Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor

Ekinci, Serdar; Hekimoğlu, Baran; İzci, Davut

doi:10.1016/j.jestch.2020.08.011

Cited by 74 publications

(70 citation statements)

References 52 publications

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“…The transient and frequency stability assessment clearly show the greater capability of the proposed LFDNM-based PID controller compared with HHO-based PID (Ekinci et al, 2020b), HGSO-based PID (Ekinci et al, 2020a), ASO-based PID (Hekimoglu, 2019), GWO-based PID (Agarwal et al, 2018a), SFS-based PID (Bhatt et al, 2019) and SCA-based PID (Agarwal et al, 2017) along with IWO and PSO-based PID (Khalilpour et al, 2011) controllers.…”

Section: Simulation Results and Discussionmentioning

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%

“…Further evaluation was performed through adopting other recently reported and best-performing algorithms-based PID controllers which have adopted the same objective function and DC motor system parameters along with the same limits of controller. The compared studies for this purpose are Harris hawks optimization (HHO)-based PID (Ekinci et al, 2020b), Henry gas solubility optimization (HGSO)-based PID (Ekinci et al, 2020a), atom search optimization (ASO)-based PID (Hekimoglu, 2019), grey wolf optimization (GWO)-based PID (Agarwal et al, 2018a), stochastic fractal search (SFS) algorithm-based PID (Bhatt et al, 2019) and SCA-based PID (Agarwal et al, 2017) along with invasive weed optimization (IWO) and PSO-based PID (Khalilpour et al, 2011) controllers. A final assessment was also carried out using a widely used time domain-based objective function proposed by Gaing (2004) in order to demonstrate the superiority of the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…In addition to this, the OHGS algorithm is capable of improving the accuracy of the best optimal gas solution. By enhancing the initialization phase and updation phase of the HGS optimization algorithm, the convergence issues are addressed (Ekinci et al, 2020).…”

Section: Ohgs Algorithmmentioning

confidence: 99%

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Ravikumar

Kavitha

2021

Journal of Field Robotics

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Numerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day-to-day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network-oppositional-based Henry gas solubility optimization (CNN-OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN-OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated.The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.

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“…DC Motor is broadly used in industrial applications such as steel rolling mills, electric cranes, robotic manipulators and electric vehicles, because of its precise, low cost, wide, simple and easiest to control [1], [2]. Generally, DC motor control system must have high performance such as load regulation response and good dynamic speed command tracking.…”

Section: Introductionmentioning

confidence: 99%

Implementing optimization of PID controller for DC motor speed control

Rashid

Hussain

2021

IJEECS

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The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.

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Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor

Cited by 74 publications

References 52 publications

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

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

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Implementing optimization of PID controller for DC motor speed control

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