Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

被引:92
|
作者
Reynoso-Meza, Gilberto [1 ]
Blasco, Xavier [1 ]
Sanchis, Javier [1 ]
Martinez, Miguel [1 ]
机构
[1] Univ Politecn Valencia, Inst Univ Automat & Informat Ind, Valencia 46022, Spain
关键词
Evolutionary multi-objective optimisation; Multi-objective evolutionary algorithms; Multi-criteria decision making; Multi-objective optimisation design procedure; Controller tuning; PID CONTROLLERS; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; DECISION-MAKING; DESIGN OPTIMIZATION; ENGINEERING DESIGN; PREDICTIVE CONTROL; CONTROL-SYSTEMS; FUZZY CONTROL; MODEL;
D O I
10.1016/j.conengprac.2014.03.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:58 / 73
页数:16
相关论文
共 50 条
  • [41] A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation
    Eduardo Segredo
    Carlos Segura
    Coromoto León
    Emma Hart
    [J]. Soft Computing, 2015, 19 : 2927 - 2945
  • [42] Multi-objective PI controller tuning using NSGA-II with preferability
    Zhang, Tao
    Yin, Xiaokang
    [J]. 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 1562 - 1568
  • [43] Delaunay meshes simplification with multi-objective optimisation and fine tuning
    Fan, Linkun
    Wu, Caiyun
    He, Fazhi
    Fan, Bo
    Liang, Yaqian
    [J]. IET COLLABORATIVE INTELLIGENT MANUFACTURING, 2023, 5 (04)
  • [44] Multi-objective optimisation
    Bortfeld, T.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2007, 84 : S72 - S73
  • [45] Multi-objective optimisation and multi-criteria decision making in SLS using evolutionary approaches
    Padhye, Nikhil
    Deb, Kalyanmoy
    [J]. RAPID PROTOTYPING JOURNAL, 2011, 17 (06) : 458 - 478
  • [46] On the Integrity of Performance Comparison for Evolutionary Multi-objective Optimisation Algorithms
    Wilson, Kevin
    Rostami, Shahin
    [J]. ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI), 2019, 840 : 3 - 15
  • [47] EMOCS: Evolutionary Multi-objective Optimisation for Clinical Scorecard Generation
    Fraser, Diane P.
    Keedwell, Edward
    Michell, Stephen L.
    Sheridan, Ray
    [J]. PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19), 2019, : 1174 - 1182
  • [48] Use of evolutionary techniques for multi-objective optimisation of electromotion devices
    Göl, Ö
    Sobhi-Najafabadi, B
    [J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2005, 161 (1-2) : 300 - 304
  • [49] Robust product sequencing through evolutionary multi-objective optimisation
    Syberfeldt, Anna
    Gustavsson, Patrik
    [J]. International Journal of Manufacturing Research, 2015, 10 (04) : 371 - 383
  • [50] A novel high speed multi-objective evolutionary optimisation algorithm
    De Buck, Viviane
    Hashem, Ihab
    Van Impe, Jan
    [J]. IFAC PAPERSONLINE, 2020, 53 (02): : 6756 - 6761