Speed control of brushless de motor using Ant Colony Optimization

Kouassi, Bonzou A.; Zhang, Yiming; Kiki, Mesmin J. Mbyamm; Ouattara, Sié

doi:10.1088/1755-1315/431/1/012022

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Those approaches have superior capability due to their stochastic natures. Some of the recently reported metaheuristic algorithms based tuning approaches can be listed as the stochastic fractal search algorithm (Bhatt et al, 2019; Khanam and Parmar, 2017), genetic algorithm (Hu et al, 2019), jaya optimization algorithm (Achanta and Pamula, 2017), teaching–learning based optimization (Mishra et al, 2020), flower pollination algorithm (Potnuru et al, 2019), particle swarm optimization (Qi et al, 2020), water cycle algorithm (Mohamed et al, 2020), gravitational search algorithm (Duman et al, 2011), Harris–Hawks optimization (Munagala and Jatoth, 2021), and ant colony optimization (Kouassi et al, 2020). This list can be extended for more algorithms (Eker et al, 2021; Ekinci et al, 2021a, 2021b).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

İzci

Ekinci

Zeynelgil

et al. 2021

Transactions of the Institute of Measurement and Control

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This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

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Section: Literature Reviewmentioning

confidence: 99%

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

İzci

Ekinci

Zeynelgil

et al. 2021

Transactions of the Institute of Measurement and Control

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“…However, literature review reveals that various optimization algorithms do exist to optimize any controller for solving any real-world application. A wide range of algorithms, including the genetic algorithm (GA) [13], [14] the particle swarm optimization (PSO) [15], [16], the ant colony Int J Pow Elec & Dri Syst ISSN: 2088-8694  An intelligent PID controller tuning for speed control of BLDC motor … (Hrishikesh Sarma) 2475 optimization (ACO) [17], the modified differential evolution [18], the teaching-learning-based optimization (TLBO) [19], the firefly algorithm (FA) [20], the bacterial foraging (BF) [21], the artificial bee colony optimization (ABC) [22], the simulated annealing (SA) [23], the grey wolf optimization (GWO) [24], the whale optimization algorithm (WOA) [25], the flower pollination [26], the salp swarm algorithm (SSA) [27], and the coronavirus optimization algorithm (COA) [28] have been implemented for controller tuning in achieving speed control of a BLDC motor. All of these studies have come to the conclusion that choosing an appropriate optimization algorithm is crucial for improving the control ability of any controller type for a BLDC motor.…”

Section: Introductionmentioning

confidence: 99%

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Sarma,

Bardalai

2023

IJPEDS

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Tuning of proportional-integral-derivative (PID) controller remains a matter of great concern for the control engineers as it plays a major role to obtain optimal performance of any system Due to their simplicity and excellent efficiency, metaheuristic algorithms have recently become extremely popular among researchers for handling a wide range of real-world optimization challenges. In order to optimize a PID controller for managing the speed of a BLDC motor, this work proposes a novel application of the driving training-based optimization (DTBO) algorithm, one of the latest and most recent human-based metaheuristic algorithms. The purpose of this present study is to optimize a PID controller for a BLDC motor speed control by DTBO method and evaluate its performance with a similar controller tuned by grey wolf optimization (GWO) method. Additionally, the suggested DTBO-PID controller's robustness analysis is being carried out with BLDC motor parameter modifications as well as a comparison to the GWO-PID controller. The comparison is carried out in MATLAB/Simulink, and the results are based on common step response metrics such rise time, settling time, and maximum overshoot. For easier comprehension, the results are presented in tabular and graphical form. The chosen BLDC motor drive system's selected DTBO-PID controller performs better and is more reliable than the GWO-PID controller, according to the final simulation findings.

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“…Several examples of such algorithms for PID controller design can be encountered in the literature due to this advantage. Some of them are: gravitational search algorithm (Duman et al, 2011), Harris hawks optimization algorithm (Ekinci et al, 2020b), kidney-inspired algorithm (Hekimoğlu, 2019b), flower pollination algorithm (Potnuru et al, 2019), grey wolf optimization algorithm (Bhatnagar and Gupta, 2018), invasive weed optimization algorithm (Khalilpour et al, 2011), genetic algorithm (El-Deen et al, 2015), stochastic fractal search algorithm (Khanam and Parmar, 2017), teaching–learning-based optimization (Mishra et al, 2020), ant colony optimization (Kouassi et al, 2020), swarm learning process (Pongfai et al, 2020), particle swarm optimization (Sabir and Khan, 2014), water cycle algorithm (Mohamed et al, 2020), sine cosine algorithm (Agarwal et al, 2018b) and its improved version (Ekinci et al, 2019) along with slime mould algorithm (Izci and Ekinci, 2021) and hybrid atom search optimization with simulated annealing algorithm (Eker et al, 2021). Further improvement on PID controller design can still be achieved despite the promising results obtained by the abovementioned algorithms since there is a dizzying effort in terms of development of new metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

İzci

2021

Transactions of the Institute of Measurement and Control

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This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

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Speed control of brushless de motor using Ant Colony Optimization

Cited by 7 publications

References 5 publications

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

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