scholarly journals Neural Network-Based Adaptive Tracking Control for a Class of Nonlinear Singularly Perturbed Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43205-43213
Author(s):  
Hao Wang ◽  
Chunyu Yang ◽  
Song Zhu
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Singularly Perturbed ◽  
Singularly Perturbed Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Perturbed Systems

Neural-network-based adaptive tracking control for Markovian jump nonlinear systems with unmodeled dynamics

2016 ◽  
Vol 179 ◽  
pp. 44-53 ◽  
Author(s):  
Ru Chang ◽  
Yiming Fang ◽  
Jianxiong Li ◽  
Le Liu
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Unmodeled Dynamics ◽  
Markovian Jump ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

scholarly journals Direct Neural Network Adaptive Tracking Control for Uncertain Non-Strict Feedback Systems With Nonsymmetric Dead-Zone

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 220795-220806
Author(s):  
Rui Wang ◽  
Liyun Zhao ◽  
Fusheng Yu
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Dead Zone ◽  
Feedback Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Strict Feedback

Neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input saturation

2019 ◽  
Vol 6 (1) ◽  
pp. 278-290 ◽  
Author(s):  
Nassira Zerari ◽  
Mohamed Chemachema ◽  
Najib Essounbouli
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Input Saturation ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

Neural network-based adaptive tracking control of mobile robots in the presence of modelling error and disturbances

2015 ◽  
Author(s):  
Tongjia Zheng ◽  
Anle Yang ◽  
Min Wang ◽  
Meichuan Huang ◽  
Baozhong Rao
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Tracking Control ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Modelling Error

RBF Neural Network Based Adaptive Tracking Control for a Class of Nonlinear Plant Using Stochastic U-Model

2011 ◽  
Vol 474-476 ◽  
pp. 1209-1214 ◽  
Author(s):  
Bin Wang ◽  
Cai Liu ◽  
Xue Li Wu ◽  
Lei Liu
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Control Algorithm ◽  
Design Procedure ◽  
Least Mean Square ◽  
Unknown Parameters ◽  
Model Framework ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Wide Range

In this paper an adaptive tracking control algorithm and its step by step implementation procedure are developed for a class of nonlinear plants within a U-model framework with unknown parameters. With the author’s previous justification, not only the control oriented model represents a wide range of smooth (polynomial) nonlinear dynamic plants (without using linearisation approximation at all), but also make almost all linear control system design techniques directly applicable (with a root solver bridging the linear design and calculation of controller output). A new technique is proposed to design an online control algorithm using the Radial Basis Functions Neural Network (RBFNN). The plant parameters are estimated online and are used to update the weights of the RBFNN. The weights update equations are derived based on the well known LMS (least mean square). A number of simulated case studies are conducted to illustrate the efficiency of the claimed insight and design procedure.

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