Linear-Convex Optimal Steady-State Control

• 成果类型: Article
• 署名作者: Lawrence, Liam S. P.; Simpson-Porco, John W.; Mallada, Enrique
• 署名单位: University of Toronto; University of Toronto; Johns Hopkins University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3044275
• 发表日期: 2021
• 页码: 5377-5384
• 关键词: Optimization; steady-state; regulation; computational modeling; Closed loop systems; predictive models; Convex functions; Convex Optimization; online optimization; output regulation; reference tracking and disturbance rejection

摘要

We consider the problem of designing a feedback controller for a multivariable linear time-invariant system, which regulates a system output to the solution of an equality-constrained convex optimization problem despite unknown constant exogenous disturbances; we term this the linear-convex optimal steady-state (OSS) control problem. We introduce the notion of an optimality model, and show that the existence of an optimality model is sufficient to reduce the OSS control problem to a stabilization problem. This yields a design framework for OSS control that unifies and extends existing design methods in the literature. We illustrate the approach via an application to optimal frequency control of power networks, where our methodology recovers centralized and distributed controllers reported in the recent literature.