Experimental implementation of Flower Pollination Algorithm for speed controller of a BLDC motor

Potnuru, Devendra; Mary, K. Alice; Babu, Ch. Sai

doi:10.1016/j.asej.2018.07.005

Cited by 78 publications

(37 citation statements)

References 22 publications

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“…One of the researches has done on Flower Pollination Algorithm (FPA) for speed control of BLDC motor with optimal PID tuning [9]. In that work, the optimization-based approach is applied for tuning of PID speed controller by considering an integral square error as the objective function.…”

Section: B Fpa Based Bldc Speed Controller Benchmark Papermentioning

confidence: 99%

Modeling and Performance Analysis of an Adaptive PID Speed Controller for the BLDC Motor

Mahmud¹,

Motakabber²,

Alam³

et al. 2020

IJACSA

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Brushless Direct Current (BLDC) motor is the most popular useable motor for automation and industry. For good performance of the BLDC motor hunger driving circuit but the driving circuit is costly, complex control mechanism, various parameter dependency and low torque. The Proportional Integral (PI), Proportional Integral Derivative (PID), fuzzy logic, adaptive, Quantity Feedback Theory (QFT), Pulse Width Modulation (PWM) controller are the common types of control method existing for the BLDC motor. This research explores some well-working experiments and identified the PID controller as far more applicable controller. For well efficacious and useful in getting satisfied control performance if the adaptability is implemented. This research proposed a combined method using PID and PID auto tuner, having the ability to improve the system adaptability, given the method named as adaptive PID controller. To verify the performance, MATLAB simulation platform was used, and a benchmark system was developed based on the actual BLDC motor parameters, auxiliary systems, and mathematically solved parameters. All work has done by using MATLAB/ Simulink.

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Section: B Fpa Based Bldc Speed Controller Benchmark Papermentioning

confidence: 99%

Modeling and Performance Analysis of an Adaptive PID Speed Controller for the BLDC Motor

Mahmud¹,

Motakabber²,

Alam³

et al. 2020

IJACSA

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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%

“…The employment of direct current (DC) motors has so far been a crucial part of a variety of real-life engineering applications (Potnuru et al, 2019) because of their convenience in terms of easier controllability, higher durability and lower cost (Ekinci et al, 2020a). Electric vehicles, machining tools, robotic arms and cranes are only a few to name as their industrial applications (Ekinci et al, 2021).…”

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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“…For example [7][8][9], the Ziegler and Nichols method may provide a high order system with big overshoots, highly oscillatory and longer settling time. To solve these challenges and difficulties, various approaches have been proposed to find optimum PID parameters such as [10] meta-heuristic algorithms [11], differential evolution (DE) [12], Flower Pollination Algorithm (FPA) [13], genetic algorithms (GN) [14], Levenberg-Marquardt Algorithm (LMA) [15], Grey Wolf Optimization Algorithm (GWO) [16], Jaya optimization algorithm (JOA) [17], PSO [18,19], Improved Sine Cosine Algorithm (ISCA) [20]. These are optimization methods that have been introduced to tune the controller parameters for speed control of the DC motor.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

Mustafa

2021

EEJET

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A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in the second scenario, the four inertia weight functions have been changed but the social and personal acceleration coefficient stayed constant during the algorithm implementation. The comparison between the presented scenarios and traditional PID was carried out and satisfied simulation results have shown that the first scenario has rapid search speeds, and very effective and fast implementation compared to the second scenario and classical PSO and even improved PSO technique. Moreover, the proposed approach has a fast searching speed compared to classical PSO. However, it has been found that the classical PSO algorithm has a premature, inaccurate and local convergence process when solving complex optimization issues. The presented algorithm is proposed to increase the search speed of the original PSO.

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Experimental implementation of Flower Pollination Algorithm for speed controller of a BLDC motor

Cited by 78 publications

References 22 publications

Modeling and Performance Analysis of an Adaptive PID Speed Controller for the BLDC Motor

Modeling and Performance Analysis of an Adaptive PID Speed Controller for the BLDC Motor

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

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

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