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Front. Neurosci. | doi: 10.3389/fnins.2019.00390

Robust Adaptive Recurrent Cerebellar Model Neural Network for Nonlinear System Based on GPSO

 Jiansheng Guan1, 2*, Shao-jiang Hong1, Shao-bo Kang1, Yong Zeng2, yuan sun1 and Chih-Min Lin3
  • 1College of Electrical Engineering and Automation, Xiamen University of Technology, China
  • 2University Health Network (UHN), Canada
  • 3Yuan Ze University, Taiwan

A robust adaptive recurrent cerebellar model articulation controller (RARC) neural network for nonlinear systems using the genetic particle swarm optimization (GPSO) algorithm is presented in this study. The RARC is used as the principal tracking controller and the robust compensation controller is designed to recover the residual of the approximation error. In the RARC neural network, the steepest descent gradient method and the Lyapunov function are used for deriving the adaptive law parameter of the system. Besides, the learning rates play an important role in these adaptive laws and they have a great effect on the functions of control systems. In this paper, the combination of the genetic algorithm with the mutation particle swarm optimization algorithm is applied to seek for the optimal learning rates of the RARC adaptation laws. The numerical simulations about the inverted pendulum system as well as the robot manipulator system are given to confirm the effectiveness and practicability of the GPSO-RARC-based control system. Compared with other control schemes, the proposed control scheme is testified to be reliable and can obtain the optimal parameter about the learning rates and the minimum root mean square error for nonlinear systems.

Keywords: RARC neural network, GAPSO algorithm, Learning Rate, Robot Manipulator System, nonlinear system

Received: 02 Jan 2019; Accepted: 04 Apr 2019.

Edited by:

Hak Keung Lam, King's College London, United Kingdom

Reviewed by:

Bo Xiao, Imperial College London, United Kingdom
Aiwen Meng, Yanshan University, China  

Copyright: © 2019 Guan, Hong, Kang, Zeng, sun and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jiansheng Guan, College of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, China,