Data-Driven Iterative Optimal Control for Switched Dynamical Systems

This article presents a data-driven algorithm to compute optimal control inputs for input-constrained nonlinear optimal control problems with switched dynamics. We consider multi-stage optimal control problems where the control inputs and the switching instants are both unknown. Our key contribution is the new iterative online optimal control algorithm which mitigates sub-optimal control caused by model bias in the challenging class of under-actuated and intrinsically unstable switched dynamical systems. This is achieved by estimating the cost and computing the control inputs along measured trajectories of the controlled system instead of doing the same procedure along error-prone trajectories predicted by an inexact model. The algorithm is evaluated using an under-actuated and intrinsically unstable hopping robot in a simulation environment. The algorithm enables real-time data-driven optimal control using inaccurate models.