Design and implementation of Fractional Order PID controller for aerofin control system

Kadiyala, Venu Kishore; Jatoth, Ravi Kumar; Pothalaiah, Sake

doi:10.1109/nabic.2009.5393470

Cited by 15 publications

(3 citation statements)

References 7 publications

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“…In fractional order based PID control, GA has been combined with particle swarm optimization (PSO) for efficient control [26] [27][28] [29]. Applications include aero fin control using this hybrid approach to minimize rise time, peak overshoot and peak time of the response [30]. Fractional order PID control has also been used to control an unmanned aerial vehicle with good disturbance handling; PSO imparting robustness to the designed controller [31].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm based Fractional Fuzzy PID Controller for Integer and Fractional order Systems

Kumar¹,

Sharma²

2018

IJISA

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This work aims at designing a fractional Proportional-Integral-Derivative controller wherein we hybridize a genetic algorithm based fractional Proportional-Integral-Derivative controller with a fuzzy logic Proportional-Integral-Derivative controller. We attempt at optimizing the fractional order Proportional-Integral-Derivative controller parameters by incorporating a Genetic Algorithm based mechanism. Thereafter, the optimized genetic algorithm based fractional Proportional-Integral-Derivative control is further fine tuned and hybridized to a fuzzy Proportional-Integral-Derivative control. Here, fuzzy logic based inference mechanism is used to tackle system uncertainties and use of rule firing strengths produces an adaptive control. Genetic Algorithm has been used to generate the most optimal controller by a natural selection of the fittest. Amalgamating Genetic Algorithm and fuzzy control approaches on fractional order systems produces a highly efficient and noise tolerant control regime. We give simulation results and compare our hybrid approach against conventional and fractional Proportional-Integral-Derivative approaches on various integer and fractional order systems (with dead time) to elucidate its superiority and effectiveness.

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Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm based Fractional Fuzzy PID Controller for Integer and Fractional order Systems

Kumar¹,

Sharma²

2018

IJISA

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“…In last few decades, the FOPID has many areas of applications in engineering and science [1][2][3][4][5]. The concept of fractional order was firstly introduced by Pdlubny in 1997 [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Robust Fractional Order PID for the Flight Control System

Kumar¹,

Narayan²,

Raheja³

2015

IJCA

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Nature inspired algorithms are the most popular and robust algorithms for the optimization of the real world problems like pitch control of an aircraft system. This paper introduces Bat algorithm and Differential Evolution technique for the multi-objective optimization based designing of the fractional order PID (FOPID) and integer order PID controllers. The optimized values obtained from the techniques have been implemented for the Pitch control of an aircraft system to obtain the desired robust response. In this paper a mixed sensitivity H ∞ problem is designed and simulated using Matlab. It has been shown that the design of FOPID using multi-objective bat algorithm gives better results than others.

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“…An improved performance has already been observed with the incorporation of fractional order controllers in many engineering problems such as speed control of DC motor [14], temperature profile tracking [15], Aerofin control system [16] and in robotics [11]. A few successful implementations have also been found in automatic voltage regulator systems [17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimal fractional order PID controller for automatic generation control of two-area power systems

Chathoth

Kumar

Sindhu

2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper proposes a fractional order PID (FOPID) controller for the supplementary automatic generation control (AGC) of two area thermal power systems. To establish the effectiveness of the proposed controller, its dynamic performance is compared with integral/PI controllers. The parameters of the PI controllers are determined using genetic algorithm (GA) and fuzzy logic‐based approaches. These comparisons are performed in terms of two time domain performance indices viz., settling time and maximum overshoot/undershoot of the dynamic response and three error indices, viz., Integral of Time multiplied Absolute Error (ITAE), Integral of the Absolute Error (IAE) and Integral of the square error (ISE). Copyright © 2014 John Wiley & Sons, Ltd.

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Design and implementation of Fractional Order PID controller for aerofin control system

Cited by 15 publications

References 7 publications

A Genetic Algorithm based Fractional Fuzzy PID Controller for Integer and Fractional order Systems

A Genetic Algorithm based Fractional Fuzzy PID Controller for Integer and Fractional order Systems

Optimal Design of Robust Fractional Order PID for the Flight Control System

Optimal fractional order PID controller for automatic generation control of two-area power systems

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