Stochastic Model Predictive Control for Constrained Linear Systems Using Optimal Covariance Steering
Stochastic Model Predictive Control for Constrained Linear Systems Using Optimal Covariance Steering
复制标题
使用最优协方差控制的约束线性系统的随机模型预测控制
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
P. Tsiotras
中科院分区：
文献类型：
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作者：
Kazuhide Okamoto;P. Tsiotras
本文针对状态和控制约束下具有加性高斯噪声的不确定线性系统，开发了一种随机模型预测控制器（SMPC）。该方法基于最近发展的有限视界最优协方差转向控制理论，在给定的终端时间将系统状态的均值和协方差转向到规定的目标值。我们在文献中表明，所提出的方法比传统的SMPC方法有几个优点。具体来说，该算法能够在保证稳定性和递归可行性的同时处理无界高斯加性噪声，并且计算量比以往的方法要少。此外，我们还证明了CS-SMPC算法的递归可行性和稳定性。通过数值模拟验证了该方法的有效性，并与传统方法进行了比较。
This work develops a stochastic model predictive controller (SMPC) for uncertain linear systems with additive Gaussian noise subject to state and control constraints. The proposed approach is based on the recently developed finite-horizon optimal covariance steering control theory, which steers the mean and the covariance of the system state to prescribed target values at a given terminal time. We show that the proposed approach has several advantages over traditional SMPC approaches in the literature. Specifically, it is shown that the newly developed algorithm can deal with unbounded Gaussian additive noise while ensuring stability and recursive feasibility, and with less computational cost than previous approaches. In addition, we demonstrate the recursive feasibility and guaranteed stability of the proposed CS-SMPC algorithm. The effectiveness of the proposed approach is verified and compared to traditional approaches using numerical simulations.
影响因子：
3
作者：
Kazuhide Okamoto;M. Goldshtein;P. Tsiotras
通讯作者：
Kazuhide Okamoto;M. Goldshtein;P. Tsiotras
线性时变系统的有限范围协方差控制
DOI：
10.1109/cdc.2017.8264189
发表时间：
2017
期刊：
Conference on Decision and Control
影响因子：
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作者：
Goldshtein, Maxim;Tsiotras, Panagiotis
通讯作者：
Tsiotras, Panagiotis