System identification and control using genetic algorithms

Kristinsson, K.; Dumont, Guy A.

doi:10.1109/21.179842

Cited by 447 publications

(166 citation statements)

References 7 publications

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“…They are effective in both domains for finding poles and zeroes. Simulations for minimum and non-minimum phase systems and a system with unmodeled dynamics were presented by Kristinsson and Dumont (1992).…”

Section: A Review Of Workability and Applicability Of Genetic Algoritmentioning

confidence: 99%

Genetic Algorithms: Concepts, Design for Optimization of Process Controllers

Malhotra¹,

Singh²,

Singh³

2011

CIS

154

111

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Genetic Algorithm is a search heuristic that mimics the process of evaluation. Genetic Algorithms can be applied to process controllers for their optimization using natural operators. This paper discusses the concept and design procedure of Genetic Algorithm as an optimization tool. Further, this paper explores the well established methodologies of the literature to realize the workability and applicability of genetic algorithms for process control applications. Genetic Algorithms are applied to direct torque control of induction motor drive, speed control of gas turbine, speed control of DC servo motor for the optimization of control parameters in this work. The simulations were carried out in simulink package of MATLAB. The simulation results show better optimization of hybrid genetic algorithm controllers than fuzzy standalone and conventional controllers.

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Section: A Review Of Workability and Applicability Of Genetic Algoritmentioning

confidence: 99%

Genetic Algorithms: Concepts, Design for Optimization of Process Controllers

Malhotra¹,

Singh²,

Singh³

2011

CIS

154

111

View full text Add to dashboard Cite

show abstract

“…In neural network based methods, the problem is modeled as a network consisting of several neurons and the network is trained suitably to solve the optimization problem efficiently [2], [3]. A system identification problem using genetic algorithms is addressed in [9]. A similar problem of identification of parameters of an induction motor using Genetic Algorithms is developed in [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Parameters for Model Matching using Genetic Algorithms

Ps¹

2014

IJCA

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This paper develops an optimization procedure to estimate the parameter values of a given dynamic system. The actual nonlinear model of the system is assumed available with the approximate range of parameter values. The model used in this work is the PS4 actuator with nozzle dynamics which is used for the liquid upper stage control system of PSLV. The objective is to estimate the parameter values so that the error between actual system output and the simulated output is minimized. This is achieved through Genetic Algorithms(GA) which is a global optimization technique. GAs are stochastic algorithms based on Darwin's theory of survival of the fittest. They are inspired by biological phenomena of natural genetics and natural selection. The basic elements of natural genetics-reproduction, crossover and mutation-are used in the genetic search procedures. GA is proved robust and efficient in finding optimal solutions in complex problem spaces.

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“…Genetic algorithm (Passino, 1996) is a general-purpose optimization algorithm based on the mechanics of natural selection and genetics. Kristinsson and Dumont (1992) proposed GA to identify plants with either minimum phase or non-minimum phase characteristic and un-modeled dynamics. Zibo and Naghdt (1995) applied genetic algorithms to identify the parameters of the Multi Input and Multi Output (MIMO) system that is assumed to have an Auto Regressive with Moving Average Exogenous (ARMAX) structure.…”

Section: Introductionmentioning

confidence: 99%

Intelligent techniques for system identification and controller tuning in pH process

Valarmathi

Devaraj

Radhakrishnan

2009

Braz. J. Chem. Eng.

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-This paper presents an application of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for system identification for controller tuning in a pH process. In this paper, the ANN based approach is applied to estimate the system parameters. Once the variations in parameters are identified frequently, GA optimally tunes the controller. The simulation results show that the proposed intelligent technique is effective in identifying the parameters and has resulted in a minimum value of the Integral Square Error, peak overshoot and minimum settling time as compared to conventional methods. The experimental results show that their performance is superior and it matches favorably with the simulation results.

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System identification and control using genetic algorithms

Cited by 447 publications

References 7 publications

Genetic Algorithms: Concepts, Design for Optimization of Process Controllers

Genetic Algorithms: Concepts, Design for Optimization of Process Controllers

Estimation of Parameters for Model Matching using Genetic Algorithms

Intelligent techniques for system identification and controller tuning in pH process

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