DMPC: A Data-and Model-Driven Approach to Predictive Control

TL;DR

DMPC is a hybrid predictive control method that combines model-based and black-box predictions, efficiently optimizing trajectories with minimal data and theoretical guarantees, demonstrated on autonomous vehicle motion planning.

Contribution

This paper introduces DMPC, a novel approach that merges model-based and data-driven predictions for control, with proven convergence and efficiency in complex scenarios.

Findings

Converges to optimal trajectories within few iterations

Handles nonlinear dynamics effectively

Requires minimal data for trajectory optimization

Abstract

This work presents DMPC (Data-and Model-Driven Predictive Control) to solve control problems in which some of the constraints or parts of the objective function are known, while others are entirely unknown to the controller. It is assumed that there is an exogenous ``black box'' system, e.g. a machine learning technique, that predicts the value of the unknown functions for a given trajectory. DMPC (1) provides an approach to merge both the model-based and black-box systems; (2) can cope with very little data and is sample efficient, building its solutions based on recently generated trajectories; and (3) improves its cost in each iteration until converging to an optimal trajectory, typically needing only a few trials even for nonlinear dynamics and objectives. Theoretical analysis of the algorithm is presented, proving that the quality of the trajectory does not worsen with each new iteration, as well as providing bounds on the complexity. We apply the DMPC algorithm to the motion planning of an autonomous vehicle with nonlinear dynamics.