Parallel Model Predictive Control for Deterministic Systems

TL;DR

This paper introduces a parallel model predictive control method for deterministic systems that leverages multiple lookahead problems to improve control performance and computational efficiency.

Contribution

It proposes a novel parallel MPC approach that computes multiple lookahead minimizations simultaneously, enhancing performance guarantees over traditional single lookahead methods.

Findings

Parallel MPC harnesses multiple computing units effectively.

The method provides better performance guarantees than single lookahead MPC.

It offers an approximate solution to intractable infinite horizon control problems.

Abstract

In this note, we consider infinite horizon optimal control problems with deterministic systems. Since exact solutions to these problems are often intractable, we propose a parallel model predictive control (MPC) method that provides an approximate solution. Our method computes multiple lookahead minimization problems at each time, where each minimization may involve a different number of lookahead steps, and terminal cost and constraint. The policy computed via parallel MPC applies the first control of the lookahead minimization with the lowest cost. We show that the proposed method can harnesses the power of multiple computing units. Moreover, we prove that the policy computed via parallel MPC has better performance guarantee than that computed via the single lookahead minimization involved in parallel MPC.