Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

DOI

10.1080/18756891.2016.1237193How to use a DOI?

Keywords

Performance Measures; Differential Evolution Variants; Fixed-order Structured Controller; Model Matching; Optimization; Statistical Comparison

Abstract

To be cost-effective, most commercial off-the-shelf industrial controllers have low system order and a predefined internal structure. When operating in an industrial environment, the system output is often specified by a reference model, and the control system must closely match the model’s response. In this context, a valid controller design solution must satisfy the application specifications, fit the controller’s configuration and meet a model matching criterion. This paper proposes a method of solving the design problem using bilinear matrix inequality formulation, and the use of Differential Evolution (DE) algorithms to solve the resulting optimization problem. The performance of the proposed method is demonstrated by comparing a set of ten DE variants. Extensive statistical analysis shows that the variants best/1/{bin, exp}and rand-to-best/1/{bin,exp}are effective in terms of mean best objective function value, average number of function evaluations, and objective function value progression.

Copyright

© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Tony Wong
AU  - Pascal Bigras
AU  - Vincent Duchaîne
AU  - Jean-Philippe Roberge
PY  - 2016
DA  - 2016/09/01
TI  - Empirical Comparison of Differential Evolution Variants for Industrial Controller Design
JO  - International Journal of Computational Intelligence Systems
SP  - 957
EP  - 970
VL  - 9
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1237193
DO  - 10.1080/18756891.2016.1237193
ID  - Wong2016
ER  -