Design for auto-tuning PID controller based on genetic algorithms

Liu, Fan; Joo, Er Meng

doi:10.1109/iciea.2009.5138538

Cited by 35 publications

(12 citation statements)

References 13 publications

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“…The fitness function is used to provide a measure of how individuals have performed in the problem domain [13].The most crucial step in applying GA is to use the fitness function that is used to evaluate fitness of each chromosome. The fitness not only indicates how good the solution is but also corresponds to how close the chromosome it to the optimal one.…”

Section: Fitness Functionmentioning

confidence: 99%

Robust PID Tuning of Autonomous Underwater Vehicle Using Harmonic Search Algorithm Based on Model Order Reduction

Rathore¹,

Kumar²

2015

Int J Swarm Intel Evol Comput

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The objective of this paper is to present Genetic Algorithm (GA) and Harmonic Search Algorithm (HSA) based on PID tuning and Model Order Reduction with Balance Truncation (MORBT). This method is used for constructing a second order balanced truncation model order reduction for symmetric linear time invariant third order control systems. These methods are closely related to the proper and improper controllability and observability Gramians and Hankel singular values of descriptor systems. The HS (Harmonic Search) algorithm mimics behaviours of music players in an improvisation process, in order to find a better state of harmony which can be translated into a solution vector in the optimization process. PID (proportional-integral-derivative) controller provides sufficient stability margins and good time responses. It is now possible to design an optimal PID controller with properties of a GA and HSA. In control strategies, like PID controller are successfully designed to control the autonomous underwater vehicle. The elementary focus is to simulate the controller response.

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Section: Fitness Functionmentioning

confidence: 99%

Robust PID Tuning of Autonomous Underwater Vehicle Using Harmonic Search Algorithm Based on Model Order Reduction

Rathore¹,

Kumar²

2015

Int J Swarm Intel Evol Comput

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“…In order to ensure the system operating under reliable, applicable and safe conditions, we introduce the following discrete PIO controllers [2], [4]. In the discrete time domain, the PIO control law can be expressed in the following form:…”

Section: Op Timi Zation Of Pio Parameters Using Igamentioning

confidence: 99%

“…The quality of the PID controller will impact the system performance significantly, so how to obtain the parameters of the PID system is very impor tant. The traditional methods [2], [4], including Ziegler Nichols method (ZN method), the corresponding curve method, critical proportion method, and simplex method, have deficiencies in obtaining the parameters. Behind the curtain of this limitation lie some reasons.…”

Section: Introductionmentioning

confidence: 99%

“…978-1-4244-7656-5/10/$26.00 mOIO IEEE This paper studies an Improved GA (IGA) [3], [4] for the PIO controller based on the Traditional GA (TGA). The global searching ability and the convergence speed of the IGA are significantly improved by using real-coded chromosomes scheme [4], and strategies of protecting the best individual [5], accepting immigrations, as well as employing adaptive crossover and mutation operators [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Optimal PID controller design in PMSM based on Improved Genetic Algorithm

Ren

Chengyao

2010

2010 the 2nd International Conference on Industrial Mechatronics and Automation

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Ahstract-A novel PID controller design method based on Improved Genetic Algorithm is proposed in the paper for PMSM Servo system. By using real-coded chromosomes scheme, and strategies of protecting the best individual, accepting immigrations, as well as employing adaptive crossover and mutation operators, meanwhile, choose output overshoot, rise time and steady error of the system as the fitness function to weight the system performance. The global searching ability and the convergence speed of the IGA are significantly improved, and the performance of the PID controller is optimized to satisfaction. Experiment results show that the novel PID controller design method can achieve a faster Motor response, better traceability and markedly improved control results.

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“…The fitness function is used to provide a measure of how individuals have performed in the problem domain [20].The most crucial step in applying GA is to use the fitness function that is used to evaluate fitness of each chromosome.The fitness not only indicates how good the solution is but also corresponds to how close the chromosome it to the optimal one.Here in this paper integral of square of error is used as a fitness function. It is given as: …”

Section: A Fitness Functionmentioning

confidence: 99%

Robust Steering Control of Autonomous Underwater Vehicle: based on PID Tuning Evolutionary Optimization Technique

Rathore¹,

Kumar²

2015

IJCA

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This paper is devoted to a robust steering control of Autonomous Underwater Vehicle (AUV) based on tuning of PID controller using Genetic Algorithm (GA) and Harmonic Search Algorithm (HSA). Tuning of PID parameters is important because, these parameters have a great effect on the stability and performance of the control system. A harmonic Search Algorithm (HSA) technique uses to tune the PID parameters in AUV system. The HS algorithm mimics behaviors of music players in an improvisation process, in order to find a better state of harmony which can be translated into a solution vector in the optimization process.Numerical solutions based on the proposed PID control of an AUV system for nominal system parameters. In control strategies, like PID controller are succesfully designed to control the autonomous underwater vehicle. The elementary focus is to simulate the controller response.

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Design for auto-tuning PID controller based on genetic algorithms

Cited by 35 publications

References 13 publications

Robust PID Tuning of Autonomous Underwater Vehicle Using Harmonic Search Algorithm Based on Model Order Reduction

Robust PID Tuning of Autonomous Underwater Vehicle Using Harmonic Search Algorithm Based on Model Order Reduction

Optimal PID controller design in PMSM based on Improved Genetic Algorithm

Robust Steering Control of Autonomous Underwater Vehicle: based on PID Tuning Evolutionary Optimization Technique

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