Abstract: Determining how to structure vehicular network environments can be done in various ways. Here, we highlight vehicle networks’ evolution from vehicular ad-hoc networks (VANET) to the internet of vehicles (IoVs), listing their benefits and limitations. We also highlight the reasons in adopting wireless technologies, in particular, IEEE 802.11p and 5G vehicle-to-everything, as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments. We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’ requirements. The presentation of each paradigm is given from a historical and logical standpoint. In particular, vehicular fog computing improves on the deficiences of vehicular cloud computing, so both are not exclusive from the application point of view. We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks, showing that they complement each other and share problems and limitations. As these networks still have many opportunities to grow in both concept and application, we finally discuss concepts and technologies that we believe are beneficial. Throughout this work, we emphasize the crucial role of these concepts for the well-being of humanity.
Abstract: Electromyography (EMG) has already been broadly used in human-machine interaction (HMI) applications. Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution. Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. In this paper, we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI. An overview of typical network structures and processing schemes will be provided. Recent progress in typical tasks such as movement classification, joint angle prediction, and force/torque estimation will be introduced. New issues, including multimodal sensing, inter-subject/inter-session, and robustness toward disturbances will be discussed. We attempt to provide a comprehensive analysis of current research by discussing the advantages, challenges, and opportunities brought by deep learning. We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems. Furthermore, possible future directions will be presented to pave the way for future research.
Abstract: Without the geometry of light and logic of photon, observer-observability forms a paradox in modern science, truth-equilibrium finds no unification, and mind-light-matter unity is unreachable in spacetime. Subsequently, quantum mechanics has been shrouded with mysteries preventing itself from reaching definable causality for a general purpose analytical quantum computing paradigm. Ground-0 Axioms are introduced as an equilibrium-based, dynamic, bipolar set-theoretic unification of the first principles of science and the second law of thermodynamics. Related literatures are critically reviewed to justify the self-evident nature of Ground-0 Axioms. A historical misinterpretation by the founding fathers of quantum mechanics is identified and corrected. That disproves spacetime geometries (including but not limited to Euclidean and Hilbert spaces) as the geometries of light and truth-based logics (including but not limited to bra-ket quantum logic) as the logics of photon. Backed with logically definable causality and Dirac 3-polarizer experiment, bipolar quantum geometry (BQG) and bipolar dynamic logic (BDL) are identified as the geometry of light and the logic of photon, respectively, and wave-particle complementarity is shown less fundamental than bipolar complementarity. As a result, Ground-0 Axioms lead to a geometrical and logical illumination of the quantum and classical worlds as well as the physical and mental worlds. With logical resolutions to the EPR and Schrödinger’s cat paradoxes, an analytical quantum computing paradigm named quantum intelligence (QI) is introduced. It is shown that QI makes mind-light-matter unity and quantum-digital compatibility logically reachable for quantum-neuro-fuzzy AI-machinery with groundbreaking applications. It is contended that Ground-0 Axioms open a new era of science and philosophy—the era of mind-light-matter unity in which human-level white-box AI&QI is logically prompted to join Einstein’s grand unification to foster major scientific advances.
Abstract: This paper addresses the problem of distributed secondary control for islanded AC microgrids with external disturbances. By using a full-order sliding-mode (FOSM) approach, voltage regulation and frequency restoration are achieved in finite time. For voltage regulation, a distributed observer is proposed for each distributed generator (DG) to estimate a reference voltage level. Different from some conventional observers, the reference voltage level in this paper is accurately estimated under directed communication topologies. Based on the observer, a new nonlinear controller is designed in a backstepping manner such that an FOSM surface is reached in finite time. On the surface, the voltages of DGs are regulated to the reference level in finite time. For frequency restoration, a distributed controller is further proposed such that a constructed FOSM surface is reached in finite time, on which the frequencies of DGs are restored to a reference level in finite time under directed communication topologies. Finally, case studies on a modified IEEE 37-bus test system are conducted to demonstrate the effectiveness, the robustness against load changes, and the plug-and-play capability of the proposed controllers.
Abstract: Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift’s law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce and scattered experiment data are available for training which is often the case in practice. The theory-guided DNN could also be applicable to other sheet metal forming processes. It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.
