Optimal FOPID Speed Control of DC Motor via Opposition-Based Hybrid Manta Ray Foraging Optimization and Simulated Annealing Algorithm

Ekinci, Serdar; İzci, Davut; Hekimoğlu, Baran

doi:10.1007/s13369-020-05050-z

Cited by 49 publications

(28 citation statements)

References 35 publications

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“…As part of the validation effort, an experimental work was also performed for this study. A previously constructed physical system (Ekinci et al, 2021; Ersali et al, 2019), shown in Figure 14, was employed for the experimental work. The hardware of the system contains a DC motor with a driver and a power supply along with a data acquisition card (DAQ) and a magnetic encoder.…”

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). In most of the application fields of DC motors, speed control is an important criterion, thus needs to be regulated with utmost accuracy (Hekimoglu, 2019) since they are operating as the main actuation devices of those dynamic systems.…”

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%

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

Self Cite

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“…There are many algorithms that emerged endlessly in the field of algorithm research. The new intelligent algorithm which combined the advantages of different algorithms has also been developed deeply [21][22][23].…”

Section: Solution Algorithmmentioning

confidence: 99%

A Joint Optimization Model of (s, S) Inventory and Supply Strategy Using an Improved PSO‐Based Algorithm

Deng

Shi

Wang

2021

Wireless Communications and Mobile Computing

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This paper mainly discussed the problem of a multiechelon and multiperiod joint policy of inventory and supply network. According to the random lead time and customers’ inventory demand, the s , S policy was improved. Based on the multiechelon supply network and the improved, the dynasty joint model was built. The supply scheme in every period with the objective of minimum total costs is obtained. Considering the complexity of the model, the improved particle swarm optimization algorithm combining the adaptive inertia weight and grading penalty function is adopted to calculate this model and optimize the spare part problems in various environments.

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“…Recently, researchers have introduced MRFO to other fields. For example, Ekinci et al [29] combined the opposition-based (OBL) with MRFO and hybrid simulated annealing algorithm to provides MRFO a better exploration capability. Shaheen et al [30] optimized the MRFO algorithm by using adaptive penalty parameters and introduced it to handle a thermal scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Duan

Liu

et al. 2021

Engineering with Computers

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The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

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Optimal FOPID Speed Control of DC Motor via Opposition-Based Hybrid Manta Ray Foraging Optimization and Simulated Annealing Algorithm

Cited by 49 publications

References 35 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

A Joint Optimization Model of (s, S) Inventory and Supply Strategy Using an Improved PSO‐Based Algorithm

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

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