Information-aware Lyapunov-based MPC in a feedback-feedforward control strategy for autonomous robots

This paper proposes a feedback-feedforward control scheme that combines the benefits of an online active sensing control strategy (the feedforward control component) to maximize the information needed for correctly executing the desired task, with a Lyapunov-based control strategy (the feedback control component) that guarantees an asymptotic convergence towards the task itself. To quantify the amount of the collected information along the planned trajectories, the smallest eigenvalue of the Constructability Gramian is adopted as a metric and optimized, for generating the feedforward control component, within a Lyapunov-based Model Predictive Control framework (LMPC). The latter indeed allows to systematically handle the closed-loop stability and robustness properties of a Lyapunov-based nonlinear control law, and, at the same time, to reduce the estimation uncertainty and, thus, increase the task execution performance. To show the effectiveness of our method, we consider three case studies where a unicycle equipped with suitable onboard sensors has to perform three classical tasks in mobile robotics: path following, point-to-point motion and trajectory tracking.