Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input

Authors:
Beibei Ren;Shuzhi Sam Ge;Chun-Yi Su;Tong Heng Lee
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore;Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, Canada;Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2009

Citing 17
Cited 0

A universal adaptive stabilizer for a class of nonlinear systems

Systems & Control Letters
A robust adaptive nonlinear control design

Automatica (Journal of IFAC)
Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form

Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Nonlinear and Adaptive Control Design

Nonlinear and Adaptive Control Design
Neural Network Control of Robot Manipulators and Nonlinear Systems

Neural Network Control of Robot Manipulators and Nonlinear Systems
Stable Adaptive Neural Network Control

Stable Adaptive Neural Network Control
Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Adaptive position/force control of robot manipulators without velocity measurements: theory and experimentation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive NN control of uncertain nonlinear pure-feedback systems

Automatica (Journal of IFAC)
Brief Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form

Automatica (Journal of IFAC)
Modeling and control of hysteresis in magnetostrictive actuators

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

Automatica (Journal of IFAC)
Robust adaptive control of a class of nonlinear systems including actuator hysteresis with Prandtl-Ishlinskii presentations

Automatica (Journal of IFAC)
Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback

IEEE Transactions on Neural Networks
Adaptive neural network control for a class of low-triangular-structured nonlinear systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.