Nonlinear state feedback controller combined with RBF for nonlinear underactuated overhead crane system

Abstract

This paper proposes a robust control scheme for an underactuated crane system. The presented scheme contains two control strategies, feedback control term and corrective control term, based on Radial Basis Function (RBF) neural networks. A feedback control term is deigned based on the nominal dynamic model of the controlled system. RBF neural networks have been used as adaptive control term to compensate for the system uncertainties and external disturbance. Lyapunov stability theorem has been used to derive updating laws for the weights of the RBF neural networks. To illustrate the robustness and effectiveness of the proposed controller, Matlab program is used to simulate the model of the nonlinear overhead crane system with the proposed control method, taking into account system uncertainties and external disturbance. Simulation results indicated superior control performance of the proposed control method compared to the other control methods used in the test.