文章基本信息

标题：Robust Adaptive Model Predictive Control with Worst-Case Cost
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作者：Anilkumar Parsi ; Andrea Iannelli ; Mingzhou Yin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：4222-4227
DOI：10.1016/j.ifacol.2020.12.2467
语种：English
出版社：Elsevier
摘要：AbstractA robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. Online set-membership identification is used to reduce uncertainty in the impulse response. In the MPC scheme, robust constraints are enforced to ensure constraint satisfaction for all the models in the feasible set. The performance objective is formulated as a worst-case cost with respect to the modeling uncertainties. That is, at each time step an optimization problem is solved in which the control input is optimized for the worst-case plant in the uncertainty set. The performance of the proposed algorithm is compared to an adaptive MPC algorithm from the literature using Monte-Carlo simulations.
关键词：Keywordspredictive controladaptive MPCimpulse responserobust optimization