Adaptive manipulator control based on RBF network approximation
Na Wang, Dongqing Wang · 2017
This paper proposes a neural network controller to achieve the tracking of a robot manipulator with a highly nonlinear structure, and presents a online adaptive control algorithm. The controller based on RBF neural network approximation is designed, and its stability and convergence is analyzed under four different circumstances. By minimizing the system error and considering the characteristic of manipulator, the online adaptive algorithm is worked out. By Lyapunov function method, the adaptive updated laws and the control laws have been developed to guarantee that the resulting closed-loop system is asymptotically stable. In addition, we also made neural network approximation for each uncertainty, and analyzed the overall stability of the system by its Lyapunov function. Finally, simulation results of a two-joint robotic manipulator certificate the effectiveness and accuracy of proposed methods.