Adaptive backstepping fuzzy control for nonlinearly parameterized systems with periodic disturbances

Authors:
Weisheng Chen;Licheng Jiao;Ruihong Li;Jing Li
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and the .Department of Applied Mathematics, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Department of Applied Mathematics, Xidian University, Xi'an, China;Department of Applied Mathematics, Xidian University, Xi'an, China
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2010

Citing 25
Cited 7

Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
Stable Adaptive Neural Network Control

Stable Adaptive Neural Network Control
Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach

Information Sciences—Informatics and Computer Science: An International Journal
Brief paper: Asymptotic rejection of asymmetric periodic disturbances in output-feedback nonlinear systems

Automatica (Journal of IFAC)
Direct adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems

Fuzzy Sets and Systems
Adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear time-delay systems

Fuzzy Sets and Systems
Neural network approximation for periodically disturbed functions and applications to control design

Neurocomputing
Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Adaptive fuzzy output-feedback controller design for nonlinear time-delay systems with unknown control direction

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decentralized robust adaptive control for the multiagent system consensus problem using neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Globally stable adaptive backstepping fuzzy control for output-feedback systems with unknown high-frequency gain sign

Fuzzy Sets and Systems
Adaptive tracking for periodically time-varying and nonlinearly parameterized systems using multilayer neural networks

IEEE Transactions on Neural Networks
Adaptive fuzzy control for strict-feedback canonical nonlinear systems with H∞ tracking performance

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Repetitive learning control: a Lyapunov-based approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust backstepping control of nonlinear systems using neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Adaptive fuzzy H∞ stabilization for strict-feedback canonical nonlinear systems via backstepping and small-gain approach

IEEE Transactions on Fuzzy Systems
Fuzzy approximate disturbance decoupling of MIMO nonlinear systems by backstepping and application to chemical processes

IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

IEEE Transactions on Fuzzy Systems
Brief Paper: Design and performance analysis of a direct adaptive controller for nonlinear systems

Automatica (Journal of IFAC)
Brief Robust learning control for a class of nonlinear systems with periodic and aperiodic uncertainties

Automatica (Journal of IFAC)
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

Automatica (Journal of IFAC)
An ISS-modular approach for adaptive neural control of pure-feedback systems

Automatica (Journal of IFAC)
Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form

IEEE Transactions on Neural Networks

Adaptive iterative learning control for nonlinear time-delay systems with periodic disturbances using FSE-neural network

International Journal of Automation and Computing
Neural network-based adaptive tracking control for nonlinearly parameterized systems with unknown input nonlinearities

Neurocomputing
New results on adaptive neural control of a class of nonlinear systems with uncertain input delay

Neurocomputing
Robust adaptive decentralized fuzzy control for stochastic large-scale nonlinear systems with dynamical uncertainties

Neurocomputing
Robust adaptive fuzzy output feedback control for stochastic nonlinear systems with unknown control direction

Neurocomputing
Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay

Neurocomputing
Adaptive fuzzy backstepping output feedback control for a class of uncertain stochastic nonlinear system in pure-feedback form

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel-function approximator is constructed by combining a fuzzy-logic system with a Fourier series expansion in order to model unknown periodically disturbed system functions. Then, an adaptive backstepping tracking-control scheme is developed, where the dynamic-surface-control approach is used to solve the problem of "explosion of complexity" in the backstepping design procedure, and the time-varying parameter-dependent integral Lyapunov function is used to analyze the stability of the closed-loop system. The semiglobal uniform ultimate boundedness of all closed-loop signals is guaranteed, and the tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the effectiveness of the control scheme designed in this paper.