Available online
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In this work, a novel shape control approach of the probability density function (PDF) for nonlinear stochastic systems is presented. First, we provide the formula for the PDF shape controller without devising the control law of the controller. Then, based on the exact analytical solution of the FPK equation, the product function of the polynomial and the exponential polynomial is regarded as the stationary PDF of the state response. To validate the performance of the proposed control approach, we compared it with the exponential polynomial method and the multi-Gaussian closure method by implementing comparative simulation experiments. The results show that the novel PDF shape control approach is effective and feasible. Using an equal number of parameters, our method can achieve a similar or better control effect as the exponential polynomial method. By comparison with the multi-Gaussian closure method, our method has clear advantages in PDF shape control performance. For all cases, the integral of squared error and the errors of first four moments of our proposed method were very small, indicating superior performance and promising good overall control effects of our method. The approach presented in this study provides an alternative for PDF shape control in nonlinear stochastic systems.
, Available online  , doi: 10.1109/JAS.2022.105407
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Load frequency regulation is an essential auxiliary service used in dealing with the challenge of frequency stability in power systems that utilize an increasing proportion of wind power. We investigate a load frequency control method for multi-area interconnected power systems integrated with wind farms, aimed to eliminate the frequency deviation in each area and the tie-line power deviation between different areas. The method explores the derivative and integral terminal sliding mode control technology to solve the problem of load frequency regulation. Such technology employs the concept of relative degrees. However, the subsystems of wind-integrated interconnected power systems have different relative degrees, complicating the control design. This study develops the derivative and integral terminal sliding-mode-based controllers for these subsystems, realizing the load frequency regulation. Meanwhile, closed-loop stability is guaranteed with the theory of Lyapunov stability. Moreover, both a thermal power system and a wind power system are applied to provide frequency support in this study. Considering both constant and variable external disturbances, several numerical simulations were carried out in a two-area thermal power system with a wind farm. The results demonstrate the validity and feasibility of the developed method.
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, Available online  , doi: 110.1109/JAS.2021.1004386
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The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems (MASs) in the presence of faults. A dynamic event-triggered protocol (DETP) by adding an auxiliary variable is utilized to improve the utilization of communication resources. First, a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation (FC) is adopted to realize the demand of reliability and safety of addressed MASs. Subsequently, a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the $l_{2}$ - $l_{\infty}$ constraint by employing the variance analysis and the Lyapunov stability approaches. Furthermore, the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way, respectively. Finally, a simulation result is employed to verify the usefulness of the proposed design framework.
, Available online
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The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within laytime, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subsequent AGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.
, Available online  , doi: 10.1109/JAS.2021.1004249
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A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant delay, which can be potentially applied to vision-based control systems. The control scheme is composed of a state prediction and a discrete predictor-based controller. The state prediction is used to compensate for the effect of the sensor-to-controller delay, and the system can be stabilized by the discrete predictor-based controller. Moreover, it is shown that the control scheme is also robust with respect to slight message rejections. Finally, the main theoretical results are illustrated by simulation results and experimental results based on a networked visual servo inverted pendulum system.
, Available online  , doi: 10.1109/JAS.2021.1004144
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Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter (KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements. The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation. The proposed algorithm exhibits good robustness, adaptability, and value on applications.
, Available online  , doi: 10.1109/JAS.2021.1003955
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Generalized eigenvector plays an essential role in the signal processing field. In this paper, we present a novel neural network learning algorithm for estimating the generalized eigenvector of a Hermitian matrix pencil. Differently from some traditional algorithms, which need to select the proper values of learning rates before using, the proposed algorithm does not need a learning rate and is very suitable for real applications. Through analyzing all of the equilibrium points, it is proven that if and only if the weight vector of the neural network is equal to the generalized eigenvector corresponding to the largest generalized eigenvalue of a Hermitian matrix pencil, the proposed algorithm reaches to convergence status. By using the deterministic discrete-time (DDT) method, some convergence conditions, which can be satisfied with probability 1, are also obtained to guarantee its convergence. Simulation results show that the proposed algorithm has a fast convergence speed and good numerical stability. The real application demonstrates its effectiveness in tracking the optimal vector of beamforming.