Statistically Consistent Approximate Model Predictive Control

TL;DR

This paper introduces a statistically consistent imitation learning method for approximate model predictive control that accurately captures set-valued policies and guarantees stability, outperforming traditional behavioral cloning.

Contribution

It proposes a novel two-stage imitation learning approach that integrates the MPC's value function, ensuring convergence to safe, stabilizing policies with theoretical guarantees.

Findings

Improved approximation of nonlinear, set-valued MPC policies.

Theoretical proof of statistical consistency and stability guarantees.

Enhanced simulation performance over behavioral cloning.

Abstract

Model Predictive Control (MPC) offers rigorous safety and performance guarantees but is computationally intensive. Approximate MPC (AMPC) aims to circumvent this drawback by learning a computationally cheaper surrogate policy. Common approaches focus on imitation learning (IL) via behavioral cloning (BC), minimizing a mean-squared-error loss on a collection of state-input pairs. However, BC fundamentally fails to provide accurate approximations when MPC solutions are set-valued due to non-convex constraints or local minima. We propose a two-stage IL procedure to accurately approximate nonlinear, potentially set-valued MPC policies. The method integrates an approximation of the MPC's optimal value function into a one-step look-ahead loss function, and thereby embeds the MPC's constraint and performance objectives into the IL objective. This is achieved by adopting a stabilizing soft constrained MPC formulation, which reflects constraint violations in the optimal value function by combining a constraint tightening with slack penalties. We prove statistical consistency for policies that exactly minimize our IL objective, implying convergence to a safe and stabilizing control law, and establish input-to-state stability guarantees for approximate minimizers. Simulations demonstrate improved performance compared to BC.