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arXiv 2303.04749eess.SYcs.SY
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面向集合论模型预测控制的数据驱动鲁棒后向可达集

Data-Driven Robust Backward Reachable Sets for Set-Theoretic Model Predictive Control

Mehran Attar, Walter Lucia

AI总结:

针对未知约束线性系统的鲁棒后向可达集计算问题,提出基于含噪数据的zonotopic内近似方法,据此构建无需模型与数据的集合论MPC控制器,经仿真验证有效且性能可与基于模型的方案相比。

摘要:

本文提出了一种针对受有界扰动的未知约束线性系统,从含噪数据计算鲁棒后向可达集的新方法。具体而言,我们开发了一种可用于控制目的的zonotopic(zonotope,zonotopic为其形容词形式,指多面体 zonotope 类的)内近似获取算法。研究表明,若这类集合构建于包含状态和输入的扩展空间之上,可用于将系统的单步演化嵌入到计算得到的扩展区域中。随后利用该结果构建集合论模型预测控制器:其离线阶段构建一族递归的鲁棒数据驱动可达集,在线阶段无需明确借助系统模型或已有数据,即可递归计算可容许控制动作。通过数值仿真验证了所提数据驱动方案的有效性,并将其性能与基于模型的对应方案进行了对比。

英文摘要:

In this paper, we propose a novel approach for computing robust backward reachable sets from noisy data for unknown constrained linear systems subject to bounded disturbances. In particular, we develop an algorithm for obtaining zonotopic inner approximations that can be used for control purposes. It is shown that such sets, if built on an extended space including states and inputs, can be used to embed the system's one-step evolution in the computed extended regions. Such a result is then exploited to build a set-theoretic model predictive controller that, offline, builds a recursive family of robust data-driven reachable sets, and online, computes recursively admissible control actions without explicitly resorting to either a model of the system or the available data.The validity of the proposed data-driven solution is verified by means of a numerical simulation and its performance is contrasted with the model-based counterpart.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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