Adaptive tracking control for discrete-time switched nonlinear systems with dead-zone inputs

2018 ◽  
Vol 344 ◽  
pp. 51-69 ◽  
Author(s):  
Li Tang ◽  
Jun Zhao
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Tracking Control ◽  
Dead Zone ◽  
Switched Nonlinear Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

Adaptive tracking control for a class of uncertain switched nonlinear systems with time-delay

2016 ◽  
Vol 40 (4) ◽  
pp. 1102-1108 ◽  
Author(s):  
Hongmin Li ◽  
Xinyong Wang
Keyword(s):  
Time Delay ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Reference Signal ◽  
Switched Nonlinear Systems ◽  
Nonlinear Functions ◽  
Common Lyapunov Function ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Logic System

This paper focuses on the problem of adaptive tracking control for switched nonlinear systems with time-delay and unknown functions under arbitrary switchings. Based on the adaptive backstepping technique and common Lyapunov function an approach to a class of adaptive fuzzy controllers is designed. The fuzzy logic system is used to approximate the unknown nonlinear functions. The proposed controller guarantees that the output can converge to a small neighbourhood of the reference signal and ensure all of the signals are bounded. A numerical example is provided to demonstrate the effectiveness of the paper.

scholarly journals Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Jidong Wang ◽  
Lengxue Zhu ◽  
Xiaoping Si
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Discrete Time ◽  
Tracking Control ◽  
Closed Loop ◽  
Dead Zone ◽  
Switched Nonlinear Systems ◽  
Closed Loop System ◽  
Neural Controllers ◽  
Arbitrary Switching

In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

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