Multiobjective evolutionary approach to the design of optimal controllers for interval plants via parallel computation

Chen Chien James Hsu, Chih Yung Yu, Shih Chi Chang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.

Original languageEnglish
Pages (from-to)2363-2373
Number of pages11
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE89-A
Issue number9
DOIs
Publication statusPublished - 2006 Sep

Fingerprint

Parallel Computation
Minimax Optimization
Interval Systems
Genetic algorithms
Controller
Controllers
Chromosome
Interval
Cost Function
Chromosomes
Genetic Algorithm
Interactive Genetic Algorithm
Cost functions
Minimax Problems
Constraint Handling
Multi-objective Genetic Algorithm
Fitness Function
Design Process
Fitness
Accelerate

Keywords

  • Genetic algorithms
  • Interval systems
  • Minimax optimization
  • Multi-objective genetic algorithms
  • Parallel computation
  • Robust controllers

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

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abstract = "Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.",
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