arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2303.04777eess.SYcs.LGcs.SYmath.OC
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基于LMI的数据驱动鲁棒模型预测控制

LMI-based Data-Driven Robust Model Predictive Control

Technical University of Darmstadt(达姆施塔特工业大学)

Hoang Hai Nguyen, Maurice Friedel, Rolf Findeisen

摘要:

预测控制是一种基于系统模型来计算应用输入以优化未来系统行为的控制方法,目前已得到广泛应用。如果标称模型没有给出或者非常不确定,则可以采用数据驱动的模型预测控制方法,其中系统模型或输入直接从过去测量的轨迹获得。利用数据信息框架和Finsler引理,提出了一种基于数据驱动的鲁棒线性矩阵不等式模型预测控制方案,该方案考虑了输入和状态约束.使用这些数据,我们制定的问题作为一个半定优化问题,其解决方案提供了线性反馈的矩阵增益,而决定性的变量是独立的测量数据的长度。所设计的控制器使闭环系统渐近稳定,并保证约束满足。最后通过数值算例说明了该方法的有效性。

英文摘要:

Predictive control, which is based on a model of the system to compute the applied input optimizing the future system behavior, is by now widely used. If the nominal models are not given or are very uncertain, data-driven model predictive control approaches can be employed, where the system model or input is directly obtained from past measured trajectories. Using a data informativity framework and Finsler's lemma, we propose a data-driven robust linear matrix inequality-based model predictive control scheme that considers input and state constraints. Using these data, we formulate the problem as a semi-definite optimization problem, whose solution provides the matrix gain for the linear feedback, while the decisive variables are independent of the length of the measurement data. The designed controller stabilizes the closed-loop system asymptotically and guarantees constraint satisfaction. Numerical examples are conducted to illustrate the method.

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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