A Computationally Governed Log-domain Interior-point Method for Model Predictive Control

TL;DR

This paper presents a novel log-domain interior-point method for MPC that reduces computation time by 90% through a computational governor and warm-starting, enabling efficient real-time control with constraints.

Contribution

It introduces a computationally efficient MPC solver with a governor for suboptimality control, improving real-time performance in constrained control tasks.

Findings

Reduced worst-case computation time by 90%

Maintained good closed-loop performance

Effective warm-starting from previous solutions

Abstract

This paper introduces a computationally efficient approach for solving Model Predictive Control (MPC) reference tracking problems with state and control constraints. The approach consists of three key components: First, a log-domain interior-point quadratic programming method that forms the basis of the overall approach; second, a method of warm-starting this optimizer by using the MPC solution from the previous timestep; and third, a computational governor that bounds the suboptimality of the warm-start by altering the reference command provided to the MPC problem. As a result, the closed-loop system is altered in a manner so that MPC solutions can be computed using fewer optimizer iterations per timestep. In a numerical experiment, the computational governor reduces the worst-case computation time of a standard MPC implementation by 90, while maintaining good closed-loop performance.