Title :
RBF-network-based sliding mode control
Author :
Lin, Sinn-Cheng ; Chen, Yung-Yaw
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
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;
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
DOI :
10.1109/ICSMC.1994.400138
