A Novel Adaptive Neural Sliding Mode Control for Systems with Unknown Dynamics


In this paper, an adaptive neural sliding mode controller (ANSMC) is proposed as an asymptotically stable robust controller for a class of Control Affine Nonlinear Systems (CANSs) with unknown dynamics. In the proposed method a Control Affine Radial Basis function Network (CARBFN) is developed for online identification of CANSs. A recursive algorithm based on Extended Kalman Filter (EKF) is used for training of CARBFN to develop an adaptive model for CANSs with unknown and uncertain system dynamics to reduce the uncertainties to low values. Since the CARBFN model learns the system time-varying dynamics online, the ANSMC will compute an efficient control input adaptively. Due to high degree of robustness, the proposed controller can be widely used in real world applications. To demonstrate this efficiency, a robust control system is successfully designed for a chaotic Duffing forced oscillator system in the presence of unknown dynamics as well as the unknown oscillation disturbance which is not available for measurement.

Meeting Name

3rd International Workshop on Advanced Computational Intelligence, IWACI 2010 (2010: Aug. 25-27, Suzhou, China)


Electrical and Computer Engineering

Keywords and Phrases

Adaptive Models; Affine Nonlinear Systems; Asymptotically Stable; Degree of Robustness; Efficient Control; Forced Oscillators; Neural Sliding Mode Control; On-Line Identification; Oscillation Disturbance; Real-World Application; Recursive Algorithms; Robust Controllers; Sliding Mode Controller; Time-Varying Dynamics; Artificial Intelligence; Chaotic Systems; Controllers; Dynamics; Radial Basis Function Networks; Robust Control; Sliding Mode Control; Adaptive Control Systems

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2010 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.