A modified design of PID controller for DC motor drives using Particle Swarm Optimization PSO

El-Gammal, Adel A. A.; Elsamahy, Adel

doi:10.1109/powereng.2009.4915157

Cited by 43 publications

(16 citation statements)

References 11 publications

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“…After continuous iterations, the particle swarm will gravitate towards the optimum solution. For the ith particle and n-dimensional space can be represented as an (30), the best previous position of its particle is recorded as (31) [24][25]:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Advanced optimal by PSO-PI for DC motor

Jeaeb

Salman

Jawad

et al. 2019

IJEECS

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<p>Interest in the drive system which used in many applications is increasing day by day. So, many researchers have focused on the analyses, design and control of these systems. In this study, Optimal for Dc motor in drive system control strategy has been proposed for PSO-PI and fuzzy logic controller (FLC) based Dc motor in drive system. In order to test dynamic performance of PSO-PI based drive System, simulation study was carried out by MATLAB/Simulink. The results obtained from the PSO-PI based drive system are not only superior in the rise time, settling time and overshoot but can prevent from voltage and has improved power quality. </p>

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Advanced optimal by PSO-PI for DC motor

Jeaeb

Salman

Jawad

et al. 2019

IJEECS

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“…These systems are typically made up of a population of simple agents interacting locally with one another and also with their environment. Usually there is no centralized control structure (leader) dictating how the individual agents should behave, but local interactions between such agents lead to the emergence of a desired global behavior as pointed by El-Gammal et al [18].…”

Section: Fitness Function For Controller Tuningmentioning

confidence: 99%

FOPID controller optimization employing PSO and TRSBF function

Gupta

Gairola²

2015

2015 2nd International Conference on Recent Advances in Engineering &Amp; Computational Sciences (RAECS)

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This paper presents a technique of PID controller tuning for fractional order industrial process and proposes a Time-Response Specification Based Fitness (TRSBF) function required by Particle Swarm Optimization (PSO) for the controller tuning. The results are compared with the conventional Integral Time Absolute Error (ITAE) based and analytic controllers. The performance and robustness is analyzed in time-domain and frequency-domain to establish the suitability of proposed fitness function. MATLAB/Simulink has been employed for PSO based tuning algorithm validation and comparison of parameters like peak overshoot, settling time, disturbance rejection time, stability in frequency domain, etc.

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“…In this direction, superior performance of the PI λ D μ controller tuned with PSO over GA is reported in the literature. A technique for determining the optimal PID controller parameters using PSO and GA Algorithms, for speed control of a linear brushless DC motor is presented in .…”

Section: Introductionmentioning

confidence: 99%

Optimal fractional order PI^λD^μ controller for stabilization of cart‐inverted pendulum system: Experimental results

Mondal

Chakraborty

Dey

et al. 2019

Asian Journal of Control

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The cart-inverted pendulum is a non-minimum phase system having right half s-plane pole and zero in close vicinity to each other. Linear time invariant (LTI) classical controllers cannot achieve satisfactory loop robustness for such systems. Therefore, in the present work the fractional order PI λ D μ (FOPID) controller is addressed for robust stabilization of the system, since fractional order controller design allows more degrees of freedom compared to its integer order counterparts by virtue of its two parameters λ and μ. The controller parameters are tuned by three evolutionary optimization techniques. In order to select the controller parameters optimally, a novel non-linear fitness function using integral time square error (ITSE), settling-time, and rise time is proposed here. The control algorithm is implemented successfully in real-time. Moreover, stability analysis of the system compensated with a fractional order controller is presented using Riemann surface. Robustness of the physical cartinverted pendulum system towards multiplicative gain variations and plant parameter variations is verified. In this regard, it is shown that the fractional order controller provides satisfactory robust performance in both simulation and real-time system. KEYWORDS cart-inverted pendulum system, FOPID controller, gain margin (GM), non-minimum phase system, Riemann surface, robust stability

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A modified design of PID controller for DC motor drives using Particle Swarm Optimization PSO

Cited by 43 publications

References 11 publications

Advanced optimal by PSO-PI for DC motor

Advanced optimal by PSO-PI for DC motor

FOPID controller optimization employing PSO and TRSBF function

Optimal fractional order PI^λD^μ controller for stabilization of cart‐inverted pendulum system: Experimental results

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A modified design of PID controller for DC motor drives using Particle Swarm Optimization PSO

Cited by 43 publications

References 11 publications

Advanced optimal by PSO-PI for DC motor

Advanced optimal by PSO-PI for DC motor

FOPID controller optimization employing PSO and TRSBF function

Optimal fractional order PIλDμ controller for stabilization of cart‐inverted pendulum system: Experimental results

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Optimal fractional order PI^λD^μ controller for stabilization of cart‐inverted pendulum system: Experimental results