Learning MPC for the Berkeley Autonomous Racing Car (BARC)

Learning MPC for the Berkeley Autonomous Racing Car (BARC)

The Learning Model Predictive Control (LMPC) technique is applied to the autonomous racing problem. The goal of the controller is to minimize the time to complete a lap. The LMPC strategy uses the data from previous laps to learn from experience, improving its performance while satisfying safety requirements. Moreover, a system identification technique is proposed to estimate the vehicle dynamics. 

In the LMPC framework the data from each lap are used to build a control invariant set and an approximation of the value function. These quantities are used to guarantee safety and performance improvement between to tasks repetition. Finally, the LMPC framework is extended to handle repetitive task, as the one represented by a vehicle driving continuously on a race track.

Reference: https://arxiv.org/abs/1610.06534

Credits:
     MS Thesis of Maximilian Brunner
Supervisor:
     Ugo Rosolia
     Jon Gonzales

Predictive data-driven vehicle dynamics and powertrain control: from ECU to the cloud

Predictive data-driven vehicle dynamics and powertrain control: from ECU to the cloud

Learning MPC:                                                                                   A control framework to teach safety critical tasks

Learning MPC: A control framework to teach safety critical tasks