Robust nonlinear receding-horizon control of Euler-Lagrange systems

In this note, we present a nonlinear receding-horizon controller for Euler-Lagrange systems. The derived nonlinear predictive law uses a quadratic objective function of the predicted tracking error and the predicted control signal. It is shown that this controller achieves positions tracking objectives via positions measurements. The stability convergence of the output tracking error to the origin is proved by the well-known Lyapunov method. To enhance the robustness of the closed loop system with respect to parameters variations and uncertainties, an integral action is introduced in the loop. Simulation results for a two-link rigid robot are performed to validate the performance of the proposed controlle