Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Framework

Understand

In this paper, we propose a novel framework for approximating the explicit MPC policy for linear parameter-varying systems using supervised learning.

• Our learning scheme guarantees feasibility and near-optimality of the approximated MPC policy with high probability.
• Furthermore, in contrast to most existing approaches that only learn the MPC policy, we also learn the "dual policy", which enables us to keep a check on the approximated MPC's optimality online during the control process.
• If the check deems the control input from the approximated MPC policy safe and near-optimal, then it is applied to the plant, otherwise a backup controller is invoked, thus filtering out (severely) suboptimal control inputs.