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Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models
Yang Li, Xiao Wang, Zhifan He, Ze Wang, Ke Cheng, Sanchuan Ding, Yijing Fan, Xiaotao Li, Yawen Niu, Shanpeng Xiao, Zhenqi Hao, Bin Gao, Huaqiang Wu
, Available online  , doi: 10.1109/JAS.2024.124422
[Abstract](2) [FullText HTML] (0) [PDF 10770KB](2)
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Automated optical inspection (AOI) is a significant process in printed circuit board assembly (PCBA) production lines which aims to detect tiny defects in PCBAs. Existing AOI equipment has several deficiencies including low throughput, large computation cost, high latency, and poor flexibility, which limits the efficiency of online PCBA inspection. In this paper, a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed. In this method, the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection framework. To improve the performance of the model, extensive real PCBA images are collected from production lines as datasets. Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices. Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods. Our method can be integrated into a lightweight inference system and promote the flexibility of AOI. The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.
Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems
Shengao Lu, Tong Wu, Lixian Zhang, Jianan Yang, Ye Liang
, Available online  
[Abstract](2) [FullText HTML] (0) [PDF 1879KB](0)
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This paper revisits the problem of bumpless transfer control (BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control (IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent mode-dependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains (with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and $ H_{\infty} $ performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.
Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications
Rong Zhao, Jun-e Feng, Dawei Zhang
, Available online  
[Abstract](3) [FullText HTML] (0) [PDF 1547KB](1)
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Set stabilization is one of the essential problems in engineering systems, and self-triggered control (STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed, respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.
Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems
Zongyu Zuo, Jingchuan Tang, Ruiqi Ke, Qing-Long Han
, Available online  , doi: 10.1109/JAS.2024.124485
[Abstract](3) [FullText HTML] (0) [PDF 2896KB](3)
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This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent systems. New hyperbolic tangent function-based protocols are proposed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed communication topologies. These new protocols not only provide an explicit upper-bound estimate for the settling time, but also have a user-prescribed bounded control level. In addition, compared to some existing results based on the saturation function, the proposed approach considerably simplifies the protocol design and the stability analysis. Illustrative examples and an application demonstrate the effectiveness of the proposed protocols.
Attention Markets of Blockchain-Based Decentralized Autonomous Organizations
Juanjuan Li, Rui Qin, Sangtian Guan, Wenwen Ding, Fei Lin, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2024.124491
[Abstract](2) [FullText HTML] (0) [PDF 1008KB](1)
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The attention is a scarce resource in decentralized autonomous organizations (DAOs), as their self-governance relies heavily on the attention-intensive decision-making process of “proposal and voting”. To prevent the negative effects of proposers’ attention-capturing strategies that contribute to the “tragedy of the commons” and ensure an efficient distribution of attention among multiple proposals, it is necessary to establish a market-driven allocation scheme for DAOs’ attention. First, the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading, where the individualized Harberger tax rate (HTR) determined by the proposers’ reputation is adopted. Then, the Stackelberg game model is formulated in these markets, casting attention to owners in the role of leaders and other competitive proposers as followers. Its equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention pricing. Moreover, utilizing the single-round Stackelberg game as an illustrative example, the existence of Nash equilibrium trading strategies is demonstrated. Finally, the impact of individualized HTR on trading strategies is investigated, and results suggest that it has a negative correlation with leaders’ self-accessed prices and ownership duration, but its effect on their revenues varies under different conditions. This study is expected to provide valuable insights into leveraging attention resources to improve DAOs’ governance and decision-making process.
A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning
Yi Liu, Xiang Wu, Yuming Bo, Jiacun Wang, Lifeng Ma
, Available online  , doi: 10.1109/JAS.2023.124173
[Abstract](26) [FullText HTML] (0) [PDF 556KB](3)
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Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction
Zhiming Zhang, Shangce Gao, MengChu Zhou, Mengtao Yan, Shuyang Cao
, Available online  
[Abstract](23) [FullText HTML] (0) [PDF 7016KB](11)
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Accurately predicting fluid forces acting on the surface of a structure is crucial in engineering design. However, this task becomes particularly challenging in turbulent flow, due to the complex and irregular changes in the flow field. In this study, we propose a novel deep learning method, named mapping network-coordinated stacked gated recurrent units (MSU), for predicting pressure on a circular cylinder from velocity data. Specifically, our coordinated learning strategy is designed to extract the most critical velocity point for prediction, a process that has not been explored before. In our experiments, MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder. This method significantly reduces the workload of data measurement in practical engineering applications. Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects. Furthermore, the comparison results show that MSU predicts more precise results, even outperforming models that use all velocity field points. Compared with state-of-the-art methods, MSU has an average improvement of more than 45% in various indicators such as root mean square error (RMSE). Through comprehensive and authoritative physical verification, we established that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields. This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios. The code is available at https://github.com/zhangzm0128/MSU.
Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems
You Zhao, Xing He, Mingliang Zhou, Tingwen Huang
, Available online  
[Abstract](15) [FullText HTML] (0) [PDF 1814KB](6)
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The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates. Most previous studies in this field have primarily concentrated on unconstrained smooth convex optimization problems. In this paper, on the basis of primal-dual dynamical approach, Nesterov accelerated dynamical approach, projection operator and directional gradient, we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints, which consist of a second-order ODE (ordinary differential equation) or differential conclusion system for the primal variables and a first-order ODE for the dual variables. By satisfying specific conditions for time scaling, we demonstrate that the proposed approaches have a faster convergence rate. This only requires assuming convexity of the objective function. We validate the effectiveness of our proposed two accelerated primal-dual projection neurodynamic approaches through numerical experiments.
Adaptive Space Expansion for Fast Motion Planning
Shenglei Shi, Jiankui Chen
, Available online  , doi: 10.1109/JAS.2023.123765
[Abstract](67) [FullText HTML] (0) [PDF 2747KB](19)
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The sampling process is very inefficient for sampling-based motion planning algorithms that excess random samples are generated in the planning space. In this paper, we propose an adaptive space expansion (ASE) approach which belongs to the informed sampling category to improve the sampling efficiency for quickly finding a feasible path. The ASE method enlarges the search space gradually and restrains the sampling process in a sequence of small hyper-ellipsoid ring subsets to avoid exploring the unnecessary space. Specifically, for a constructed small hyper-ellipsoid ring subset, if the algorithm cannot find a feasible path in it, then the subset is expanded. Thus, the ASE method successively does space exploring and space expansion until the final path has been found. Besides, we present a particular construction method of the hyper-ellipsoid ring that uniform random samples can be directly generated in it. At last, we present a feasible motion planner BiASE and an asymptotically optimal motion planner BiASE* using the bidirectional exploring method and the ASE strategy. Simulations demonstrate that the computation speed is much faster than that of the state-of-the-art algorithms. The source codes are available at https://github.com/shshlei/ompl.
Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy
Yuhan Zhang, Zidong Wang, Lei Zou, Yun Chen, Guoping Lu
, Available online  , doi: 10.1109/JAS.2024.124314
[Abstract](21) [FullText HTML] (0) [PDF 1287KB](7)
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This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints. These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics. Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observer-based controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.
Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach
Zheng Wu, Yiyun Zhao, Fanbiao Li, Tao Yang, Yang Shi, Weihua Gui
, Available online  , doi: 10.1109/JAS.2024.124416
[Abstract](40) [FullText HTML] (0) [PDF 1853KB](12)
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This paper presents an asynchronous output-feedback control strategy of semi-Markovian systems via sliding mode-based learning technique. Compared with most literature results that require exact prior knowledge of system state and mode information, an asynchronous output-feedback sliding surface is adopted in the case of incompletely available state and non-synchronization phenomenon. The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem. Based upon the co-occurrence of two subsystems, the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions. Furthermore, a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion. Finally, the proposed theoretical method is substantiated through two numerical simulations with the practical continuous stirred tank reactor and F-404 aircraft engine model, respectively.
Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems
Lihong Feng, Bonan Huang, Jiayue Sun, Qiuye Sun, Xiangpeng Xie
, Available online  
[Abstract](48) [FullText HTML] (0) [PDF 2616KB](23)
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In this paper, a class of time-varying output group formation containment control problem of general linear heterogeneous multiagent systems (MASs) is investigated under directed topology. The MAS is composed of a number of tracking leaders, formation leaders and followers, where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations, respectively. Firstly, compensators are designed whose states are estimations of tracking leaders, based on which, a controller is developed for each formation leader to accomplish the expected formation. Secondly, two event-triggered compensators are proposed for each follower to evaluate the state and formation information of the formation leaders in the same group, respectively. Subsequently, a control protocol is designed for each follower, utilizing the output information, to guide the output towards the convex hull generated by the formation leaders within the group. Next, the triggering sequence in this paper is decomposed into two sequences, and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior. Finally, a numerical simulation is introduced to confirm the validity of the proposed results.
