Minimum time learning model predictive control

In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed-loop system. We show how to construct a time varying safe set and terminal cost function using closed-loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non-decreasing performance. Computational efficiency is obtained by convexifing the time-varying safe set and time-varying terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and nondecreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples.

Automated summary

This paper presents a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. The LMPC policy is time varying and guarantees recursive constraint satisfaction and non-decreasing performance through constructing time varying safe set and terminal cost function. Computational efficiency is achieved by convexifing the time-varying safe set and terminal cost function.