Robust min-max model predictive control of linear systems with constraints

J. Zeman, B. Rohal’-Ilkiv
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引用次数: 9

Abstract

In this paper the method of design the robust MPC controller for nonzero targets tracking is presented. The proposed method is based on dual mode paradigm in which the inner unconstrained controller is designed together with the robust feasible and invariant region. This robust feasible and invariant set unsure that the unconstrained controller does not violate the process constraints. So, the theory of robust invariant sets is investigated and the algorithm for finding needed robust invariant and feasible is presented. The outer controller is based on the solution of a min-max finite horizon optimisation problem. The mentioned min-max formulation of MPC involves minimisation of maximum of disturbance influence which range over the given polytope of possible disturbance realisations In the formulation of the robust MPC is a new linear cost function used that allow computation of the min-max optimisation problem using only single linear program. At the end, the illustrative real-time experiment is given.
带约束线性系统的鲁棒最小-最大模型预测控制
提出了用于非零目标跟踪的鲁棒MPC控制器的设计方法。该方法基于双模范式,设计了内无约束控制器和鲁棒可行不变区域。该鲁棒可行不变集不确定无约束控制器不违反过程约束。因此,研究了鲁棒不变量集的理论,给出了寻找所需鲁棒不变量集和可行集的算法。外部控制器基于最小-最大有限水平优化问题的求解。上述MPC的最小-最大公式涉及在给定可能的干扰实现多面体的范围内最大干扰影响的最小化。在鲁棒MPC的公式中使用了一个新的线性代价函数,允许使用单个线性程序计算最小-最大优化问题。最后,给出了一个说明性实时实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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