A Novel Prescribed-Performance Path-Following Problem for Non-Holonomic Vehicles
Zirui Chen, Jingchuan Tang, Zongyu Zuo
, Available online  
[Abstract](33) [FullText HTML] (0) [PDF 1790KB](13)
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The issue of achieving prescribed-performance path following in robotics is addressed in this paper, where the aim is to ensure that a desired path within a specified region is accurately converged to by the controlled vehicle. In this context, a novel form of the prescribed performance guiding vector field is introduced, accompanied by a prescribed-time sliding mode control approach. Furthermore, the interdependence among the prescribed parameters is discussed. To validate the effectiveness of the proposed method, numerical simulations are presented to demonstrate the efficacy of the approach.
Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints
Qian Ma, Peng Jin, Frank L. Lewis
, Available online  
[Abstract](31) [FullText HTML] (0) [PDF 2805KB](15)
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In this paper, guaranteed cost attitude tracking control for uncertain quadrotor unmanned aerial vehicle (QUAV) under safety constraints is studied. First, an augmented system is constructed by the tracking error system and reference system. This transformation aims to convert the tracking control problem into a stabilization control problem. Then, control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances, and they are incorporated into the reward function as penalty items. Based on the modified reward function, the problem is simplified as the optimal regulation problem of the nominal augmented system, and a new Hamilton-Jacobi-Bellman equation is developed. Finally, critic-only reinforcement learning algorithm with a concurrent learning technique is employed to solve the Hamilton-Jacobi-Bellman equation and obtain the optimal controller. The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances, but also enforce safety constraints. The performance of the algorithm is evaluated by the numerical simulation.
A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching
Quanbo Ge, Yang Cheng, Hong Li, Ziyi Ye, Yi Zhu, Gang Yao
, Available online  
[Abstract](16) [FullText HTML] (0) [PDF 1922KB](4)
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For accurately identifying the distribution characteristic of Gaussian-like noises in unmanned aerial vehicle (UAV) state estimation, this paper proposes a non-parametric scheme based on curve similarity matching. In the framework of the proposed scheme, a Parzen window (kernel density estimation, KDE) method on sliding window technology is applied for roughly estimating the sample probability density, a precise data probability density function (PDF) model is constructed with the least square method on K-fold cross validation, and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape, abruptness and symmetry. Some comparison simulations with classical methods and UAV flight experiment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data, which provides better reference for the design of Kalman filter (KF) in complex water environment.
Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game
Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch
, Available online  
[Abstract](11) [FullText HTML] (0) [PDF 881KB](2)
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Partially-Observed Maximum Principle for Backward Stochastic Differential Delay Equations
Shuang Wu
, Available online  
[Abstract](12) [FullText HTML] (0) [PDF 287KB](2)
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New Second-Level-Discrete Zeroing Neural Network for Solving Dynamic Linear System
Min Yang
, Available online  
[Abstract](11) [FullText HTML] (0) [PDF 488KB](1)
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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization
Jing Liang, Hongyu Lin, Caitong Yue, Ponnuthurai Nagaratnam Suganthan, Yaonan Wang
, Available online  , doi: 10.1109/JAS.2024.124377
[Abstract](33) [FullText HTML] (0) [PDF 2013KB](12)
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In multimodal multiobjective optimization problems (MMOPs), there are several Pareto optimal solutions corresponding to the identical objective vector. This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables. Due to the increase in the dimensions of decision variables in real-world MMOPs, it is difficult for current multimodal multiobjective optimization evolutionary algorithms (MMOEAs) to find multiple Pareto optimal solutions. The proposed algorithm adopts a dual-population framework and an improved environmental selection method. It utilizes a convergence archive to help the first population improve the quality of solutions. The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population. The combination of these two strategies helps to effectively balance and enhance convergence and diversity performance. In addition, to study the performance of the proposed algorithm, a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed. The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.
Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends
Weihao Song, Zidong Wang, Zhongkui Li, Jianan Wang, Qing-Long Han
, Available online  , doi: 10.1109/JAS.2023.123588
[Abstract](207) [FullText HTML] (0) [PDF 1014KB](58)
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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance. The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation, cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter, and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security. Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization
Sheng Qi, Rui Wang, Tao Zhang, Xu Yang, Ruiqing Sun, Ling Wang
, Available online  , doi: 10.1109/JAS.2024.124341
[Abstract](13) [FullText HTML] (0) [PDF 2548KB](6)
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Traditional large-scale multi-objective optimization algorithms (LSMOEAs) encounter difficulties when dealing with sparse large-scale multi-objective optimization problems (SLMOPs) where most decision variables are zero. As a result, many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately. Nevertheless, existing optimizers often focus on locating non-zero variable positions to optimize the binary variables Mask. However, approximating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized. In data mining, it is common to mine frequent itemsets appearing together in a dataset to reveal the correlation between data. Inspired by this, we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets (TELSO) to address these SLMOPs. TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence. Experimental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms (SLMOEAs) in terms of performance and convergence speed.
Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis
Jiaxin Ren, Jingcheng Wen, Zhibin Zhao, Ruqiang Yan, Xuefeng Chen, Asoke K. Nandi
, Available online  , doi: 10.1109/JAS.2024.124290
[Abstract](23) [FullText HTML] (0) [PDF 11570KB](4)
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Recently, intelligent fault diagnosis based on deep learning has been extensively investigated, exhibiting state-of-the-art performance. However, the deep learning model is often not truly trusted by users due to the lack of interpretability of “black box”, which limits its deployment in safety-critical applications. A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases, and the human in the decision-making loop can be found to deal with the abnormal situation when the models fail. In this paper, we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks, called SAEU. In SAEU, Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks. Based on the SAEU, we propose a unified uncertainty-aware deep learning framework (UU-DLF) to realize the grand vision of trustworthy fault diagnosis. Moreover, our UU-DLF effectively embodies the idea of “humans in the loop”, which not only allows for manual intervention in abnormal situations of diagnostic models, but also makes corresponding improvements on existing models based on traceability analysis. Finally, two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.
Set-Valued State Estimation of Nonlinear Discrete-Time Systems and Its Application to Attack Detection
Hao Liu, Qing-Long Han, Yuzhe Li
, Available online  
[Abstract](27) [FullText HTML] (0) [PDF 1560KB](10)
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This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded (UBB) noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets. First, properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity. Then, the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets. Based on generalized intersection, the utilization of set-based estimation for attack detection is analyzed. Finally, an example is given to show the efficiency of our results.
A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry
Xuerui Zhang, Zhongyang Han, Jun Zhao
, Available online  , doi: 10.1109/JAS.2023.124116
[Abstract](56) [FullText HTML] (0) [PDF 9864KB](24)
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Ingredient optimization plays a pivotal role in the copper industry, for which it is closely related to the concentrate utilization rate, stability of furnace conditions, and the quality of copper production. To acquire a practical ingredient plan, which should exhibit long duration time with sufficient utilization and feeding stability for real applications, an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions. To address the complex challenges posed by this integer programming model, including multiple coupling feeding stages, intricate constraints, and significant non-linearity, a multi-stage differential-multifactorial evolution algorithm is developed. In the proposed algorithm, a differential evolutionary algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model. First, unlike traditional time-consuming serial approaches, the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm (DE) caused by the feeding stability in a parallel manner. Second, a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner. In addition, a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm. Finally, the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted, which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches. It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.
Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach
Tao Chen, Ruiyuan Zhao, Jian Chen, Zichao Zhang
, Available online  , doi: 10.1109/JAS.2023.124158
[Abstract](94) [FullText HTML] (0) [PDF 753KB](38)
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A Novel Vibration-Based Self-Adapting Method to Acquire Real-Time Following Distance for Virtually Coupled Trains
Qinglai Zhang, Jianmin Gao, Qing Wu, Qinglie He, Libin Tie, Wanming Zhai, Shengyang Zhu
, Available online  , doi: 10.1109/JAS.2024.124326
[Abstract](19) [FullText HTML] (0) [PDF 3816KB](7)
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Virtual coupling (VC) is an emerging technology for addressing the shortage of rail transportation capacity. As a crucial enabling technology, the VC-specific acquisition of train information, especially train following distance (TFD), is underdeveloped. In this paper, a novel method is proposed to acquire real-time TFD by analyzing the vibration response of the front and following trains, during which only onboard accelerometers and speedometers are required. In contrast to the traditional arts of train positioning, this method targets a relative position between two adjacent trains in VC operation, rather than the global positions of the trains. For this purpose, an adaptive system containing three strategies is designed to cope with possible adverse factors in train operation. A vehicle dynamics simulation of a heavy-haul railway is implemented for the evaluation of feasibility and performance. Furthermore, a validation is conducted using a set of data measured from in-service Chinese high-speed trains. The results indicate the method achieves satisfactory estimation accuracy using both simulated and actual data. It has favorable adaptability to various uncertainties possibly encountered in train operation. Additionally, the method is preliminarily proven to adapt to different locomotive types and even different rail transportation modes. In general, such a method with good performance, low-cost, and easy implementation is promising to apply.
Event-Based Networked Predictive Control of Cyber-Physical Systems With Delays and DoS Attacks
Wencheng Luo, Pingli Lu, Changkun Du, Haikuo Liu
, Available online  
[Abstract](19) [FullText HTML] (0) [PDF 457KB](9)
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A Multi-Constrained Matrix Factorization Approach for Community Detection Relying on Alternating-Direction-Method of Multipliers
Ying Shi, Zhigang Liu
, Available online  
[Abstract](12) [FullText HTML] (0) [PDF 826KB](3)
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Privacy-Preserving Average Consensus Algorithm under Round-Robin Scheduling Protocol
Yingjiang Guo, Wenying Xu, Haodong Wang, Jianquan Lu, Shengli Du
, Available online  , doi: 10.1109/JAS.2023.123921
[Abstract](26) [FullText HTML] (0) [PDF 389KB](12)
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A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs
Yalin Zhang, Zhongxin Liu, Zengqiang Chen
, Available online  , doi: 10.1109/JAS.2024.124236
[Abstract](41) [FullText HTML] (0) [PDF 2430KB](7)
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Battery energy storage systems (BESSs) are widely used in smart grids. However, power consumed by inner impedances and the capacity degradation of each battery unit become particularly severe, which has resulted in an increase in operating costs. The general economic dispatch (ED) algorithm based on marginal cost (MC) consensus is usually a proportional (P) controller, which encounters the defects of slow convergence speed and low control accuracy. In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance, we attempt to design a proportional integral (PI) controller with a reset mechanism (PI+R) to asymptotically promote MC consensus and total power mismatch towards 0 in this paper. To be frank, the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing, which accelerates convergence, improves control accuracy, and avoids overshoot. The eigenvalues of the system under a PI+R controller is well analyzed, ensuring the regularity of the system and enabling the reset mechanism. To ensure supply and demand balance within the isolated BESSs, a centralized reset mechanism is introduced, so that the controller is distributed in a flow set and centralized in a jump set. To cope with Zeno behavior and input delay, a dwell time that the system resides in a flow set is given. Based on this, the system with input delays can be reduced to a time-delay free system. Considering the capacity limitation of the battery, a modified MC scheme with PI+R controller is designed. The correctness of the designed scheme is verified through relevant simulations.
Input-to-State Stability of Impulsive Switched Systems Involving Uncertain Impulse-switching Moments
Chang Liu, Wenlu Liu, Tengda Wei, Xiaodi Li
, Available online  
[Abstract](25) [FullText HTML] (0) [PDF 474KB](5)
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A Local-Global Attention Fusion Framework With Tensor Decomposition for Medical Diagnosis
Peishu Wu, Han Li, Liwei Hu, Jirong Ge, Nianyin Zeng
, Available online  
[Abstract](26) [FullText HTML] (0) [PDF 576KB](10)
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Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells
Meng Zhou, Zihao Wang, Jing Wang, Zhengcai Cao
, Available online  , doi: 10.1109/JAS.2023.124041
[Abstract](132) [FullText HTML] (0) [PDF 2229KB](35)
Abstract:
This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine (SVM) based on the definition of buffered Voronoi cells (BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm. Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.
