Intelligent Adaptive Cooperative Control for Complex Nonlinear Systems
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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.
Key Features
- 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
Readership
Researchers, scholars, engineering experts, and graduate students in the field of intelligent control, control and mathematics, industrial mathematics, and engineering and information
Table of Contents
- Chapter 1. Introduction
1.1 Intelligent adaptive cooperative control
1.2 Fuzzy Control
1.3 Neural Networks
1.4 Graph Theory
1.4.1 Directed Graph
1.4.2 Undirected Graph
1.5 Publication Outline
Chapter 2. Adaptive NN Finite-Time Control of Constrained Nonlinear Systems
2.1 Introduction
2.2 Problem Formulation and Preliminaries
2.3 Controller Design-based Barrier Lyapunov Functions
2.4 Simulation Results
2.5 Conclusion
Chapter 3. Observer-Based Adaptive Fuzzy Control for State Constrained Nonlinear Systems
3.1 Introduction
3.2 Problem Formulation and Preliminaries
3.2.1 System Model
3.2.2 Fuzzy State Observer Design
3.3 Controller Design and Stability Analysis
3.3.1 Controller Design
3.3.2 Stability Analysis
3.4 Simulation Results
3.5 Conclusion
Chapter 4. Event-Triggered Adaptive Tracking Control for Non-affine Systems
4.1 Introduction
4.2 Problem Formulation and Preliminaries
4.2.1 Basic Graph Theory
4.2.2 Problem Formulation
4.2.3 Radial Basis Function Neural Networks
4.2.4 Error Transformation Based Design
4.3 Control Law Design And Stability Analysis
4.4 Simulation Results
4.5 Conclusion
Chapter 5. Adaptive Control for multiagent Systems with Unknown Control Directions
5.1 Introduction
5.2 Problem Formulation and Preliminaries
5.2.1 Algebraic Graph Theory
5.2.2 Problem Formulation
5.2.3 Radial Basis Function Neural Networks
5.3 Distributed Adaptive Controllers Design
5.4 Stability analysis
5.5 Simulation Results
5.6 Conclusions
Chapter 6 Event-Triggered Adaptive Control for multiagent Systems with Disturbances
6.1 Introduction
6.2 Problem Formulation and Preliminaries
6.2.1 Algebraic Graph Theory
6.2.2 RBF Neural Networks
6.2.3 System Model
6.3 Disturbance Observer Design
6.4 Distributed consensus controller design and Stability analysis
6.5 Simulation Results
6.6 Conclusion
Chapter 7 Adaptive Fuzzy Containment Control with Unknown Disturbances
7.1 Introduction
7.2 Problem Formulation and Preliminaries
7.2.1 Algebraic Graph Theory
7.2.2 System Model
7.2.3 Prescribed Performance
7.2.4 Fuzzy Logic Systems (FLSs)
7.2.5 Disturbance Observer Design
7.2.6 Nussbaum Function Properties
7.3 Distributed Adaptive Controllers Design
7.3.1 Adaptive Containment Control Scheme
7.4 Stability Analysis
7.5 Simulation Results
7.5.1 Numerical Example
7.5.2 Multiple Single-Link Robot Manipulator Systems (SLRMSs)
7.6 Conclusion
Chapter 8. Adaptive Control for Nonlinear Strict-Feedback Stochastic Systems
8.1 Introduction
8.2 Problem Formulation and Preliminaries
8.2.1 Stochastic Stability Analysis
8.2.2 System Model
8.2.3 Fuzzy Logic Systems (FLSs)
8.2.4 Barrier Lyapunov function
8.3 Control Design and Stability Analysis
8.3.1 Observer design
8.3.2 Stability analysis
8.4 Simulation Results
8.5 Conclusion
Chapter 9. Cooperative Fault-Tolerant Control for Stochastic Nonlinear Systems
9.1 Introduction
9.2 Problem Formulation and Preliminaries
9.2.1 Algebraic Graph Theory
9.2.2 System Model
9.2.3 Problem Statement
9.2.4 FNNs Approximation Property
9.3 Solution of Distributed Adaptive Controller Design Problem
9.4 Simulation Results
9.5 Conclusion
Chapter. 10 Dynamic Surface Control for Stochastic Nonlinear Systems
10.1 Introduction
10.2 Problem Formulation and Preliminaries
10.2.1 Algebraic Graph Theory
10.2.2 System Model
10.2.3 Problem Statement
10.3 Distributed Adaptive Controller Design
10.4 Stability Analysis of Stochastic Nonlinear Systems
10.5 Simulation Results
10.6 Conclusion
Chapter 11. Fault Detection for Semi-Markov jump systems
11.1 Introduction
11.2 Problem Formulation and Preliminaries
11.2.1 System Model
11.2.2 Adaptive Event-Triggered Scheme
11.2.3 Output Quantization
11.2.4 Fault Detection Filter Design and Fault Detection System Model
11.3 Solution of Fault Detection Problem
11.4 Simulation Results
11.5 Conclusion
Chapter 12. Adaptive Fuzzy Bipartite Tracking Control for Stochastic Nonlinear Systems
12.1 Introduction
12.2 Problem Formulation and Preliminaries
12.2.1 Algebraic Graph Theory
12.2.2 System Model
12.2.3 Problem Statement
12.3 Solution of Adaptive Fuzzy Bipartite Tracking Control Problem
12.4 Stability Analysis for Stochastic Nonlinear Systems
12.5 Simulation Results
12.6 Conclusion
References
Product details
- Language: English
- Copyright: © Academic Press 2023
- Published: August 1, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780443161629
About the Authors
Hongjing Liang
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.
Affiliations and Expertise
Associate Professor, College of Control Science and Engineering, Bohai University, Jinzhou, China
Liang Cao
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.
Affiliations and Expertise
College of Mathematical Science, Bohai University, Jinzhou, China
Hong Xue
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.
Affiliations and Expertise
College of Mathematical Science, Bohai University, Jinzhou, China
Yingnan Pan
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.
Affiliations and Expertise
College of Control Science and Engineering, Bohai University, Jinzhou, China
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