Feedback nonlinear predictive control of rigid link robot manipulators

Abstract

Three nonlinear continuous-time predictive control schemes are proposed to address the trajectory tracking control problem of rigid link robot manipulators. The control laws using state variable feedback minimize a quadratic performance index of the state predicted tracking error. Without online optimization, an asymptotic tracking of smooth reference trajectories is guaranteed. The proposed controllers achieve the positions and speed tracking objectives via link position measurements. Lyapunov theory is used to prove the boundedness and stability convergence of the state tracking. Robustness with respect to payload uncertainties and viscous friction is shown. Simulations for a two-link rigid robot are performed to validate the proposed controller.