Empirical Comparison of Differential Evolution Variants for Industrial Controller Design

• Published in: International journal of computational intelligence systems Vol. 9; no. 5; pp. 957 - 970
• Main Authors: Wong, Tony, Bigras, Pascal, Duchaîne, Vincent, Roberge, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2016; Springer
• Subjects: Differential Evolution Variants; Fixed-order Structured Controller; Model Matching; Optimization; Performance Measures; Research Article; Statistical Comparison; Differential Evolution Variants; Fixed-order Structured Controller; Statistical Comparison; Performance Measures; Optimization; Model Matching
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.1080/18756891.2016.1237193

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/l/(bin, exp) and rand-to-best/l/(bin, exp) are effective in terms of mean best objective function value, average number of function evaluations, and objective function value progression.