ADAPTIVE SLIDING MODE CONTROL USING RBF NEURAL NETWORK FOR NONLINEAR SYSTEM

摘要：

A novel adaptive sliding mode controller based on Radial Basis Function neural network (RBENN) Is proposed In Oils paper for the nonlinear systems with uncertainties using feedback linearization method. An adaptive role is utilized to on line adj listing tbe weights of RBFNN. which is used to compute the equivalent control. Adaptive training algorithm was derived In the sense of Lyapunov stability analysis, so that the stability of the doscdioop system can be guaranteed even In tbe case of uncertainty. Using the RBFNN, Instead of multilayer feed forward network trained with back propagation, works out shorter reaching time. Chattering problem of SMC Is substantially derived in the proposed controller. Simulation results show thai the position tracking responses closely fallow the desired trajectory occurrence of the disturbances. Also, simulation results demonstrate that the proposed controller is a stable control scheme for the Inverted pendulum trajectory tracking applications and has strong rubu stness.

关键词： Adaptive Sliding mode control RBF neural network Redback linearization Inverted pendulum

作者: MING-GUANG ZHANG YU-WU CHEN PENG WANG ZHAO-GANG WANG

作者单位: School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1860-1865

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）