An Efficient MPC Algorithm based on a Priori Zone Control

P. Park, S. Kim, J. Moon, M. Shin
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Abstract

This paper presents an efficient MPC algorithm for uncertain time-varying systems with input constraints. The proposed algorithm adopts the method of increasing free control horizon in the dual mode (i.e., a free control mode in the first finite horizon and a state-feedback mode in the following infinite horizon) paradigm so as to enlarge the set of stabilizable initial states. In the method, however, since the number of LMIs growing exponentially with the free control horizon makes the corresponding optimization problems intractable even for small horizon, it is impracticable to blindly increase the free control horizon. The objective of this paper is to relax the restriction on increase of the free control horizon, incurred on computational burdens in the method. By choosing a combination of hyper-boxes including a possible region of the initial states and then by designing a priori zone controller for each hyper-box so as to send any initial states in the hyper-box into the invariant ellipsoidal target set, the algorithm can dramatically reduce the on-line computational burden for enlarging the set of stabilizable initial states
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基于先验区域控制的高效MPC算法
针对具有输入约束的不确定时变系统,提出了一种高效的MPC算法。该算法采用双模式(即第一个有限视界的自由控制模式和下一个无限视界的状态反馈模式)范式中增加自由控制视界的方法,以扩大可稳定初始状态集。然而,由于lmi的数量随自由控制水平呈指数增长,使得即使在很小的水平下,相应的优化问题也难以解决,因此盲目地增加自由控制水平是不可行的。本文的目的是放宽该方法对自由控制水平的增加所带来的计算负担的限制。该算法通过选择包含可能初始状态区域的超盒组合,然后为每个超盒设计一个先验区域控制器,将超盒中的任意初始状态发送到不变椭球目标集中,从而大大减少了扩大可稳定初始状态集的在线计算量
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