Neural-Network-Based Discrete-Time Variable Structure Control of Robotic Manipulators

This paper presents a neural-network-based discrete-time variable structure control for a planar robotic manipulator. Radial basis function neural networks are used to learn about uncertainties affecting the system. The analysis of the control stability is given and the controller is experimentally evaluated on the ERICC robot arm. The experiments show that the proposed controller produces good trajectory tracking performance and is robust in the presence of model inaccuracies.