Publications

In this paper, we consider the distributed optimization control problem for second-order uncertain nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. To solve the problem, a hierarchical control method consisting of designing a time-varying weight regulator in the signal processing layer, distributed resilient coordinators in the network communication layer, and decentralized resilient optimal controllers in the local control layer is proposed. Specifically, a time-varying weight regulator is first designed by using the polynomial interpolation method to ensure that the signal used from neighbors is continuously differentiable. Based on this weight regulator, distributed resilient coordinators with up to second-order derivatives are designed to generate a local reference signal for each agent to follow. Then, decentralized resilient optimal controllers are proposed by using the backstepping method. It is shown that the practical distributed optimization objective can be achieved by means of the developed method. Finally, a simulation example is provided to show the effectiveness of the proposed distributed resilient optimization method.

Cyber-physical systems (CPS) are engineered systems with built-in seamless integration of physical and cyber components. Fundamental developments in sensing, communication, control, and computing technologies endow CPS with flexibility, adaptability, scalability, and robustness. The availability and size of input-output data generated along with the control of CPS bring a unique opportunity for machine learning techniques to advance the theory of dynamical control systems, by learning control rules directly from data. Integration of input-output data into adaptive, robust, predictive, and distributed control policies holds the key to exploiting the potential of learning and optimisation in the CPS designs. There are several challenges related to sampling, transmission, synchronization, as well as associated cyber security when merging contemporary data-based and traditional model-based control techniques for CPS. The overarching goal of this special issue is to bring together innovative developments on the interface between learning, control, and optimisation targeting cyber-physical opportunities emerging from power, transportation, and manufacturing systems. Through a rigorous peer review process, three articles have been accepted, which are summarised below. In the study, "Learning-based distributed adaptive control of heterogeneous multi-agent systems with unknown leader dynamics", the authors develop a distributed adaptive tracking control method for heterogeneous multi-agent systems with unknown leader dynamics in a directed graph. In contrast to the reported leader-following consensus studies, the prior knowledge of the leader is supposed to be cognised by some or all of the followers, the situation that the leader's dynamics are totally unrecognised but can be learned for each individual follower is considered. A data-driven learning algorithm using the system's data is developed to reconstruct the unknown systems matrix. Then, an adaptive distributed dynamic compensator is exploited to provide the leader's state estimation in a directed graph. Afterwards, a dynamic output feedback control law for each agent is projected. Theoretical analysis shows that the proposed algorithms not only ensure that all followers can identify the unknown system matrix but also guarantee that the distributed output leader-following consensus control with heterogeneous dynamics is achieved without any global information. In the study, "Sampled-data synchronisation of singular Markovian jump system (SMJS): application to a DC motor model", the authors consider the sampled-data synchronisation problem for SMJSs subject to aperiodic sampled-data control. Firstly, by constructing mode-dependent one-sided loop-based Lyapunov functional (LBLF) and two-sided LBLF, two different stochastically admissible conditions are suggested for error SMJSs with aperiodic sampled-data. It is guaranteed that the slave system is stochastically synchronised to the master system on the basis of the proposed stochastically admissible conditions. Secondly, two corresponding mode-dependent aperiodic sampled-data controller design approaches are provided for error SMJSs based on two different conditions, respectively. Finally, the validity of these approaches is demonstrated by a DC motor model. It also demonstrated that the two-sided LBLF method possesses a larger upper bound of the sampling period than the one-sided LBLF method. In the study, "Predefined-time distributed event-triggered algorithms for resource allocation", the authors propose a predefined-time distributed algorithm and analyse its convergence by using the Lyapunov stability theory in which the local constraint is ensured by a differential projection operator. Thus, a predefined time is obtained using a time-varying time-based generator. In addition, to reduce the communication consumption between agents, the authors develop a static as well as a dynamic-based event-triggered control scheme, where the information broadcast only occurs at some discrete time instants. Moreover, the three proposed algorithms converge precisely to the global optimal solution. Besides, the Zeno behaviour is excluded in the above static and dynamic event-triggered mechanisms. The guest editors would like to thank the Editor-in-Chief of the IET Cyber-Physical Systems: Theory & Applications, Prof. Shiyan Hu, and the Editorial Office for their great support of