Identification and Speed Control of DC Motor Using Fractional Order PID: Microcontroller

El‐Saadawi, Magdi M.; Gouda, Eid Abdelbaqi; Elhosseini, Mostafa A.; Essa, Mohamed Said

doi:10.24018/ejece.2020.4.1.170

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…we = P 𝑤 𝑟 (7) where, λq = 𝐿 𝑞 𝑖 𝑞 (8) λd = 𝐿 𝑑 𝑖 𝑑 + λ 𝑎𝑓 (9) Currents along the d and q axes are denoted by id and iq, whereas voltages along those axes are written as Vd and Vq. The pole pair count is represented by P; The inductances Lq, Ld are on the q and d axes; p is the derivative operator; TL., Te are the load and electric torques; The damping coefficient is denoted by B; The inertial moment is characterised by J, and the mutual flux or airgap flux is represented by λaf.…”

Section: Mathematical Modeling Of Pmsm Motormentioning

confidence: 99%

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Comparative Performance Evaluation of Swarm Intelligence-Based FOPID Controllers for PMSM Speed Control

Azzawi,

Ameen,

Gitaffa

2023

JESA

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This paper presents a study focused on the design and performance evaluation of Fractional-Order Proportional-Integral-Derivative (FOPID) controllers for the speed control of Permanent Magnet Synchronous Motors (PMSMs). Effective speed control of PMSMs is of great importance in various applications such as robotics, electric vehicles, and industrial automation. However, achieving precise and efficient speed control poses several challenges due to the nonlinear and time-varying nature of PMSMs. To address these challenges, the study proposes the utilization of FOPID controllers, which offer advantages over traditional PID controllers, including improved robustness and greater flexibility in handling complex system dynamics. Additionally, the study explores the use of Swarm Intelligence (S.I.) algorithms for the design and tuning of FOPID controllers. Swarm Intelligence algorithms, such as Particle Swarm Optimization (PSO), ant colony optimization (ACO), and Grey Wolf Optimization (GWO), are known for their ability to effectively search and optimize complex parameter spaces. The main contribution of this work is the comparison and evaluation of PSO, GWO, and ACO algorithms for the design of FOPID controllers in PMSM speed control applications. The controllers are assessed through both simulations and experimental tests to analyze their performance in terms of speed-tracking accuracy, overshoot, and settling time. The key finding of the study is that the ACO-FOPID controller exhibits the best performance in terms of transient response. It achieves a rise time of 0.008978 s, a settling time of 0.01 s, and zero absolute time error (ITAE). These results indicate that the ACO-FOPID controller provides precise and fast speed control for PMSMs, making it a promising solution for practical applications. In summary, this study highlights the importance of PMSM speed control and the challenges associated with it. It introduces the FOPID controller as a potential solution and motivates the utilization of Swarm Intelligence algorithms for its design. The comparison of PSO, GWO, and ACO algorithms for FOPID controller design demonstrates the superiority of the ACO-FOPID controller in terms of transient response. This research contributes to the advancement of control systems for PMSMs and showcases the potential of Swarm Intelligence algorithms in optimizing complex control parameters.

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Section: Mathematical Modeling Of Pmsm Motormentioning

confidence: 99%

“…(5) El-Saadawi et al [9] introduced the Gray Wolf Optimization (GWO/FOPID) method for speed control of a D.C. motor. The Nelder-Mead (NM) algorithm-based FOPID approach yielded the best results, achieving zero overshoot with low settling and rising times.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Evaluation of Swarm Intelligence-Based FOPID Controllers for PMSM Speed Control

Azzawi,

Ameen,

Gitaffa

2023

JESA

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“…PID parameters are set using different methods, including traditional and modern ones, such as partial gray wolf optimization (PGWO), good motor performance, high precision, high speed and better response than traditional methods were obtained, the corresponding rising time, settling time, and maximum overshoot. This paper presents a study of using PID-GWO optimization to control SM which is used in road printers and compares these results with conventional PID controllers to improve SM performance [13].…”

Section: Introductionmentioning

confidence: 99%

Simulation and modeling for controlling stepper motor with tuned PID by GWO: comparative study

Shneen,

Dakheel,

Abdullah

2024

IJAAS

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The current work aims to simulate the operation of the electric motor in one of the most important industrial applications, which is printers, by adopting stepper motors (SM). The performance of the motor is also improved by adopting traditional control systems and adjusting them using the gray wolf optimization (GWO) advanced algorithm. It works to adjust the parameters of a conventional controller. Simulation to reach an appropriate design with high performance, which is obtained by adopting the integral time absolute error (ITAE) function to get rid of the error for transient cases. Transfer function was adopted to represent the engine and two methods of control were used, traditional and advanced optimization. Results demonstrated the possibility of improving performance by adopting both methods with a clear superiority of advanced optimization. Response of SM without controller for close loop shows the values of each rising time equal 130.440 ms, overshoot equal 0.505%, and undershoot equal 1.077%. Response of SM for close loop with proportional-integral-derivative controllers (PIDC) shows the parameters, performance, and robustness of PIDC also the values of overshoot=9.16%, settling time=0.406, and rise time=0.0628 s. Results were developed by using GWO-PID over the previous cases by reducing values of overshoot to zero, rise time, and settling time to 0.00145 and 0.0027 respectively.

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“…The results showed 0.001 percent error, 0.1455 sec settling time, 0.91 percent for MP and 0.0889 sec rising time [9]. El-Saadawi et al [10] proposed Gray Wolf Optimization (GWO/FOPID), tuning of Single-loop PID, Nelder-Mead (NM/FOPID) and Genetic Algorithm (GA) to regulate the speed of a DC motor. It is determined that the NM algorithm-based FOPID provides the optimum response.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Performance Analysis of Fractional Order PID Controller for DC Motor Speed Control

Ibrahim¹,

Issa²,

Gitaffa³

2022

JESA

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Multi-Agent System (MAS) technology is one of the cores and promising areas in the field of Artificial Intelligence (AI) as well as in the stream of Computer Science. The technology is comprised of multiple decision-making agents that exist in an environment to achieve common or conflicting goals. Multi-Agent System technology has a rapid growth and evolution due to its marvelous features such as flexibility and intelligence that are very useful when solving complex distributed problems. This paper focuses on the history and evolution of MAS technology, present applications, and future trends by addressing more detailed explanations about the foundations or key principles of Multi-Agent System technology such as agents, agent taxonomy, agent communication approaches, MAS development frameworks as well as the history of agent technology. The goal of this paper is to provide broad and comprehensive knowledge about Multi-Agents System technology.

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Identification and Speed Control of DC Motor Using Fractional Order PID: Microcontroller

Cited by 6 publications

References 16 publications

Comparative Performance Evaluation of Swarm Intelligence-Based FOPID Controllers for PMSM Speed Control

Comparative Performance Evaluation of Swarm Intelligence-Based FOPID Controllers for PMSM Speed Control

Simulation and modeling for controlling stepper motor with tuned PID by GWO: comparative study

Optimization and Performance Analysis of Fractional Order PID Controller for DC Motor Speed Control

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