Intelligent Control for Nonlinear Systems using Dynamic Neural Networks with Robotic Applications

Abstract

ABSTRACTIn this article the problem of intelligent control for systems with complex, unknown, and high nonlinear dynamics is studied using neuro-control approaches. Some new schemes of dynamic neural networks (DNNs) are proposed to design robust learning control systems for a general class of multi-input and multi-output (MIMO) nonlinear systems with unknown dynamics. The detailed structure of the DNNs and their learning capability are first discussed. The synthesis and design methods for the purposes of regulation, tracking, and model reference control are then proposed. Based on the DNNs approaches presented in this article, a new torque control scheme for robot manipulators is developed. The potentials of this scheme are demonstrated extensively by simulation studies.