RBF-network-based sliding mode control

Author

Lin, Sinn-Cheng ; Chen, Yung-Yaw

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan

Volume

2

fYear

1994

fDate

2-5 Oct 1994

Firstpage

1957

Abstract

A sliding mode controller (SMC) design method based on radial basis function network (RBFN) is proposed in this paper. Similar to the multilayer perceptron, the RBFN also known to be a good universal approximator. In this work, the weights of the RBFN are changed according to some adaptive algorithms for the purpose of controlling the system state to hit a user-defined sliding surface and then slide along it. The initial weights of the RBFN can be set to small random numbers, and then online tuned automatically, no supervised learning procedures are needed. By applying the RBFN-based sliding mode controller to control a nonlinear unstable inverted pendulum system, the simulation results show the expected approximation sliding property was occurred, and the dynamic behavior of the control system can be determined by the sliding surface

Keywords

adaptive control; control system synthesis; feedforward neural nets; neurocontrollers; nonlinear systems; pendulums; variable structure systems; adaptive algorithms; initial weights; neural networks; nonlinear unstable inverted pendulum system; radial basis function network; sliding mode control; sliding surface; universal approximator; Adaptive algorithm; Automatic control; Control systems; Design methodology; Multilayer perceptrons; Nonlinear control systems; Nonlinear dynamical systems; Radial basis function networks; Sliding mode control; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2129-4

Type

conf

DOI

10.1109/ICSMC.1994.400138

Filename

400138