Predictive Control of an Electromagnetic Suspension System via Modified Locally Linear Model Tree with Merging Ability

Author

Jamab, Atiye Sarabi ; Mohammadzaman, Iman

Author_Institution

Fac. of Electr. Eng., Malek Ashtar Univ. of Technol., Tehran

fYear

2006

fDate

7-9 June 2006

Firstpage

1

Lastpage

6

Abstract

A predictive control algorithm based on modified locally linear model tree (LOLIMOT) with merging is implemented to control of an electromagnetic suspension system. A self-construction LOLIMOT is used to predict the response of the plant in a future time interval. This modified algorithm could improve the accuracy with reduced computational times and fewer rules which is important in real-time input optimization. An evolutionary programming (EP) is used to determine the optimized control variables for a finite future time interval. This method is applied to an electromagnetic suspension system (EMS) and simulation results show the effectiveness of the proposed predictive control strategy

Keywords

electromagnetic devices; evolutionary computation; magnetic levitation; optimal control; predictive control; electromagnetic suspension system; evolutionary programming; locally linear model tree; merging ability; optimized control variables; predictive control; real-time input optimization; Control systems; Electric variables control; Electrical equipment industry; Electromagnetic modeling; Genetic programming; Medical services; Merging; Partitioning algorithms; Predictive control; Predictive models; Electromagnetic Suspension system; LOLIMOT; Predictive control; evolutionary programming;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics and Intelligent Systems, 2006 IEEE Conference on

Conference_Location

Bangkok

Print_ISBN

1-4244-0023-6

Type

conf

DOI

10.1109/ICCIS.2006.252301

Filename

4017860