Solving Mixed-Model Assembly Lines Using a Hybrid of Ant Colony Optimization and Greedy Algorithm

Al-Khazraji, Huthaifa; Khlil, Sohaib; Alabacy, Zina

doi:10.30684/etj.v40i1.2153

Cited by 2 publications

(2 citation statements)

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“…However, ACO can be sensitive to the choice of parameters, such as the pheromone evaporation rate and the balance between pheromone and heuristic information in the decision rule. Therefore, carefully tuning the parameters is often required to perform well [25,26].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

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: Ant Colony Optimization (Aco)mentioning

confidence: 99%

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

Azzawi,

Ameen,

Gitaffa

2023

JESA

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“…With the substantial improvement in the capabilities of swarm optimization algorithms such as the ability in handling multivariate, high dimensional problems and easy implementation, these algorithms have been successfully utilized in different fields of applications [9]. Among these applications, they combined with classical and modern controllers for further improvement in the performance of the controller.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control Design for Propeller Pendulum Systems Using Gorilla Troops Optimization

Ahmed,

Al-Khazraji

2023

JESA

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This study conducts a comprehensive examination of the nonlinear propeller pendulum system's angular position control, utilizing three distinct control strategies: Proportional-Integral-Derivative (PID) controller, State Feedback (SF) controller, and Sliding Mode Control (SMC). In order to optimize the performance of each controller, Gorilla Troops Optimization (GTO) is employed to identify the optimal value of the controllers' design parameters. The dynamics of the system under each controller are simulated via MATLAB software, and the performance of the controlled system is quantitatively assessed utilizing the Integral Time of Absolute Error (ITAE). The resilience of the controllers under uncertainties is evaluated by introducing an external disturbance to the system. Simulation results indicate that the SMC, tuned by GTO, exceeds the performance of the other controllers in reducing the settling time, eliminating maximum overshoot, and minimizing the ITAE index. Moreover, under external disturbance, the SMC tuned by GTO demonstrates superior robustness compared to other controllers.

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Solving Mixed-Model Assembly Lines Using a Hybrid of Ant Colony Optimization and Greedy Algorithm

Cited by 2 publications

References 0 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

Optimal Control Design for Propeller Pendulum Systems Using Gorilla Troops Optimization

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