The proposed iterative Model Predictive Control algorithm successfully steered a Spring Loaded Inverted Pendulum model to a goal state while remaining robust to disturbances.
The proposed iterative MPC algorithm safely and efficiently steers piecewise nonlinear systems to a goal state, demonstrating robustness to disturbances in a SLIP walking model.
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 the controller is to steer the system from a starting configuration to a goal state while minimizing a cost function. First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time. Utilizing this construction, we present a policy iteration scheme that iteratively generates safe trajectories which have non-decreasing performance. Finally, we test the proposed strategy on a discretized Spring Loaded Inverted Pendulum (SLIP) model with massless legs. We show that our methodology is robust to changes in initial conditions and disturbances acting on the system. Furthermore, we demonstrate the effectiveness of our policy iteration algorithm in a minimum time control task.
Rosolia et al. (Fri,) conducted a other in Piecewise nonlinear systems (Robotics/Control). Iterative Model Predictive Control (MPC) algorithm vs. MPC tracking controller was evaluated on Finite time convergence to goal state while satisfying state and input constraints. The proposed iterative Model Predictive Control algorithm successfully steered a Spring Loaded Inverted Pendulum model to a goal state while remaining robust to disturbances.