A Computational Governor for Maintaining Feasibility and Low Computational Cost in Model Predictive Control

• 成果类型: Article
• 署名作者: Leung, Jordan; Permenter, Frank; Kolmanovsky, Ilya V.
• 署名单位: University of Michigan System; University of Michigan; Toyota Motor Corporation
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3292980
• 发表日期: 2024
• 页码: 2791-2806
• 关键词: Computational efficiency; Closed loop systems; asymptotic stability; Stability criteria; Predictive control; Numerical stability; CONVERGENCE; model predictive control (MPC); quadratic programming; stability analysis; interior-point methods

摘要

This article introduces an approach for reducing the computational cost of implementing linear quadratic model predictive control (MPC) for set-point tracking subject to pointwise-in-time state and control constraints. The approach consists of the following three key components: First, a log-domain interior-point method used to solve the receding horizon optimal control problems; second, a method of warm-starting this optimizer by using the MPC solution from the previous timestep; and third, a computational governor that maintains feasibility and bounds the suboptimality of the warm-start by altering the reference command provided to the MPC problem. Theoretical guarantees regarding the recursive feasibility of the MPC problem, asymptotic stability of the target equilibrium, and finite-time convergence of the reference signal are provided for the resulting closed-loop system. In a numerical experiment on a lateral vehicle dynamics model, the worst-case execution time of a standard MPC implementation is reduced by over a factor of 10 when the computational governor is added to the closed-loop system.