论文标题
线性减少订单模型预测控制
Linear Reduced Order Model Predictive Control
论文作者
论文摘要
模型预测控制器使用动力学模型来解决受约束的最佳控制问题。但是，实时控制的计算要求将其用于低维模型的系统的使用限制。然而，在许多设置中出现了高维模型，例如，用于生成有限维近似值的偏微分方程的离散化方法可能会导致具有数千至数百万个维度的模型。在这种情况下，减少的订单模型（ROM）可以大大降低计算要求，但是必须考虑模型近似误差以确保控制器性能。在这项工作中，提出了降低的订单模型预测控制（ROMPC）方案，以解决高维线性系统的强大，输出反馈，最佳控制问题。通过使用基于投影的ROM获得计算效率，并提供了可靠的约束满意度和稳定性。在模拟中显示了该方法的性能，其中有几个示例，包括利用尺寸998,930的Inviscid计算流体动力学模型的飞机控制问题。
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.
