Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO

Abstract

A robust adaptive recurrent cerebellar model articulation controller (RARC) neural network for non-linear 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 non-linear systems.

Hybrid Neural Network Cerebellar Model Articulation Controller Design for Non-linear Dynamic Time-Varying Plants

“…It addresses the problems of fast-growing size and the learning difficulties that are inherent to current neural networks. Several studies showed that, for applications that require online learning, CMACs perform better than simple neural networks (Lin and Chen, 2009 ; Guan et al, 2019 ). Since CMACs have a non-fully connected perceptron-like associative-memory network with overlapping receptive fields, they have fast learning performance, and its computation is simple.…”