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arXiv 2003.10166eess.SYcs.SY
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Constrained Controller and Observer Design by Inverse Optimality

Mario Zanon, Alberto Bemporad

英文摘要:

Model Predictive Control (MPC) is often tuned by trial and error. When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active. We formulate this problem as a controller matching similar to [1]-[3], which we extend to a more general framework. We prove that a positive-definite stage cost matrix yielding this matching property can be computed for all stabilizing linear controllers. Additionally, we prove that the constrained estimation problem can also be solved similarly, by matching a linear observer with a Moving Horizon Estimator (MHE). Finally, we discuss various aspects of the practical implementation of the proposed technique in some examples.

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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