Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem

Alejandro Rodríguez-Molina, Efrén Mezura-Montes, Miguel G. Villarreal-Cervantes, Mario Aldape-Pérez

    Research output: Contribution to journalArticlepeer-review

    9 Scopus citations

    Abstract

    Multi-objective optimization has been adopted in many engineering problems where a set of requirements must be met to generate successful applications. Among them, there are the tuning problems from control engineering, which are focused on the correct setting of the controller parameters to properly govern complex dynamic systems to satisfy desired behaviors such as high accuracy, efficient energy consumption, low cost, among others. These requirements are stated in a multi-objective optimization problem to find the most suitable controller parameters. Nevertheless, these parameters are tough to find because of the conflicting control performance requirements (i.e., a requirement cannot be met without harming the others). Hence, the use of techniques from computational intelligence and soft computing is necessary to solve multi-objective problems and handle the trade-offs among control performance objectives. Meta-heuristics have shown to obtain outstanding results when solving complex multi-objective problems at a reasonable computational cost. In this survey, the literature related to the use of multi-objective meta-heuristics in intelligent control focused on the controller tuning problem is reviewed and discussed.

    Original languageEnglish
    Article number106342
    JournalApplied Soft Computing Journal
    Volume93
    DOIs
    StatePublished - Aug 2020

    Keywords

    • Controller tuning
    • Intelligent control
    • Meta-heuristics
    • Multi-objective optimization

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