An approximate model matching technique for controller design of linear time-invariant systems using hybrid firefly-based algorithms

Ganguli, Souvik; Kaur, Gagandeep; Sarkar, Prasanta

doi:10.1016/j.isatra.2021.08.043

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…A reference model approach is followed. A standard model is taken up as per [11]. This is compared with the controlled plant model containing unknown control parameters.…”

Section: Resultsmentioning

confidence: 99%

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Reduced Order Modelling and Control of Induction Motor with Chaos-Based Gorilla Troop Optimizers (CBGTO)

Ganguli

2022

Advances in Transdisciplinary Engineering

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This paper proposes 50 new chaos-based gorilla troop optimizers (CBGTO). It is possible to change two regulating parameters in the parent algorithm by using various one-dimensional chaotic maps. Along with these two controlling variables, one of the random variables is also changed in order. Ten well-known and widely used chaotic maps are used to create chaotic algorithms. An induction motor model of order five is used to evaluate the performance of the suggested method. In addition, the same approach is used to build an intelligent PID controller. By using the delta operator, the induction motor model is made simpler. The applicability of the findings is confirmed by statistical analyses of the optimised values, in this example, the integral of time-weighted absolute error (ITAE). Results for the test system under consideration, a 50 hp induction motor, are remarkably positive. Additional parameter changes could be done in the future to boost the functionality of the gorilla troop optimizer (GTO). The PID controller is put into action using a framework for approximate model matching. The novel methods outperform the existing standard and state-of-the-art methods in terms of convergence speed and accuracy.

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“…A reference model approach is followed. A standard model is taken up as per [11]. This is compared with the controlled plant model containing unknown control parameters.…”

Section: Resultsmentioning

confidence: 99%

“…Next an intelligent PID controller is proposed for the streamlined system. Design guidelines for approximate model matching (AMM) [11] are adopted to create the controller. Since AMM ensures that the controller will be physically realized, it's preferred over exact model matching (EMM).…”

Section: Problem Definitionmentioning

confidence: 99%

Reduced Order Modelling and Control of Induction Motor with Chaos-Based Gorilla Troop Optimizers (CBGTO)

Ganguli

2022

Advances in Transdisciplinary Engineering

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Modeling and Control of Induction Machine and Drive in the Combined Domain with New Chaotic Gorilla Troop Optimizer

Chaudhary¹,

Ganguli²

2022

Lecture Notes in Networks and Systems

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Delta Operator-Based Modelling and Control of High Power Induction Motor Using Novel Chaotic Gorilla Troop Optimizer

Chaudhary

Nagpal

Ganguli

2023

Advances in Intelligent Systems and Computing

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An approximate model matching technique for controller design of linear time-invariant systems using hybrid firefly-based algorithms

Cited by 8 publications

References 34 publications

Reduced Order Modelling and Control of Induction Motor with Chaos-Based Gorilla Troop Optimizers (CBGTO)

Reduced Order Modelling and Control of Induction Motor with Chaos-Based Gorilla Troop Optimizers (CBGTO)

Modeling and Control of Induction Machine and Drive in the Combined Domain with New Chaotic Gorilla Troop Optimizer

Delta Operator-Based Modelling and Control of High Power Induction Motor Using Novel Chaotic Gorilla Troop Optimizer

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