A Neural Network Approach for Tracking Control of Uncertain Switched Nonlinear Systems with Unknown Dead-Zone Input

2015 ◽  
Vol 34 (8) ◽  
pp. 2695-2710 ◽  
Author(s):  
Lei Yu ◽  
Shumin Fei ◽  
Gang Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Dead Zone ◽  
Switched Nonlinear Systems ◽  
Network Approach ◽  
Neural Network Approach

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

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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