A sliding mode controller using neural networks for robot manipulator

Abstract. This paper proposes a new sliding mode controller using neural networks. Multilayer neural networks with the error back-propagation learning algorithm are used to compensate for the system uncertainty in order to reduce tracking errors and control torques. The stability of the proposed control scheme is proved with the Lyapunov function method. Computer simulation shows that the proposed neuro-controller yields better control performance than the conventional sliding mode controller in the view of tracking errors and overall control torque.

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Hajoon Lee, Dongkyung Nam, Cheol Hoon Park
• Keywords: ESANN 2004, ESANN 2007, Mode Controller, Neural Networks, Sliding Mode