our special issue. In addition, we thank all the authors who submitted their quality papers to this special issue, and special thanks go to all anonymous reviewers for their great efforts and time to accomplish their review tasks. Data sharing not applicable. Jian Sun is currently a professor at Beijing Institute of Technology, China. He received the bachelor's degree from the Department of Automation and Electric Engineering, Jilin Institute of Technology, Changchun, China, in 2001, the master's degree from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CAS), Changchun, in 2004, and the Ph.D. degree from the Institute of Automation, CAS, Beijing, China, in 2007. He was a Research Fellow with the Faculty of Advanced Technology, University of Glamorgan, Pontypridd, U.K., from 2008 to 2009. He was a Post-Doctoral Research Fellow with the Beijing Institute of Technology, Beijing, from 2007 to 2010. In 2010, he joined the School of Automation, Beijing Institute of Technology, where he has been a Professor since 2013. His current research interests include networked control systems, time-delay systems, and security of cyber-physical systems. He is also an Editorial Board Member of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, the Journal of Systems Science and Complexity, and Acta Automatica Sinica. Qing-Long Han is Pro Vice-Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology, Melbourne, Australia. He held various academic and management positions at Griffith University and Central Queensland University, Australia. He received the Ph.D. degree in Control Engineering from East China University of Science and Technology in 1997. He has been conducting research in the areas of networked control systems, cyber physical systems, time-delay systems, multi-agent systems, smart grids, offshore structure, unmanned surface vehicles, cyber security, and neural networks. Since 2001, as of 12 August 2021, he has authored or co-authored three hundred and thirty-nine (339) fully-refereed high quality journal articles including one hundred and seventy-six (176) articles in the most prestigious IEEE Transactions, and thirty-seven (37) articles in Automatica. He has also authored or co-authored one hundred and eighty-four (184) international leading conference papers, five (5) monographs, one (1) research-based book chapter, and edited four (4) conference proceedings and ten (10) special issues. His research work has been cited 32,886 times with an h-index of 102, an i10-index of 296 according to Google Scholar. He is a Highly Cited Researcher in the Essential Science Indicator (ESI) field of Engineering (2014–2017), in the ESI Cross-Field (2018) and in both the ESI fields of Engineering and Computer Science (2019–2020) by Clarivate Analytics (Thomson Reuters). He is one of Australia's Top 5 Lifetime Achievers (Research Superstars) in the discipline area of Engineering and Computer Science by The Australian's Research Magazine (2019-2020). He is one of Australia's Top 5 Researchers in Computer Science and Electronics by Guide2Research. He was the recipient of The 2021 M. A. Sargent Medal (the Highest Award of the Electrical College Board of Engineers Australia), The 2020 IEEE Systems, Man, and Cybernetics (SMC) Society Andrew P. Sage Best Transactions Paper Award, The 2020 IEEE Transactions on Industrial Informatics Outstanding Paper Award, and The 2019 IEEE SMC Society Andrew P. Sage Best Transactions Paper Award. He was the recipient of The 2020 IEEE SMC Society Best Associate Editor Award and The 2020 IEEE/CAA Journal of Automatica Sinica Outstanding Associate Editor Award. Professor Han is a Member of the Academia Europaea (The Academy of Europe). He is a Fellow of The Institute of Electrical and Electronic Engineers (IEEE) and a Fellow of The Institution of Engineers Australia. He has served as an AdCom Member of IEEE Industrial Electronics Society (IES), a Member of IEEE IES Fellows Committee, and Chair of IEEE IES Technical Committee on Network-based Control Systems. He has served as Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics (2022–2024), Co-Editor of Australian Journal of Electrical & Electronics Engineering, an Associate Editor for 12 international journals including IEEE Transactions on Cybernetics, IEEE Industrial Electronics Magazine, IEEE/CAA Journal of Automatica Sinica, Control Engineering Practice, Information Sciences, and a Guest Editor for 13 Special Issues. Guo-Ping Liu is currently a chair professor with the Southern University of Science and Technology, China. He received the BEng and MEng degrees from Central South University of Technology (now Central South University, China) in 1982 and 1985, respectively, and the PhD degree from University of Manchester in the UK in 1992. He did the postdoctoral research in the University of York in 1992–1993. He worked as a research fellow with the University of Sheffield in 1994-1996. During 1996–2000, he was a senior engineer with GEC-Alsthom and ALSTOM, and then a principal engineer and a project leader with ABB ALSTOM Power. He was a senior lecturer with the University of Nottingham in 2000–2003, a visiting professor with the Chinese Academy of Sciences in 2001-2005 and with the Harbin Institute of Technology in 2008–2013, and a professor with the University of South Wales in 2004–2019 and with Wuhan University in 2019–2021. Prof Liu's research interests include networked control systems, multi-objective optimal control, nonlinear identification and control, and industrial advanced control applications. He has completed more than 70 research