Adaptive sliding mode control of dynamic system using RBF neural network

作者：Fei Juntao^*; Ding Hongfei

来源：Nonlinear Dynamics, 2012, 70(2): 1563-1573.

DOI：10.1007/s11071-012-0556-2

• Radial basis function
• Adaptive neural network
• Sliding mode control

This paper presents a robust adaptive sliding mode control strategy using radial basis RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.

• 出版日期2012-10
• 单位河海大学