Title :
Adaptive Control of Stochastic System by Using Multiple Models
Author :
Li, Xiaoli ; Kang, Yunfeng ; Yin, Yixin
Author_Institution :
Dept. of Autom., Inf. & Eng. Sch., Univ. of Sci. & Technol. of China, Beijing
Abstract :
Two kinds of multiple model adaptive control (MMAC) methods are used for the control of stochastic system with measured noise and jumping parameters. In the first method, the multiple Kalman filters are used to calculate the weight of different local model, and control signal is generated as a probability-weight average of all the local controller outputs. For the second method, the controller of system is selected from multiple local model controllers by using an index switching function with form of integral of output error of each local model. From the simulation, it can be seen the former method is more effective compared with the later one when a stochastic system is controlled
Keywords :
Kalman filters; adaptive control; probability; stochastic systems; adaptive control; index switching function; multiple Kalman filter; multiple model adaptive control method; probability-weight averaging method; stochastic system; Adaptive control; Control system synthesis; Control systems; Error correction; Noise measurement; Optimal control; Performance evaluation; Signal generators; Stochastic resonance; Stochastic systems; Multiple model; adaptive control; stochastic system;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Weihai
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305948