Abstract: This paper investigates the problem of controlling half-vehicle semi-active suspension system involving a magnetorheological (MR) damper. This features a hysteretic behavior that is presently captured through the nonlinear Bouc-Wen model. The control objective is to regulate well the heave and the pitch motions of the chassis despite the road irregularities. The difficulty of the control problem lies in the nonlinearity of the system model, the uncertainty of some of its parameters, and the inaccessibility to measurements of the hysteresis internal state variables. Using Lyapunov control design tools, we design two observers to get online estimates of the hysteresis internal states and a stabilizing adaptive state-feedback regulator. The whole adaptive controller is formally shown to meet the desired control objectives. This theoretical result is confirmed by several simulations demonstrating the supremacy of the latter compared to the skyhook control and passive suspension.
Abstract: In this paper, a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism. First, a Petri net for the deadlock control of such systems is defined. Its admissible markings and first-met inadmissible markings (FIMs) are introduced. Next, place invariants are designed via an integer linear program (ILP) to survive all admissible markings and prohibit all FIMs, keeping the underlying system from reaching deadlocks, livelocks, bad markings, and the markings that may evolve into them by firing uncontrollable transitions. ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition. In addition, the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs. The condition under which the supervisor is maximally permissive in behavior is given. Finally, experimental results with the proposed method and existing ones are given to show its effectiveness.
Abstract: The control of battery energy storage systems (BESSs) plays an important role in the management of microgrids. In this paper, the problem of balancing the state-of-charge (SoC) of the networked battery units in a BESS while meeting the total charging/discharging power requirement is formulated and solved as a distributed control problem. Conditions on the communication topology among the battery units are established under which a control law is designed for each battery unit to solve the control problem based on distributed average reference power estimators and distributed average unit state estimators. Two types of estimators are proposed. One achieves asymptotic estimation and the other achieves finite time estimation. We show that, under the proposed control laws, SoC balancing of all battery units is achieved and the total charging/discharging power of the BESS tracks the desired power. A simulation example is shown to verify the theoretical results.
Abstract: A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints. The fuzzy logic system is used to design the approximator, which deals with uncertain and continuous functions in the process of backstepping design. The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint, but also mixes the states and errors to directly constrain the state, reducing the conservativeness of the constraint satisfaction condition. Considering that the states in most nonlinear systems are immeasurable, a fuzzy adaptive states observer is constructed to estimate the unknown states. Combined with adaptive backstepping technique, an adaptive fuzzy output feedback control method is proposed. The proposed control method ensures that all signals in the closed-loop system are bounded, and that the tracking error converges to a bounded tight set without violating the full state constraint. The simulation results prove the effectiveness of the proposed control scheme.
Abstract: In this paper, a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed. The methodological starting point relies on a smart use of predictive arguments with a twofold aim: 1) Promptly detect malicious agent behaviors affecting normal system operations; 2) Apply specific control actions, based on predictive ideas, for mitigating as much as possible undesirable domino effects resulting from adversary operations. Specifically, the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent. Finally, numerical simulations are carried out to show benefits and effectiveness of the proposed approach.
Abstract: This paper suggests a novel model-based nonlinear DC motor speed regulator without the use of a current sensor. The current dynamics, machine parameters and mismatched load variations are considered. The proposed controller is designed to include an active damping term that regulates the motor speed in accordance with the first-order low-pass filter dynamics through the pole-zero cancellation. Meanwhile, the angular acceleration and its reference are obtained from simple first-order estimators using only the speed information. The effectiveness is experimentally verified using hardware comprising the QUBE-Servo2, myRIO-1900, and LabVIEW.
Abstract: In the study of a visual projection field with swarm movements, an autonomous control strategy is presented in this paper for a swarm system under attack. To ensure a fast swarm dynamic response and stable spatial cohesion in a complex environment, a new hybrid swarm motion model is proposed by introducing global visual projection information to a traditional local interaction mechanism. In the face of attackers, individuals move towards the largest free space according to the projected view of the environment, rather than directly in the opposite direction of the attacker. Moreover, swarm individuals can certainly regroup without dispersion after the attacker leaves. On the other hand, the light transmittance of each individual is defined based on global visual projection information to represent its spatial freedom and relative position in the swarm. Then, an autonomous control strategy with adaptive parameters is proposed according to light transmittance to guide the movement of swarm individuals. The simulation results demonstrate in detail how individuals can avoid attackers safely and reconstruct ordered states of swarm motion.
