模型预测控制
离散化
计算机科学
控制理论(社会学)
数学优化
最优控制
维数(图论)
控制器(灌溉)
计算复杂性理论
投影(关系代数)
线性系统
约束(计算机辅助设计)
模型降阶
理论(学习稳定性)
控制(管理)
数学
算法
人工智能
机器学习
数学分析
几何学
纯数学
农学
生物
作者
Joseph Lorenzetti,Andrew R. McClellan,Charbel Farhat,Marco Pavone
标识
DOI:10.1109/tac.2022.3179539
摘要
Model predictive controllers use dynamics models to solve constrained optimal control problems. However, computational requirements for real-time control have limited their use to systems with low-dimensional models. Nevertheless, high-dimensional models arise in many settings, for example discretization methods for generating finite-dimensional approximations to partial differential equations can result in models with thousands to millions of dimensions. In such cases, reduced order models (ROMs) can significantly reduce computational requirements, but model approximation error must be considered to guarantee controller performance. In this work, a reduced order model predictive control (ROMPC) scheme is proposed to solve robust, output feedback, constrained optimal control problems for high-dimensional linear systems. Computational efficiency is obtained by using projection-based ROMs, and guarantees on robust constraint satisfaction and stability are provided. Performance of the approach is demonstrated in simulation for several examples, including an aircraft control problem leveraging an inviscid computational fluid dynamics model with dimension 998,930.
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