A Probabilistic approach for model following of Markovian jump linear systems subject to actuator saturation

• Published in: International journal of control, automation, and systems Vol. 10; no. 5; pp. 1042 - 1048
• Main Authors: Wang, Linpeng, Zhu, Jin, Park, Junhong
• Format: Journal Article
• Language: English
• Published: Heidelberg Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.10.2012; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Actuators; Analysis; Complexity; Control; Control theory; Design; Engineering; Industrial equipment; Kalman filters; Linear programming; Linear systems; Markov analysis; Mechatronics; Optimization; Probabilistic methods; Probability theory; Robotics; Saturation; Stochasticity; Studies; System theory; 제어계측공학; model following; Markovian jump linear systems; particle control approach; Actuator saturation
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-012-0522-2

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.