projects as a principal investigator. He is an author of 10 books, 2 edited books, over 330 journal papers and 320 conference papers, and has 14 patents, 34 software copyrights and 2 software products. He was awarded the Alexander von Humboldt Research Fellowship in 1992. He was named a Highly Cited Researcher by Thomson Reuters in 2014 and 2015, by Clarivate Analytics in 2016–2018, and by Elsevier in 2014–2020. He received the second prize of Chinese National Science and Technology Awards in 2008 and 2015. Prof. Liu was the general chair of the 2007 IEEE International Conference on Networking, Sensing and Control, the 2011 International Conference on Intelligent Control and Information Processing, and the 2012 UKACC International Conference on Control. He has served as an Editor-in-Chief of the International Journal of Automation and Computing in 2004–2021 and an Honorary Editor-in-Chief of the journal "Advances in Internet of Things" since 2011. He is a Fellow of IEEE, a Fellow of IET and a member of the Academy of Europe. Yajun Pan is currently a Professor in the Department of Mechanical Engineering at Dalhousie University, Canada. She received the B.E. degree in Mechanical Engineering from Yanshan University, P.R. China, in 1996, the M.E. degree in Mechanical Engineering from Zhejiang University, P.R. China, in 1999 and the Ph.D degree in Electrical and Computer Engineering from the National University of Singapore, in 2003. After receiving the Ph.D. degree, she was a post-doctoral fellow of CNRS in the Laboratoire d'Automatique de Grenoble, France from 2003 to 2004. In 2004, she held post-doctoral position in the department of Electrical and Computer Engineering at the University of Alberta, Canada. She is currently an Associate Editor of the IEEE Transactions on Industrial Electronics (2019 Present), IEEE Transactions on Cybernetics (2016-Present), and IEEE/ASME Transactions on Mechatronics (2015–2020). She has been the Associate Editor for the Journal of Franklin Institute (2007–2013), International Journal of Advanced Robotics Systems (2014–2018), and the International Journal of Information and Systems Sciences (2009–2017). She is the Editor of the special issue on Advances in Methods for Control over Networks for the Journal of Control Science and Engineering; and the guest editor for the special issue in Advanced Modelling and Control of Complex Mechatronic Systems with Nonlinearity and Uncertainty for IEEE Access (2017–2018). She is a Fellow of Engineering Institute of Canada (FEIC - 2021), a Fellow of the American Society of Mechanical Engineers (ASME - 2017), a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), a member of Canadian Society of Mechanical Engineering (CSME) and a Registered Professional Engineer in the Province of Nova Scotia, Canada. Dr. Pan was awarded the Research Excellence Award (Junior Category, Academic Year of 2008–2009) in the Faculty of Engineering, Dalhousie University. She has served as the evaluation group member for NSERC Discovery Grant (2017–2020) and also the selection committee member for NSERC RTI grant in 2016–2017. She has served as the Vice-President, Atlantic Region, for Canadian Society of Mechanical Engineering (CSME) from 2018–2020. Tao Yang is a Professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He was an Assistant Professor at the Department of Electrical Engineering, University of North Texas, Denton, USA, from 2016–2019. He received the Ph.D. degree in electrical engineering from Washington State University in 2012. Between August 2012 and August 2014, he was an ACCESS Post-Doctoral Researcher with the ACCESS Linnaeus Centre, Royal Institute of Technology, Sweden. He then joined the Pacific Northwest National Laboratory as a postdoc, and was promoted to Scientist/Engineer II in 2015. His research interests include industrial artificial intelligence, integrated optimization and control, distributed control and optimization with applications to process industries, cyber physical systems, and networked control systems. He is an Associate Editor for IEEE Transactions on Control Systems Technology and IEEE/CAA Journal of Automatica Sinica. He currently is a member of the Technical Committee on Nonlinear Systems and Control, the Technical Committee on Networks and Communication Systems, and the Technical Committee on Smart Grids of the IEEE Control Systems Society, a member of the IEEE Control Systems Society Conference Editorial Board, and a member of the IFAC Technical Committee on Large Scale Complex Systems. He received Ralph E. Powe Junior Faculty Enhancement Award and Best Student Paper award (as an advisor) of the 14th IEEE International Conference on Control & Automation in 2018. Jiahu Qin is currently a Professor with the Department of Automation, University of Science and Technology of China, Hefei, China. He received the first Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2012, and the second Ph.D. degree in systems and control from the Australian National University, Canberra, ACT, Australia, in 2014. His current research interests include multi-agent systems, cyber-physical systems, and complex dynamical networks. Dr. Qin serves as the Deputy Director of Youth Committee of Chinese Association of Automation. He is currently an Associate Editor of IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics, Journal of The Franklin Institute, Neurocomputing, Acta Automatica Sinica, IET Cyber-Physical Systems: Theory & Applications, etc. He is a recipient of the 2017 IEEE IES (Industrial Electronics Society) Best Conference Paper Award.

