Nonlinear Model Predictive Control using Multi-Model Approach Based on Fractal Dimension Measurement

被引：0
|
作者：
Luo Wenguang [1 ]
Lan Hongli [2 ]
机构：
[1] Guangxi Univ Technol, Dept Elect Informat & Control Engn, Liuzhou, Peoples R China
[2] Guangxi Univ Technol, Dept Comp Engn, Liuzhou, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 2 | 2009年
关键词：
nonlinear system; switch control; fractal dimension measurement; Euclid norm; multi-model control; ADAPTIVE-CONTROL;
D O I：
10.1109/ICICISYS.2009.5358330
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A nonlinear discrete time system can be locally linearized and represented by a multi-model structure, and model's switching operation will affect system's performances A novel switching strategy is proposed to make the multi-model system satisfy the given performances, namely, Fractal Dimension Measurement (shortened as FDM) of Euclid Norms between working points and the equilibrium point acts as a criterion for switching A model predictive control strategy based on Laguerre functions is designed to make each linear system optimize for a given cost function The simulation results are presented to validate the method
引用
收藏
页码：627 / +
页数：2
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