Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system

Devaraj, D.; Selvabala, B.

doi:10.1049/iet-gtd.2008.0287

Cited by 83 publications

(39 citation statements)

References 7 publications

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“…where K A is in the range of [10,400], and s A is very small ranging from 0.02 s to 0.1 s. Exciter model: The transfer function of an exciter may be represented by…”

Section: Description Of Avr Systemmentioning

confidence: 99%

“…Details analysis and performance comparisons were also made. In [10], a combined genetic algorithm (GA) and fuzzy logic approach was presented to determine the optimal PID controller parameters in AVR system. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

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Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

Tang

Zhao

Han

et al. 2015

Int. J. Mach. Learn. & Cyber.

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Automatic voltage regulator (AVR) is an equipment maintaining the terminal voltage of generators to a specific level all the time and under any load conditions. Many controllers for AVR system are designed based on a linearized normal model of AVR system and they are not robust enough against uncertainties such as parameter variation and load change in the system. In this paper, a gray PID (GPID) controller is designed for AVR system. The GPID controller consists of two parts, i.e. a conventional PID controller together with a gray compensation controller. In GPID controller, gray GM (0, N) model is used to estimate the uncertainties online, and a gray compensation controller is constructed according the estimation results to eliminate the effect of uncertainties. To further improve its performance, the GPID controller's parameters are optimized through a new evolutionary algorithm, i.e., imperialist competitive algorithm (ICA). The proposed GPID controller can effectively deal with the uncertainties in AVR system. Simulation results illustrate its effectiveness of the proposed control scheme.

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“…where K A is in the range of [10,400], and s A is very small ranging from 0.02 s to 0.1 s. Exciter model: The transfer function of an exciter may be represented by…”

Section: Description Of Avr Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

Tang

Zhao

Han

et al. 2015

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

show abstract

“…Neath et al (2014) discussed about an optimal PID controller for a bidirectional inductive power transfer system using multiobjective Genetic Algorithm. Devaraj and Selvabala (2009) proposed the real coded genetic algorithm and fuzzy logic approach for real time tuning of PID controller. Seng et al (1999) tuned a Neuro-fuzzy controller by Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improving the performance indices of a dynamic system using adaptive learning controllers

Swain

Khuntia

2017

Int. j. adv. appl. sci.

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In this paper, the angle of attack of an aircraft is controlled by using soft computing techniques like Genetic Algorithm (GA), Fuzzy Model Reference Learning Controller (FMRLC) and Radial Basis Function Neural Controller (RBFNC) and the performance indices like Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Time Error (IATE) etc. of the dynamic system is improved. The result is compared with the conventional techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and Interpolation Rule (IR) for tuning the PID controller. It was established that the errors by using soft computing techniques are very less as compared to the conventional techniques thereby improving the performance indices of the dynamic system.

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“…Tuning of PID controller parameters in off line made easy with the advent of heuristic optimization techniques [5]- [7]. Genetic Algorithms and Particle swarm Optimization are very popularly used random search heuristic optimization techniques for tuning PID controller parameters [6], [8]. These techniques have very high probability to achieve global optimum solution.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents how to employ the particle swarm optimization to seek efficiently the optimal parameters of PI [8]. These techniques have very high probability to achieve global optimum solution.…”

mentioning

confidence: 99%

Robustness Study of Fractional Order PID Controller Optimized by Particle Swarm Optimization in AVR System

Raju¹,

Reddy²

2016

IJECE

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In this paper a novel design method for determining fractional order PID (PI λ D µ ) controller parameters of an AVR system using particle swarm optimization algorithm is presented. This paper presents how to employ the particle swarm optimization to seek efficiently the optimal parameters of PI [8]. These techniques have very high probability to achieve global optimum solution.Since last decade the fractional order PID controllers are widely accepted in place of integer order PID controller [9], [10]. It has been proved that fractional order PID controller has superior performance compared to integer order PID controller [9]. The problem with fractional order PID controller is that it has five parameters need to be tuned,where as integer order PID controller has three parameters need to be tuned. This makes one step tuning of fractional order PID controller difficult compared to integer order PID controller. In this paper particle swarm optimization is used to tune the PI

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Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system

Cited by 83 publications

References 7 publications

Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

Improving the performance indices of a dynamic system using adaptive learning controllers

Robustness Study of Fractional Order PID Controller Optimized by Particle Swarm Optimization in AVR System

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