Adaptive control of stochastic system by using multiple models

被引：1

作者：

Li, Xiaoli ^{[1
]}

Kang, Yunfeng ^{[1
]}

Yin, Yixin ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Dept Automat, Beijing 100083, Peoples R China

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION ACQUISITION, VOLS 1 AND 2, CONFERENCE PROCEEDINGS | 2006年

关键词：

multiple model; adaptive control; stochastic system;

D O I：

10.1109/ICIA.2006.305948

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1344 / 1348

页数：5

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