A Probabilistic Approach for Model Following of Markovian Jump Linear Systems Subject to Actuator Saturation

被引：5

作者：

Wang, Linpeng ^{[1
]}

Zhu, Jin ^{[1
]}

Park, Junhong ^{[2
]}

机构：

[1] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China

[2] Hanyang Univ, Dept Mech Engn, Seoul 133791, South Korea

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2012年 / 10卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Actuator saturation; Markovian jump linear systems; model following; particle control approach; ROBUST TRACKING;

D O I：

10.1007/s12555-012-0522-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is concerned with the model following problem of Markovian jump linear systems (MJLSs), which suffer from stochastic uncertainties and actuator saturation. By applying a probabilistic approach based on particles, a sequence of control inputs is designed to guarantee that the model following error remains within a desired region in a certain probability, as well as the control cost is optimal. Motivated by this, the stochastic control problem is represented by chance constrained programming, and approximated as a determinate optimization one, which is solved by mixed integer linear programming (MILP). Furthermore, an improved particle control approach is proposed to reduce the computation complexity. The effectiveness of this improved approach is demonstrated by an example along with complexity comparison.

引用

页码：1042 / 1048

页数：7

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