(8) Nonlinear Systems and Applications: Hongyi Li, Guangdong University of Technology, China (Chair)

8.1. Speaker: Prof. Qinglai Wei, The State Key Laboratory of Management and Control for Complex System, China

Talk Title: Discrete-Time Zero-Sum Games for Nonlinear Systems via Adaptive Dynamic Programming

Abstract: In this talk, the principle of ADP is discussed. A novel iterative zero-sum ADP algorithm is introduced for solving infinite horizon discrete-time two-player zero-sum games of nonlinear systems. The present iterative zero-sum ADP algorithm permits arbitrary positive semi-definite functions to initialize the upper and lower iterations. When the saddle-point equilibrium exists, both the upper and lower iterative value functions are proven to converge to the optimal solution of the zero-sum game, where the existence criteria of the saddle-point equilibrium are not required. If the saddle-point equilibrium does not exist, the upper and lower optimal performance index functions are obtained, respectively, where the upper and lower performance index functions are proven to be not equivalent. Finally, simulation results are shown to illustrate the performance of the present method.

Biography：Prof. Qinglai Wei, received the B.S. degree in Automation, and the Ph.D. degree in control theory and control engineering, from the Northeastern University, Shenyang, China, in 2002 and 2009, respectively. From 2009-2011, he was a postdoctoral fellow with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He is currently a Professor of the institute and the associate director of The State Key Laboratory of Management and Control for Complex Systems. He has authored three books, and published over 70 international journal papers. His research interests include adaptive dynamic programming, neural-networks-based control, optimal control, nonlinear systems and their industrial applications.

Dr. Wei is an Associate Editor of IEEE Transaction on Systems Man, and Cybernetics: Systems since 2016, Information Sciences since 2016, Neurocomputing since 2016, Optimal Control Applications and Methods since 2016, Acta Automatica Sinica since 2015, and has been holding the same position for IEEE Transactions on Neural Networks and Learning Systems during 2014-2015. He is the Secretary of IEEE Computational Intelligence Society (CIS) Beijing Chapter since 2015. He was Registration Chair of the 12th World Congress on Intelligent Control and Automation (WCICA2016). He was the Publication Chair of 5th International Conference on Information Science and Technology (ICIST 2015). He was the Finance Chair of the 4th International Conference on Intelligent Control and Information Processing (ICICIP 2013) and the Publicity Chair of the 2012 International Conference on Brain Inspired Cognitive Systems (BICS 2012). He was guest editors for several international journals. He was a recipient of Shuang-Chuang Talents in Jiangsu Province, China, in 2014. He was a recipient of the Outstanding Paper Award of Acta Automatica Sinica in 2011 and Zhang Siying Outstanding Paper Award of Chinese Control and Decision Conference (CCDC) in 2015. He was a recipient of Young Researcher Award of Asia Pacific Neural Network Society (APNNS) in 2016.

8.2. Speaker: Prof. Qinmin Yang, Zhejiang University, China

Talk Title: Adaptive transient performance enhancement control and its applications in microgrid

Abstract: A microgrid is a small power system consisting of distributed generation units, energy storage units, and loads, and has the characteristics include low inertia and large uncertainties. The traditional microgrid control strategies mainly consider steady-state performance, whereas transient performance is not quantitatively analyzed. Learning-based nonlinear control methods are also criticized by their uncertain performance during transient stage. To ensure the stable and efficiently operation of micro-grids, for example ship power systems (SPS), adaptive transient performance enhancement control is introduced in this talk. Various applications are also demonstrated.

Biography：Prof. Qinmin Yang received the Bachelor's degree in Electrical Engineering from Civil Aviation University of China, Tianjin, China in 2001, the Master of Science Degree in Control Science and Engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China in 2004, and the Ph.D. degree in Electrical Engineering from the University of Missouri-Rolla, MO USA, in 2007.

From 2007 to 2008, he was a Post-doctoral Research Associate at University of Missouri-Rolla. From 2008 to 2009, he was a system engineer with Caterpillar Inc. From 2009 to 2010, he was a Post-doctoral Research Associate at University of Connecticut. Since 2010, he has been with the State Key Laboratory of Industrial Control Technology, the College of Control Science and Engineering, Zhejiang University, China, where he is currently a professor. He has also held visiting positions in University of Toronto and Lehigh University. He has been serving as an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics: Systems, Transactions of the Institute of Measurement and Control, and Automatica Sinica. His research interests include intelligent control, renewable energy systems, smart grid, and industrial big data.

