An approximate model matching technique for controller design of linear time-invariant systems using hybrid firefly-based algorithms. (August 2022)

• Record Type: Journal Article
• Authors: Ganguli, Souvik; Kaur, Gagandeep; Sarkar, Prasanta
• Is Part Of: ISA transactions. Volume 127(2022)
• Page Start: 437
• Page End: 448
• Publication Date: 2022-08
• Subjects: Hybrid firefly algorithms -- Approximate model matching (AMM) -- Controller design -- Delta operator; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.08.043
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this article, a delta operator based intelligent control scheme is developed with the help of model match approximately using some new firefly-based hybrid metaheuristic algorithms developed by the authors. The design approach is systematic and applies to a wide variety of plant models. The approach depends mainly on an approximate model match and gives rise to rational controllers of a lower order that can be implemented using only output feedback. The plant model, when attached with a PID controller having unknown parameters, is compared with a model considered as a reference using an approximation technique to realize the controller in the unified domain. It can be agreed upon that the estimated controller parameters using the unified delta operator approach are quite similar to those found in the continuous-time domain. Thus, a consolidated approach in the area of controller synthesis is established. Some typical models widely popular in the literature further justify the usefulness of the advocated techniques. A sufficient number of comparison has been carried out to validate the efficacy of the techniques presented. The percentage improvement of the proposed methods over the parent algorithms and other techniques has also been shown for each of the test systems. The selection of a suitable reference model may increase the utility of the new control scheme applicable to various unstable and non-minimum-phase plant models. Highlights: An intelligent control scheme is developed using approximate model matching with the help of some new firefly-based hybrid metaheuristic algorithms. The control scheme is tested for a wide variety of model plants with satisfactory results. A unified framework of controller design is established using delta-operator modelling.