A Hybrid Bacterial Foraging-Particle Swarm Optimization Technique for Optimal Tuning of Proportional-Integral-Derivative Controller of a Permanent Magnet Brushless DC Motor

El-Wakeel, Amged S.; Ellissy, Abou El-Eyoun K. M.; Abdel-hamed, Alaa M.

doi:10.1080/15325008.2014.981320

Cited by 28 publications

(11 citation statements)

References 6 publications

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“…To emphasize the advantage of the proposed FA-PID method in terms of performance, the results have been compared with the existing BF-PID approach in [26] for different objectives. The efforts of the MG system shown in Figure 1 with PID controllers tuned by the BFO and FA are shown in Table 5.…”

Section: Frequency Deviation With Pid Controllers Tuned By Fa and Bfomentioning

confidence: 99%

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Optimized Control Scheme for Frequency/Power Regulation of Microgrid for Fault Tolerant Operation

Abdel-hamed

Ellissy

El-Wakeel

et al. 2016

Electric Power Components and Systems

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Section: Frequency Deviation With Pid Controllers Tuned By Fa and Bfomentioning

confidence: 99%

“…The FA-PID controllers are compared to the PID controllers tuned by bacterial foraging (BF) as discussed in [26]. According to the trials, the basic BF parameters are given in Table 3.…”

Section: Comparison With Bfomentioning

confidence: 99%

Optimized Control Scheme for Frequency/Power Regulation of Microgrid for Fault Tolerant Operation

Abdel-hamed

Ellissy

El-Wakeel

et al. 2016

Electric Power Components and Systems

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“…Compared to the BF technique, the PSO technique has improved the desired step response characteristics by minimizing the maximum overshoot, settling time and steady-state error by 0.5698 %, 0.0047 seconds and 0.0368 seconds, respectively. Reference [9] shows that A.S. El-Wakeel et al had combined these BF and PSO techniques for optimal tuning of the PID controller of a PM-BLDC motor. The best-recorded settling time is 0.5 seconds under BF-PSO tuning criteria.…”

Section: Similar Research Workmentioning

confidence: 99%

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Jayetileke

Mel

Ratnayake

2019

ijisae

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Compared to traditional motor controlling techniques, modern AI controllers have many advantages. However, most of the developed FOC mechanisms are based on classical controlling techniques such as PID controllers, hybrid AI-classical controllers and model reference controllers. All these traditional controllers are based on sophisticated mathematical models (system transfer functions). These traditional controllers are unable to tune its system parameters by itself to adapt according to the non-linear variations of actual speed and torque of the motor. This paper discussed, a novel scheme of metaheuristic adaptive fuzzy logic-particle swarm optimization control mechanism to optimize the speed regulation of electric current space vector-controlled BLDC motor. Therefore, dynamic TSK-PSO-FLC was investigated. The dynamic behaviour of the proposed controller enables it to optimize its tuning parameters by itself under non-linear load and speed varying conditions to track the desired angular speed and the torque trajectories. This is a part of the designed and developed dynamic AI controller, for stability and traction control of an all-wheel-drive electric rover. Therefore, initially, the performance of the proposed controller has been tested on a simulation environment (MATLAB Simulink model) for one wheel. Finally, the identified dynamic parameters through the Simulink model (the sensored BLDC motor and the proposed AI controller) were utilized to test the performance of the developed TSK-PSO-FLC, while it is tracking a given desired speed trajectory of the BLDC motor (sensored, 3-ph and 250 W) in real-time operation. Simulated test results are analyzed and compared with the observed test results through the developed hardware model in addition to the newly published research work. The angular speed of 2500 rpm within a 500 N m torque have been taken into consideration as a generalized condition. It was noticed that the percentage overshoot (Mp%), settling time (Ts) and the steady-state error (Ess) is 0.501, 731.455 μs and 1.22 respectively. Therefore, compared to classical control based FOC mechanisms, the analyzed test results of the proposed control mechanism is showing that it has been optimized and enhanced the speed regulation performance of the BLDC motor significantly while increasing the frequency of the desired input trajectory up to 2 kHz.

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“…The randomness in chemotaxis can be overcome by the velocity updating strategy of PSO based on global best and personal best, it improves the speed of convergence random introduction of new solutions in elimination and dispersal of BFA helps to avoid premature convergence of PSO. Amged Saeed El-Wakeel et al, [12] implemented hybrid BF-PSO algorithm by introducing velocity updating strategy after first random tumble. Faqing Zhao et al, [13] applied differential mutation to overcome tumble failure of BFA and slow convergence in chemotaxis step.…”

Section: Introductionmentioning

confidence: 99%

A hybrid bacterial foraging-particle swarm optimization technique for solving optimal reactive power dispatch problem

Reddy

Yesuratnam

2020

IJAPE

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<div data-canvas-width="397.27351844386203">This paper presents a hybrid evolutionary computation algorithm termed as hybrid bacterial foraging-particle swarm optimization (HBFPSO) algorithm, to optimal reactive power dispatch (ORPD) problem. HBFPSO algorithm merges velocity and position updating strategy of particle swarm optimization (PSO) algorithm and reproduction and elimination dispersal of bacterial foraging algorithm (BFA). The ORPD is solved for minimization of two objective functions; system real power loss and voltage stability L-index. The objective is minimized by optimally choosing the control variables; generator excitations, tap positions of on-load tap changing transformers and switched var compensators while satisfying their constraints and also the constraints of dependent variabl</div><div data-canvas-width="98.30049385204596">es; voltages of all load buses and reactive power generation of all generators. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results offered by the proposed algorithm are compared with those offered by other evolutionary computation algorithms reported in the recent state of the art literature and the superiority of the proposed algorithm is demonstrated.</div>

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A Hybrid Bacterial Foraging-Particle Swarm Optimization Technique for Optimal Tuning of Proportional-Integral-Derivative Controller of a Permanent Magnet Brushless DC Motor

Cited by 28 publications

References 6 publications

Optimized Control Scheme for Frequency/Power Regulation of Microgrid for Fault Tolerant Operation

Optimized Control Scheme for Frequency/Power Regulation of Microgrid for Fault Tolerant Operation

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

A hybrid bacterial foraging-particle swarm optimization technique for solving optimal reactive power dispatch problem

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