Approximately Bi-Similar Symbolic Model for Discrete-time Interconnected Switched System
Yang Song, Yongzhuang Liu, Wanqing Zhao
, Available online  , doi: 10.1109/JAS.2023.123927
[Abstract](61) [FullText HTML] (0) [PDF 933KB](9)
Abstract:
Distributed Finite-Time Event-Triggered Formation Control Based on a Unified Framework of Affine Image
Yan-Jun Lin, Yun-Shi Yang, Li Chai, Zhi-Yun Lin
, Available online  , doi: 10.1109/JAS.2023.123885
[Abstract](64) [FullText HTML] (0) [PDF 644KB](28)
Abstract:
Event-Triggered Fault Detection — An Integrated Design Approach Directly Toward Fault Diagnosis Performance
Aibing Qiu, Yu Hu, Jingsong Wu
, Available online  , doi: 10.1109/JAS.2023.124074
[Abstract](40) [FullText HTML] (0) [PDF 457KB](14)
Abstract:
Global Stabilization Via Adaptive Event-Triggered Output Feedback for Nonlinear Systems With Unknown Measurement Sensitivity
Yupin Wang, Hui Li
, Available online  , doi: 10.1109/JAS.2023.123984
[Abstract](67) [FullText HTML] (0) [PDF 389KB](24)
Abstract:
Synchronous Membership Function Dependent Event-Triggered H∞ Control of T-S Fuzzy Systems Under Network Communications
Bo-Lin Xu, Chen Peng, Wen-Bo Xie
, Available online  , doi: 10.1109/JAS.2023.123729
[Abstract](49) [FullText HTML] (0) [PDF 414KB](15)
Abstract:
A Novel Scalable Fault-Tolerant Control Design for DC Microgrids WIth Nonuniform Faults
Aimin Wang, Minrui Fei, Dajun Du, Yang Song
, Available online  , doi: 10.1109/JAS.2023.123918
[Abstract](99) [FullText HTML] (0) [PDF 525KB](24)
Abstract:
Intra-independent Distributed Resource Allocation Game
Jialing Zhou, Guanghui Wen, Yuezu Lv, Tao Yang, Guanrong Chen
, Available online  , doi: 10.1109/JAS.2023.123906
[Abstract](88) [FullText HTML] (0) [PDF 552KB](25)
Abstract:
A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network
Yishuai Lin, Gang Hu, Liang Wang, Qingshan Li, Jiawei Zhu
, Available online  , doi: 10.1109/JAS.2023.123300
[Abstract](226) [FullText HTML] (0) [PDF 1544KB](40)
Abstract:
Integrating Inventory Monitoring and Capacity Changes in Dynamic Supply Chains with Bi-Directional Cascading Propagation Effects
En-Zhi Cao, Chen Peng, Qing-Kui Li
, Available online  , doi: 10.1109/JAS.2023.123309
[Abstract](71) [FullText HTML] (0) [PDF 1122KB](16)
Abstract:
Recurrent Neural Network Inspired Finite-Time Control Design
Jianan Liu, Shihua Li, Rongjie Liu
, Available online  , doi: 10.1109/JAS.2023.123297
[Abstract](145) [FullText HTML] (0) [PDF 1090KB](32)
Abstract:
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
[Abstract](143) [FullText HTML] (0) [PDF 703KB](20)
Abstract:
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Minfeng Qi, Ziyuan Wang, Qing-Long Han, Jun Zhang, Shiping Chen, Yang Xiang
, Available online  , doi: 10.1109/JAS.2022.106058
[Abstract](624) [FullText HTML] (0) [PDF 1296KB](105)
Abstract:
To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
Distributed Minimum-Energy Containment Control of Continuous-Time Multi-Agent Systems by Inverse Optimal Control
Fei Yan, Xiangbiao Liu, Tao Feng
, Available online  , doi: 10.1109/JAS.2022.106067
[Abstract](196) [FullText HTML] (0) [PDF 521KB](27)
Abstract:
Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers
Meiling Xie, Derui Ding, Xiaohua Ge, Qing-Long Han, Hongli Dong, Yan Song
, Available online  , doi: 10.1109/JAS.2022.105941
[Abstract](230) [FullText HTML] (0) [PDF 1736KB](77)
Abstract:
Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”
Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang
, Available online  
[Abstract](211) [FullText HTML] (0) [PDF 131KB](1)
Abstract:
Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
, Available online  
[Abstract](225) [FullText HTML] (0) [PDF 8129KB](6)
Abstract: