Parameters Identification of a Permanent Magnet DC Motor: A Review

Fazdi, Mohamad Farid; Hsueh, Po-Wen

doi:10.3390/electronics12122559

Cited by 9 publications

(4 citation statements)

References 91 publications

(127 reference statements)

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“…In order to conduct this evaluation, two case studies were selected: a DC motor (Direct Current) and a hydraulic actuator. Both are components widely found in various industrial contexts and, as highlighted in the literature, present significant challenges in terms of maintenance and control due to the variety of failures and operational conditions they can face [ 27 , 46 , 47 , 48 ].…”

Section: Simulation and Resultsmentioning

confidence: 99%

An Interpretable Digital Twin for Self-Aware Industrial Machines

Vilar-Dias,

Junior,

Lima-Neto

2023

Sensors

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This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system. Our methodology, as exemplified through DC Motor and Hydraulic Actuator simulations, underscores the potential of Digital Twins to augment the self-awareness of industrial machines. The results indicate that our approach can proficiently optimize system parameters in real-time and unveil previously unknown components, thereby enhancing the adaptive capacities of the Digital Twin. While the reliance on accurate data to develop Digital Twin models is a notable consideration, the proposed methodology serves as a promising framework for advancing the efficiency of industrial applications. It further extends its relevance to fault detection and system control. Central to our approach is the emphasis on interpretability, ensuring a more transparent understanding and effective usability of such systems.

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Section: Simulation and Resultsmentioning

confidence: 99%

An Interpretable Digital Twin for Self-Aware Industrial Machines

Vilar-Dias,

Junior,

Lima-Neto

2023

Sensors

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“…The author of [34] proposes that this behavior can be simulated by decreasing the value ⃗ A, a random value in an interval of [−2a, 2a], where a is reduced from 2 to 0 throughout iterations. When the arbitrary values of ⃗ A are at [1,1], the next position of search agent "a" can be anywhere between its current situation and the position of the dam. With the hunting phases explained so far, the algorithm offers good search agents that converge to attack the dam (optimal solution).…”

Section: Attacking Preymentioning

confidence: 99%

“…A direct current (DC) motor has many applications in the industrial sector [1], such as aeronautics, robotics, automation, manufacturing, automotive, etc. Therefore, it is constantly subjected to multiple experiments or investigations in identification, estimation, and parametric control to create solutions in different areas of research that could have applications in real life.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

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Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used.

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“…Its strength lies in its powerful ability to conduct global searches, resulting in a widespread implementation in various fields. The DE algorithm has the advantage of a strong global search capability and is suitable for parallel computation, but the local search capability is limited [36,37]. Chen et al verified the advantages of the DE algorithm for solving complex problems in group work, offering the possibility of combin-ing it with the GWO algorithm [38].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Chen,

Zhang,

Wang

et al. 2023

Electronics

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For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

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Parameters Identification of a Permanent Magnet DC Motor: A Review

Cited by 9 publications

References 91 publications

An Interpretable Digital Twin for Self-Aware Industrial Machines

An Interpretable Digital Twin for Self-Aware Industrial Machines

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

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