Evolution of Controllers Under a Generalized Structure Encoding/Decoding Scheme With Application to Magnetic Levitation System

Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimi...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 69; no. 9; pp. 9655 - 9666
Main Authors: Xin, Bin, Wang, Yipeng, Xue, Wenchao, Cai, Tao, Fan, Zhun, Zhan, Jiaoyang, Chen, Jie
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automatic design of controllers
Combinatorial analysis
Control systems
Controllers
Design methodology
Design parameters
disturbance-rejection
Dynamical systems
Encoding-Decoding
Evolutionary design method
intelligent optimization algorithms
Magnetic levitation
magnetic levitation system (MLS)
Magnetic levitation systems
mixed-variable optimization
Nonlinear dynamics
Optimization
Periodic structures
Real numbers
Signal processing
Tuning
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this article. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indicators, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3114700