8.3. Speaker:  Prof. Yongming Li, Liaoning University of Technology, China

Talk Title: Fuzzy Adaptive Control for A Class of Nonlinear Interconnected Large-Scale Systems

Abstract: Practical control systems, such as aerospace systems, robot manipulators and chemistry reaction process, possess highly nonlinear, large-scale, uncertain, multivariable and strong coupling characteristics, making it extremely challenging to model accurately. As a result, the control theory and method based on precise mathematical model cannot solve the control problem for this kind of complex nonlinear system. Thanks to the fuzzy control approach that has been successfully used to handle uncertain nonlinear systems, which provides a possibility to deal with above-mentioned challenge. We propose an fuzzy adaptive control strategy for a class of interconnected nonlinear large scale systems. Specifically, the nonlinear systems considered consist of unmodeled uncertainties, unmeasured states, unknown interconnected terms, unknown control directions, and actuator faults. The developed control scheme guarantees that all closed loop signals are bounded and outputs of each subsystem converge to the neighborhood of origin whose size can be made as small as desired by appropriately selecting design parameters.

Biography：Prof. Yongming Li is a professor in the college of science, Liaoning University of Technology. He received the B.S. and theM.S. degrees in applied mathematics from LiaoningUniversity of Technology, Jinzhou, China, in 2004and 2007, respectively. He received the Ph.D. degreein transportation information engineering and controlfrom Dalian Maritime University, Dalian, China,in 2014. Hisresearch interests include adaptive control, fuzzycontrol, and neural networks control for nonlinearsystems. He has published over 70 papers, and his recent awards include the Outstanding Paper Award of IEEE Transactions on Fuzzy Systems, the Best Paper Award of IEEE Transactions on Systems, Man and Cybernetics: Systems, a Second Prize of National Natural Science Award of Chinese Ministry of Education in 2015, and a Youth Science and Technology Award of Liaoning province in 2017.

8.4. Speaker: Prof. Zhuo Wang, Beihang University, China

Talk Title: A Data-driven State Observation Method for Atomic Spin-exchange Relaxation-free Comagnetometer

Abstract:  With the development of quantum mechanics, modern optics and atomic manipulation technique, the traditional electromechanical and optical sensing has gradually developed to quantum sensing based on intrinsic properties of atoms. Atomic spin-exchange relaxation-free (SERF) comagnetometer, which can sense inertial rotation using atomic spins, is one kind of quantum sensors with ultra-sensitivity.This talk is about the atomic dual-axis spin-exchange relaxation-free (SERF) comagnetometers. In our work, we first establish a state-space model of the atomic SERF comagnetometer system according to its linearized Bloch equations, by selecting the transverse polarizations of electron spin and nuclear spin as the state variables. However, the transverse nuclear spin polarizations cannot be directly measured, which means some of the system states cannot be directly observed. To solve this problem, a data-driven state observation (DDSO) method is developed to estimate the nuclear spin polarizations in real time. Simulation results based on practical system parameters illustrate the feasibility of the DDSO method. In the end, some comments are also given on the meaning and value of this DDSO method.

Biography：Prof. Zhuo Wang received the Ph.D. degree in electrical and computer engineering from University of Illinois at Chicago, Chicago, Illinois, USA, in 2013. He was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Alberta, from 2013 to 2014. He worked as a Research Assistant Professor with the Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, from 2014 to 2015. He was selected for the ``12th Recruitment Program for Young Professionals'' by the Organization Department of the CPC Central Committee, and the ``100 Talents Program'' by Beihang University, in 2015. He is currently a Professor and a Ph.D. Instructor with the Research Institute of Frontier Science, Beihang University, Beijing, China. Prof. Wang is currently a Vice Director of the 9th Chinese Association of Automation (CAA) Youth Work Committee; a Member of the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society; a Member of the Data Driven Control, Learning & Optimization Professional Committee of CAA; and is also a Member of the Fault Diagnosis & Safety for Technical Processes Professional Committee of CAA. He is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems; an Associate Editor of Control Theory & Applications; and is also an Associate Editor of Pattern Recognition and Artificial Intelligence.

8.5. Speaker:  Prof. Tengfei Liu, Northeastern University, China

Talk Title:  Robust Event-Triggered Control of Nonlinear Systems: Three Examples

Abstract:  This talk discusses the design of event triggered control schemes for nonlinear systems subject to both external disturbances and dynamic uncertainties. To avoid Zeno behavior, this paper proposes an event-triggering mechanism that uses not only the measured system state but also an estimation of the influence of the external disturbances and dynamic uncertainty. It is shown that the proposed event-triggering mechanism guarantees that the inter-sampling intervals are bounded blow by a positive constant, leading to the absence of Zeno phenomenon. Moreover, the closed-loop event-triggered system is input-to-state stable with the external disturbance as the input. Three examples are employed to show the basic idea.

Biography：Prof. Tengfei Liu received the B.E. degree in Automation and the M.E. degree in Control Theory and Control Engineering from South China University of Technology, in 2005 and 2007, respectively. He received the Ph.D. degree in Engineering from the Australian National University in 2011. Tengfei Liu is a visiting assistant professor at Polytechnic Institute of New York University. His research interests include stability theory, robust nonlinear control, quantized control, distributed control and their applications in mechanical systems, power systems and transportation systems. Dr. Liu, with Z. Jiang and D. J. Hill, received the Guan Zhao-Zhi Best Paper Award at the 2011 Chinese Control Conference.