Adaptive Fourier Series-Based Control of Electrically Driven Robot Manipulators

This paper presents a novel adaptive control for electrically driven robot manipulators based on the voltage control strategy. The control law is designed using a nominal model. Then, Fourier series is applied to estimate the uncertainty originated from the mismatch between the actual model and nominal model. The uncertainty includes parametric uncertainty, un-modeled dynamics and external disturbance. The adaptation laws for the coefficients of the Fourier series are derived from a Lyapunov function to guarantee the closed loop stability. The approximation error is then compensated to provide the asymptotically convergence of the tracking error. The case study is an articulated robot manipulator driven by permanent magnet DC motors. Simulation results show the effectiveness of the proposed method.