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

Tien-Loc Le,Chih-Min Lin,Sung-Kyung Hong,Tuan-Tu Huynh

doi:10.3389/fnins.2020.00695

Abstract

This study proposes a hybrid method to control dynamic time-varying plants that comprises a neural network controller and a cerebellar model articulation controller (CMAC). The neural-network controller reduces the range and quantity of the input. The cerebellar-model articulation controller is the main controller and is used to compute the final control output. The parameters for the structure of the proposed network are adjusted using adaptive laws, which are derived using the steepest-descent gradient approach and back-propagation algorithm. The Lyapunov stability theory is applied to guarantee system convergence. By using the proposed combination architecture, the designed CMAC structure is reduced, and it makes it easy to design the network size and the initial membership functions. Finally, numerical-simulation results demonstrate the effectiveness of the proposed method.

Highlights

Nowadays, the control of non-linear systems is a topic that continues to attract many researchers because of its widespread applications
This study proposes a new method with a structure that includes a neural network connected in series with a cerebellar model articulation controller (CMAC)
The main contributions of this study are: (1) the successful design of an adaptive hybrid neural-network–CMAC (HNNCMAC) system for the control of non-linear dynamic time-varying plants; (2) adaptive laws are derived using the steepest-descent gradient approach and a back-propagation algorithm; (3) input range and quantity in the proposed CMAC could be reduced by the neural networks (NNs) pre-controller; (4) the stability of the proposed method is guaranteed by Lyapunov analysis; and (5) the method could be used for non-linear control problems, as proven by the results of numerical simulations

Summary

INTRODUCTION

The control of non-linear systems is a topic that continues to attract many researchers because of its widespread applications. In comparison with previous modified CMAC neural networks, as in Lin and Le (2017b) and Lin et al (2018a,b), the proposed HNNCMAC has some advantages, such as small CMAC structure, and ease in designing network size and initial membership functions. The main contributions of this study are: (1) the successful design of an adaptive HNNCMAC system for the control of non-linear dynamic time-varying plants; (2) adaptive laws are derived using the steepest-descent gradient approach and a back-propagation algorithm; (3) input range and quantity in the proposed CMAC could be reduced by the NN pre-controller; (4) the stability of the proposed method is guaranteed by Lyapunov analysis; and (5) the method could be used for non-linear control problems, as proven by the results of numerical simulations.

METHODS

SIMULATION OF RESULTS

CONCLUSIONS