Intelligent Adaptive Cooperative Control for Complex Nonlinear Systems introduces new intelligent adaptive control strategies for solving problems that occur in external disturbances, faults, communication burden, input saturation, dead zone and unmeasured states. The book focuses on intelligent adaptive cooperative control for stochastic systems and semi-Markov jump systems and systematically introduces basic theory and methods. The valuable resource promotes the development of intelligent adaptive cooperative control for complex nonlinear systems, and is a useful resource for researchers, scholars, engineering experts and graduate students in the field of intelligent control, control and mathematics, industrial mathematics, mechatronics and engineering.
- Covers intelligent adaptive control in both stochastic systems and semi-markov jump systems, introducing the basic theory and method of intelligent adaptive control systematically
- Presents an analysis of multiagent systems under external disturbances, faults and communication burdens and develops disturbance compensation, fault-tolerant control and event-triggered control strategies
- Helps users design adaptive compensation control technologies for complex dynamical systems with state constraint, output constraint and error constraint
1. Introduction
2. Adaptive NN Finite-Time Control of Constrained Nonlinear Systems
3. Observer-Based Adaptive Fuzzy Control for State Constrained Nonlinear
Systems
4. Event-Triggered Adaptive Tracking Control for Non-affine Systems
5. Adaptive Control for multiagent Systems with Unknown Control Directions
6 Event-Triggered Adaptive Control for multiagent Systems with Disturbances
7 Adaptive Fuzzy Containment Control with Unknown Disturbances
8. Adaptive Control for Nonlinear Strict-Feedback Stochastic Systems
9. Cooperative Fault-Tolerant Control for Stochastic Nonlinear Systems
10. Dynamic Surface Control for Stochastic Nonlinear Systems
11. Fault Detection for Semi-Markov jump systems
12. Adaptive Fuzzy Bipartite Tracking Control for Stochastic Nonlinear
Systems
Hongjing Liang received his B.S. degree in mathematics from Bohai University, Jinzhou, and the M.S. degree in fundamental mathematics and Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China. He is currently an associate professor with Bohai University. His research interests include adaptive control, fuzzy control, multiagent systems, and their applications. Dr. Liang was recipient of the Best Paper Award in Theory from ICCSS 2017 and Outstanding Reviewer Award of CAA/Automatica Sinica 2019. He has been on the editorial board of Fluctuation and Noise Letters. He is a member of the Chinese Association of Automation. Liang Cao received the Ph.D. degree in control science and engineering from Guangdong University of Technology in 2019. His research interests include intelligent control and adaptive control for nonlinear systems. Hong Xue received her B.S. degree in mathematics from Bohai University, Jinzhou, China, where is she currently a lecturer. She obtained her M.S. degree in system theory from the Institute of Complexity Science, Qingdao University, Qingdao, China. Her current research interests include adaptive control as well as fuzzy control and their applications. Yingnan Pan received his B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Bohai University, Jinzhou, China, and Ph.D. degree in navigation guidance and control from Northeastern University, Shenyang, China. He is currently a lecturer at Bohai University. His research interests include fuzzy control, robust control, and event-triggered control and their applications.
