Clustering-based Mode Reduction for Markov Jump Systems
Clustering-based Mode Reduction for Markov Jump Systems
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发表时间：
2022
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通讯作者：
Zhe Du;N. Ozay;L. Balzano
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作者：
Zhe Du;N. Ozay;L. Balzano
虽然马尔可夫跳跃系统（MJS）在建模突然变化的动态方面比LTI系统更合适，但MJS（和其他切换系统）可能会受到由潜在的绝对数量的切换模式带来的模型复杂性的影响。现有的工作减少切换系统集中在状态空间的离散化和降维等技术进行，但减少模式的复杂性很少受到关注。在这项工作中，受无监督学习的聚类技术的启发，我们提出了一种MJS的简化方法，使得可以构建具有保证近似性能的模式简化MJS。此外，我们展示了如何减少MJS可以用于设计控制器的原始MJS，以减少计算成本，同时保持保证次优。
While Markov jump systems (MJSs) are more appropriate than LTI systems in terms of modeling abruptly changing dynamics, MJSs (and other switched systems) may suffer from the model complexity brought by the potentially sheer number of switching modes. Much of the existing work on reducing switched systems focuses on the state space where techniques such as discretization and dimension reduction are performed, yet reducing mode complexity receives few attention. In this work, inspired by clustering techniques from unsupervised learning, we propose a reduction method for MJS such that a mode-reduced MJS can be constructed with guaranteed approximation performance. Furthermore, we show how this reduced MJS can be used in designing controllers for the original MJS to reduce the computation cost while maintaining guaranteed suboptimality.