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

作者：Wang Linpeng; Zhu Jin^*; Park Junhong

来源：International Journal of Control Automation and Systems, 2012, 10(5): 1042-1048.

DOI：10.1007/s12555-012-0522-2

• Actuator saturation
• Markovian jump linear systems
• model following
• particle control approach

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.

• 出版日期2012-10
• 单位中国科学技术大学