Practical Considerations for Implementing Robust-to-Early Termination Model Predictive Control

TL;DR

This paper investigates the discrete-time implementation of the Robust-to-Early Termination (REAP) strategy for Model Predictive Control, ensuring feasibility and convergence in practical, computationally limited systems through simulations and experiments.

Contribution

It extends REAP from continuous to discrete time, establishing conditions for maintaining its theoretical guarantees in real-world applications.

Findings

Discrete-time REAP preserves feasibility and convergence under certain conditions.

Simulation and experimental results validate the effectiveness of discrete-time REAP.

The approach enables practical deployment of MPC with limited computational resources.

Abstract

Model Predictive Control (MPC) is widely used to achieve performance objectives, while enforcing operational and safety constraints. Despite its high performance, MPC often demands significant computational resources, making it challenging to implement in systems with limited computing capacity. A recent approach to address this challenge is to use the Robust-to-Early Termination (REAP) strategy. At any time instant, REAP converts the MPC problem into the evolution of a virtual dynamical system whose trajectory converges to the optimal solution, and provides guaranteed sub-optimal and feasible solution whenever its evolution is terminated due to limited computational power. REAP has been introduced as a continuous-time scheme and its theoretical properties have been derived under the assumption that it performs all the computations in continuous time. However, REAP should be practically implemented in discrete-time. This paper focuses on the discrete-time implementation of REAP, exploring conditions under which anytime feasibility and convergence properties are maintained when the computations are performed in discrete time. The proposed methodology is validated and evaluated through extensive simulation and experimental studies.