Abstract: Wind energy can be considered a push-driver factor in the integration of renewable energy sources within the concept of smart grids. For its full deployment, it requires a modern telecommunication infrastructure for transmitting control signals around the distributed generation, in which, the wireless communication standards stand out for employing modern digital modulation and coding schemes for error correction, in order to guarantee the power plant operability. In some developing countries, such as Brazil, the high penetration of commercial mobile wireless standards GPRS and EGPRS (based on GSM technology) have captivated the interests of the energy sector, and they now seek to perform remote monitoring and control operations. In this context, this article presents a comparative performance analysis of a wireless control system for a wind SRG, when a GPRS or EGPRS data service is employed. The system performance is analyzed by co-simulations, including the wind generator dynamics and the wireless channel effects. The satisfactory results endorse the viability and robustness of the proposed system.
Abstract: This paper proposes a static-output-feedback based robust fuzzy wheelbase preview control algorithm for uncertain active suspensions with time delay and finite frequency constraint. Firstly, a Takagi-Sugeno (T-S) fuzzy augmented model is established to formulate the half-car active suspension system with consideration of time delay, sprung mass variation and wheelbase preview information. Secondly, in view of the resonation between human’s organs and vertical vibrations in the frequency range of 4–8 Hz, a finite frequency control criterion in terms of H∞ norm is developed to improve ride comfort. Meanwhile, other mechanical constraints are also considered and satisfied via generalized H2 norm. Thirdly, in order to maintain the feasibility of the controller despite of some state variables are not online-measured, a two stage approach is adopted to derive a static output feedback controller. Finally, numerical simulation results illustrate the excellent performance of the proposed controller.
Abstract: The traditional orthogonal multiple access (OMA) is unable to satisfy the needs of large number of smart devices. To increase the transmission rate in the limited spectrum resource, implementation of both non-orthogonal multiple access (NOMA) and successive interference cancelation (SIC) is essential. In this paper, an optimal resource allocation algorithm in NOMA is proposed to maximize the total system rate in a multi-sector multi-subcarrier relay-assisted communication network. Since the original problem is a non-convex problem with mixed integer programming which is non-deterministic polynomial-time (NP)-hard, a three-step solution is proposed to solve the primal problem. Firstly, we determine the optimal power allocation of the outer users by using the approach of monotonic discrimination, and then the optimal user pairing is determined. Secondly, the successive convex approximation (SCA) method is introduced to transform the non-convex problem involving central users into convex one, and the Lagrangian dual method is used to determine the optimal solution. Finally, the standard Hungarian algorithm is utilized to determine the optimal subcarrier matching. The simulation results show that resource allocation algorithm is able to meet the user performance requirements with NOMA, and the total system rate is improved compared to the existing algorithms.
Abstract: As wind energy is becoming one of the fastest-growing renewable energy resources, controlling large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. The main goal of the current work is to propose an intelligent control of the wind turbine system without the need for model identification. For this purpose, a novel model-independent nonsingular terminal sliding-mode control (MINTSMC) using the basic principles of the ultra-local model (ULM) and combined with the single input interval type-2 fuzzy logic control (SIT2-FLC) is developed for non-linear wind turbine pitch angle control. In the suggested control framework, the MINTSMC scheme is designed to regulate the wind turbine speed rotor, and a sliding-mode (SM) observer is adopted to estimate the unknown phenomena of the ULM. The auxiliary SIT2-FLC is added in the model-independent control structure to improve the rotor speed regulation and compensate for the SM observation estimation error. Extensive examinations and comparative analyses were made using a real-time software-in-the-loop (RT-SiL) based on the dSPACE 1202 board to appraise the efficiency and applicability of the suggested model-independent scheme in a real-time testbed.
IEEE/CAA Journal of Automatica Sinica
JCR Impact Factor 2020: 6.171 Rank：Top 11% (7/93), Category of Automation & Control Systems Quantile: The 1st (SCI Q1)
CiteScore 2020 : 11.2 Rank： Top 5% (Category of Computer Science: Information System) , Top 6% (Category of Control and Systems Engineering), Top 7% (Category of Artificial Intelligence)Quantile: The 1st (Q1)