Robust Adaptive Trajectory Tracking Sliding mode control based on Neural networks for Cleaning and Detecting Robot Manipulators

This paper proposes an robust adaptive control method based on Radial Basis Function Neural networks (RBFNN) to investigate the joint position control for periodic motion and predefined trajectory tracking control of two link Cleaning and Detecting Robot Manipulators (CDRM). The proposed control scheme uses a three layer RBFNN to approximate nonlinear robot dynamics. The RBF network is one of the most popular intelligent approaches which has shown a great promise in this sort of problems because of simple network structure and its faster learning capacity. When the RBF networks are used to approximate a nonlinear dynamic system, the control system is stable. In addition, Sliding mode control (SMC) is a well known nonlinear control strategy because of its robustness. A robust term function is selected as an auxiliary controller to guarantee the stability and robustness under various envirorments, such as the mass variation, the external disturbances and modeling uncertainties. The adaptation laws for the weights of the RBFNN are adjusted using the Lyapunov stability theorem, the global stability and robustness of the entire control system are guaranteed, and the tracking errors converge to the required precison, and position is proved. Finally, experiments performed on a two-link CDRM in electric power substation are provided in comparison with proportional differential (PD) and adaptive Fuzzy (AF) control to demonstrate superior tracking precision and robustness of the proposed control methodology.