Literature DB >> 19398404

Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems.

Chih-Min Lin1, Te-Yu Chen.   

Abstract

This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.

Mesh:

Year:  2009        PMID: 19398404     DOI: 10.1109/TNN.2009.2013852

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  1 in total

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

Authors:  Jian-Sheng Guan; Shao-Jiang Hong; Shao-Bo Kang; Yong Zeng; Yuan Sun; Chih-Min Lin
Journal:  Front Neurosci       Date:  2019-05-29       Impact factor: 4.677

  1 in total

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