Iterative Model Predictive Control for Piecewise Systems

In this letter, we present an iterative Model Predictive Control (MPC) design for piecewise nonlinear systems. We consider finite time control tasks where the goal of controller is to steer system from a starting configuration state while minimizing cost function. First, algorithm that leverages feasible trajectory completes task construct policy which guarantees and input constraints are recursively satisfied closed-loop reaches in time. Utilizing construction, iteration scheme iteratively generates safe trajectories have non-decreasing performance. Finally, test proposed strategy on discretized Spring Loaded Inverted Pendulum (SLIP) model with massless legs. show our methodology robust changes initial conditions disturbances acting system. Furthermore, demonstrate effectiveness minimum task.