Iterative Online Optimal Feedback Control

• 成果类型: Article
• 署名作者: Chen, Yuqing; Braun, David J.
• 署名单位: Singapore University of Technology & Design; Vanderbilt University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2986211
• 发表日期: 2021
• 页码: 566-580
• 关键词: Nonlinear systems; optimal control; optimization algorithms; uncertain systems

摘要

This article proposes a data-driven iterative feedback control method to efficiently solve finite time horizon, nonlinear, input constrained optimal control problems. The proposed method introduces a novel approach to combine an inexact system model with measured state information to reduce the cost and provide near-optimal control by approximately solving the optimal control problem along the trajectory of the real system, as opposed to solving it along the trajectory predicted by the inexact model. We present a new algorithm that implements the proposed method, establish the convergence and optimality properties of the proposed algorithm, and compare it to optimal feedback control and model predictive control that solve the same optimal control problem along the trajectory predicted by the inexact model. Finally, we illustrate the generality of the proposed algorithm by approximately solving a challenging optimal control problem with unknown and changing dynamics.