Neural Network Direct Adaptive Control Strategy for a Class of Switched Nonlinear Systems

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Lei Yu ◽  
Xiefu Jiang ◽  
Shumin Fei ◽  
Jun Huang ◽  
Gang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Method ◽  
Average Dwell Time ◽  
Switching Control ◽  
Network Control ◽  
Switched Nonlinear Systems ◽  
Output Tracking ◽  
Direct Adaptive Control ◽  
Adaptive Control Strategy

This paper deals with the adaptive neural network (NN) switching control problem for a class of switched nonlinear systems. Radial basis function (RBF) NNs are utilized to approximate the unknown switching control law term which includes a neural network control term, a supervisory control term, and a compensation control term. Also, based on the average dwell-time, a direct adaptive neural switching controller is designed to heighten the robustness of switching system. We can prove to ensure stability of the resulting closed-loop system such that the output tracking performance can be well obtained and all the signals are kept bounded. Simulation results validate the tracking control performance and investigate the effectiveness of the proposed switching control method.

Robust Output Tracking Control for a Class of Uncertain Switched Nonlinear Systems

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Xiao X. Dong ◽  
Jun Zhao
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Control Method ◽  
Design Method ◽  
Average Dwell Time ◽  
Variable Structure ◽  
Linear Matrix ◽  
Switched Nonlinear Systems ◽  
Output Tracking ◽  
Output Tracking Control

This paper is devoted to the problem of robust output tracking control for uncertain cascade switched nonlinear systems with external disturbances. A sufficient condition for the output tracking problem of switched systems to be solvable is given in terms of the average dwell-time scheme and linear matrix inequalities where no solvability of the output tracking control problem for all subsystems is assumed. The controllers are designed based on a variable structure control method in order to conquer the uncertainties. Simulations illustrate the effectiveness of the proposed robust tracking design method.

scholarly journals Neural Network-Based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

2021 ◽  
Author(s):  
Baomin Li ◽  
Jianwei Xia ◽  
Wei Sun ◽  
Hao Shen ◽  
Huasheng Zhang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
Network Control ◽  
Switched Nonlinear Systems ◽  
Unknown Control Directions ◽  
Asymptotic Tracking ◽  
Unknown Control ◽  
Event Triggered

Abstract This paper addresses the event-triggered based adaptive asymptotic tracking control problem for switched nonlinear systems with unknown control directions based on neural network technique. A novel asymptotic tracking controller, in which Nussbaum functions are introduced to address the issue of unknown control directions, is designed by combining neural network control technology and event-triggered strategy. Different from the existing tracking control schemes, the proposed controller in this paper can guarantee that the tracking error ς 1 asymptotically converges to the origin and reduce the communication burden from the controller to the actuator. Finally, the effectiveness of the presented control design is proved by numerical examples.

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