Sample-Based Learning Model Predictive Control for Linear Uncertain Systems
Sample-Based Learning Model Predictive Control for Linear Uncertain Systems
复制标题
线性不确定系统的基于样本的学习模型预测控制
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
F. Borrelli
中科院分区：
文献类型：
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作者：
Ugo Rosolia;F. Borrelli
针对受有界加性扰动约束的不确定线性系统，提出了一种基于样本的学习模型预测控制器（LMPC）。所提出的控制器建立在早期对确定性系统的LMPC工作的基础上。首先，我们介绍了用于保证安全性和性能改进的安全集和值函数的设计。之后，我们将展示如何使用有噪声的历史数据来近似这些量。通过数值算例验证了该方法的有效性。我们证明了LMPC能够安全地探索状态空间并迭代提高最坏情况闭环性能，同时鲁棒地满足状态和输入约束。
We present a sample-based Learning Model Predictive Controller (LMPC) for constrained uncertain linear systems subject to bounded additive disturbances. The proposed controller builds on earlier work on LMPC for deterministic systems. First, we introduce the design of the safe set and value function used to guarantee safety and performance improvement. Afterwards, we show how these quantities can be approximated using noisy historical data. The effectiveness of the proposed approach is demonstrated through a numerical example. We show that the LMPC is able to safely explore the state space and to iteratively improve the worst-case closed-loop performance, while robustly satisfying state and input constraints.