SUMMARY This paper is concerned with network‐based H ∞ stabilization for stochastic systems, where network‐induced delays, packet dropouts, and packet disorders are taken into account simultaneously. The packet disorders arising from both the sampler‐to‐controller channel and the controller‐to‐actuator channel are considered by introducing a logic controller and a logic zero‐order hold. The network‐induced delays and packet dropouts are modeled as a constant delay plus a non‐differentiable time‐varying delay in the input. By employing Lyapunov–Krasovskii functional approach, we establish results that parallel well‐known bounded real Lemmas. More specifically, these results provide conditions to bound the H ∞ level of the system, which means the worst case energy of the output of the system when subjected to a unitary norm deterministic disturbance signal. On the basis of these results, suitable network‐based H ∞ controllers are designed by using cone complementary linearization method. An air vehicle system is finally taken as an example to show the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

This paper investigates the networked active vibration control for a multi-degree-of-freedom structural system subject to modeling uncertainties, stochastic sensor faults, and earthquake excitations. By adopting a positive velocity feedback controller and introducing a communication network that produces proper network-induced delays and packet dropouts in the feedback control loop, the closed-loop system is first modeled by an uncertain stochastic system with a nonsmall interval time-varying input delay. Then a delay-dependent criterion is derived by employing a complete Lyapunov–Krasovskii functional such that the stochastic system with the proper nonzero input delay is stochastically stable with L 2 -gain performance, but unstable without delay. An iterative algorithm is presented to design the networked controller. A three-storey shear-beam building model subject to the El Centro 1940 earthquake is given to validate the proposed approach.

This paper addresses the secure and safe distributed cooperative control problem of multiple platoons of automated vehicles under unknown data falsification attacks on driving commands. First, a general multi-platoon control framework is developed, which accommodates longitudinal and lateral vehicle dynamics, inter- and intra-platoon information exchanges, falsified driving commands, and unknown external disturbances. To deal with the unknown falsified driving commands on the platoon performance, a neural-network-based adaptive control strategy is developed to compensate their adverse effects. In order to avert both longitudinal and lateral collisions under various maneuvering scenarios, a built-in avoidance mechanism is then designed for each platoon vehicle. Furthermore, a secure and anti-collision multi-platoon control design approach is proposed to ensure the desired inter- and intra-platoon tracking performance with a collision-free guarantee. It is formally proved that the inter- and intra-platoon tracking errors converge to small neighborhood around zero. Finally, several comparative simulation cases are presented to verify the effectiveness and merits of the proposed multi-platoon control approach.

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.

In this paper, a new unidirectional coupling for global synchronization of two Hindmarsh-Rose neurons is designed. Compared with existing results, external control for global synchronization is no longer required. A synchronization criterion of two Hindmarsh-Rose neurons with rigorous mathematical proof is derived. An example is given to illustrate the effectiveness of the obtained result.

International Press of Boston - publishers of scholarly mathematical and scientific journals and books

This paper is concerned with pure delayed non-fragile control for an offshore steel jacket platform subject to non-linear self-excited wave force. By purpo

This article is concerned with the influence maximization (IM) problem under a network with probabilistically unstable links (PULs) via graph embedding for multiagent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the IM problem under the network with PULs. Second, the MAS model for the IM problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the IM problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the developed algorithms and 2) illustrate the optimal solution for IM under different scenarios with PULs.

This paper is concerned with the stability problem for a class of uncertain linear discrete-time systems with time-varying delay. The delay is of an interval-like type, which means that both lower and upper bounds for the time-varying delay are available. The uncertainty under consideration is norm-bounded uncertainty. Based on Lyapunov-Krasovskii functional approach, delay-dependent stability criteria are obtained using a sum inequality which is first introduced and plays an important role in deriving stability conditions. The criteria are formulated in the form of linear matrix inequalities (LMIs). A numerical example is given to show the effectiveness of the proposed criteria.

No abstract is provided for this article.

This article deals with the problem of leader-following consensus for multiple wheeled mobile robots. Under a directed graph, a distributed observer is proposed for each follower to estimate the leader state in a fixed time. Based on the observer and a constructed nonlinear manifold, a novel protocol is designed such that the estimated leader state is tracked in a fixed time. Moreover, a switching protocol together with a linear manifold is proposed to ensure that fixed-time leader-following consensus is realized for any initial conditions without causing singularity issues. In contrast to alternative fixed-time consensus protocols in some existing results, the protocol proposed in this article is designed by constructing the nonlinear or linear manifold, which builds a new framework for fixed-time leader-following consensus. Furthermore, the obtained upper bound of settling time is explicitly linked with a single parameter in the protocol, which facilitates the adjustment of the bound under different performance requirements. Finally, the proposed protocol is applied to formation control of wheeled mobile robots.