Adaptive control of a DC servo motor using particle swarm and gray wolf optimization algorithms

May 3, 2025

Journal 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS, AI, INFORMATION AND COMMUNICATION TECHNOLOGIES: ICMAICT2023

publisher AIP Conf. Proc

DOI https://doi.org/10.18196/jrc.v6i1.24958

Servomotors are essential in industrial position control applications, due to their feedback control system, high accuracy, simple controllability, efficiency, and position control capabilities. In this research, a method for adaptive control of a DC servomotor utilizing gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms is presented. The suggested control strategies aim to decrease (rise time, settling time, and overshooting) to enhance the DC servo engine's performance. Simulations are used to assess the efficacy of the suggested techniques, and the findings show that the (adaptive PSO and GWO-based control) approaches perform noticeably had better than conventional PID control techniques. The suggested techniques present a viable means of improving DC servo engine performance in a range of industrial applications. The most important in this paper depends on the third-order equations that deal with details specifications and parameters of the motor. These parameters will lead to an increase in the complexity of the transfer function for the motor. MATLAB software was used to validate the simulation and calculate the results values. The results show that the (APSO) are most powerful performance, where its rise time is enhanced by (97%), settling time (95%), and overshoot (85%).

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Adaptive control of a DC servo motor using particle swarm and gray wolf optimization algorithms

May 3, 2025

Journal 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS, AI, INFORMATION AND COMMUNICATION TECHNOLOGIES: ICMAICT2023

publisher AIP Conf. Proc

DOI https://doi.org/10.18196/jrc.v6i1.24958

Servomotors are essential in industrial position control applications, due to their feedback control system, high accuracy, simple controllability, efficiency, and position control capabilities. In this research, a method for adaptive control of a DC servomotor utilizing gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms is presented. The suggested control strategies aim to decrease (rise time, settling time, and overshooting) to enhance the DC servo engine's performance. Simulations are used to assess the efficacy of the suggested techniques, and the findings show that the (adaptive PSO and GWO-based control) approaches perform noticeably had better than conventional PID control techniques. The suggested techniques present a viable means of improving DC servo engine performance in a range of industrial applications. The most important in this paper depends on the third-order equations that deal with details specifications and parameters of the motor. These parameters will lead to an increase in the complexity of the transfer function for the motor. MATLAB software was used to validate the simulation and calculate the results values. The results show that the (APSO) are most powerful performance, where its rise time is enhanced by (97%), settling time (95%), and overshoot (85%).

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Performance Optimization of BLDC Motor Control Using Sand Cat Swarm Algorithm and Linear Quadratic Regulator

May 2, 2025

Journal Journal of Robotics and Control (JRC)

publisher Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia

DOI https://doi.org/10.18196/jrc.v6i1.24958

Issue No. 1 (2025)

Volume Vol. 6

Brushless Direct Current (BLDC) motors are widely utilized in industrial applications due to their precision, efficiency, and ease of control. This study optimizes BLDC motor performance by enhancing the linear quadratic regulator (LQR) using the Matlab program's Sand Cat Swarm Optimization (SCSO) algorithm. The research evaluates key performance metrics, including settling time, overshoot, and cost function, to demonstrate the advantages of the proposed approach. Additionally, a comparative analysis was conducted using the butterfly optimization algorithm (BOA) and conventional LQR to validate the superiority of SCSO. Simulation results show that the LQR-SCSO method significantly improves performance, achieving a 77.2% reduction in settling time, a 91% reduction in overshoot, and a cost function of 0.3376. In comparison, the BOA method achieves reductions of 68.54% in settling time, 67.37% in overshoot, and a cost function of 0.8736, while the conventional LQR achieves reductions of 68% in settling time, 62.3% in overshoot, and a cost function of 1.8393. SCSO has excellent convergence and adaptability; however, the implementation is explored further in terms of computational cost adopted for industrial use in real time. The data are so highly processed that better controls are implemented to repeat simulations across defined parameters. The proposed LQR-SCSO approach is practical and potent in enhancing motor performance, which is a significant advancement and can applied in various fields in the industry, such as robotics and automated systems. However, the proposed method may face obstacles related to the higher computational complexity of higher-order applications, which can be a subject of future studies.

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Design and Implementation of Model Predictive Controller

Jul 2, 2022

publisher Al-Rafidain Engineering Journal (AREJ)

DOI https://doi.org/10.33899/rengj.2022.130477.1108

Issue No.1,

Volume 27

The precise position control of a DC servo motor is a major concern in today's control theory. This work presents position following and forecast of DC servo engine utilizing an alternate control technique. Control technique is required to limit and diminish the consistent state error. A model predictive controller MPC is utilized to plan and actualize these prerequisites. Two sorts of controlling techniques are presented in this task. The Active Set Method (ASM), the inside point technique (IIP), and have been utilized as controlling strategies. This work distinguishes and depicts the plan decisions identified with a two sorts of controllers and judicious regulator for a DC servo motor. Execution of these regulators has been confirmed through reproduction utilizing MATLAB/SIMULINK programming. As indicated by the recreation results the Comparisons among ASM, IIP. The tuning strategy was increasingly proficient in improving the progression reaction attributes, for example, decreasing the rise time, settling time and most prominent overshoot in Position control of DC servo motor.

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