Decreasing-horizon Robust Model Predictive Control With Specified Settling Time To A Terminal Constraint Set
参考中译:具有指定建立时间的降时域鲁棒模型预测控制到终端约束集


          

刊名：Asian Journal of Control: Affiliated with ACPA, the Asian Control Professors' Association
作者：Yang, Weilin（Masdar Inst Sci & Technol, Dept Mech & Mat Engn, Abu Dhabi, U Arab Emirates）
Feng, Gang（City Univ Hong Kong, Dept Mech & Biomed Engn, Kowloon, Hong Kong, Peoples R China）
Zhang, TieJun（Masdar Inst Sci & Technol, Dept Mech & Mat Engn, Abu Dhabi, U Arab Emirates）
刊号：737C0022
ISSN：1561-8625
出版年：2016
年卷期：2016, vol.18, no.2
页码：664-673
总页数：10
分类号：TP13
关键词：Robustness；model predictive control；specified settling time；affine feedback controller
参考中译：鲁棒性；模型预测控制；指定建立时间；仿射反馈控制器
语种：eng
文摘：Robust model predictive control for discrete-time linear systems with norm-bounded disturbances is investigated in this paper. The control objective is to steer the system state to a terminal constraint set within specified number of steps. Meanwhile, the performance of the closed-loop control system is optimized. A decreasing-horizon predictive control strategy is proposed. Moreover, affine state-feedback control laws with memory of prior states are adopted over the prediction horizon. To optimize the system performance, an H-type cost function is considered in this paper. It is shown that finite settling time is achieved, if the optimization problem in the proposed control strategy is initially solvable. Some simulations are presented to show the effectiveness of the proposed control strategy.
参考中译：研究了具有范数有界扰动的离散线性系统的鲁棒模型预测控制问题。控制目标是将系统状态引导到指定步数内的终端约束集。同时，对闭环控制系统的性能进行了优化。提出了一种递减时域预测控制策略。此外，在预测时域内采用具有先验状态记忆的仿射状态反馈控制律。为了优化系统性能，本文考虑了一个H-型代价函数。结果表明，如果所提出的控制策略中的优化问题是初始可解的，则可以获得有限的调整时间。仿真结果表明了该控制策略的有效性。



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