Enhanced Speed Control of Separately Excited DC Motor Using Fuzzy-Neural Networks Controller

Soumana, Ricsa Alhassane; Saulo, Michael Juma; Muriithi, Christopher Maina

doi:10.1109/icssit53264.2022.9716556

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…High performance motor propulsion in industrial and other purpose applications such as household appliances and robotics is essential [1], [2]. Motor drives that have high performance must be able to receive dynamic commands and load control responses.…”

Section: Introductionmentioning

confidence: 99%

Sand cat swarm optimization for controlling PID in DC motor

Aribowo,

Ghith,

Rahmadian

et al. 2024

TELKOMNIKA

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In this article, the direct current (DC) motor control approach is presented using the sand cat swarm optimization (SCSO) method to obtain the best proportional integral derivative (PID) parameters. DC motors are popular equipment. In addition, DC motors are easy to apply. SCSO is a method that adopts the desert cat's life in nature when searching for prey. This cat is able to detect low frequencies below 2 kHz and also has an extraordinary ability to dig for prey. This research was carried out using the MATLAB/Simulink application. To obtain the performance of the SCSO method, a comparison method was used, namely particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and aquila optimizer (AO). From the results of the study, it was found that the settling time value and integral total weighted absolute value error (ITAE) value of the SCSO method were the best compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Sand cat swarm optimization for controlling PID in DC motor

Aribowo,

Ghith,

Rahmadian

et al. 2024

TELKOMNIKA

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“…Proportional-integral-derivative (PID) controllers are widely used to control the speed of a SEDCM above the base speed, because of their simplicity and ease of implementation. However, at high speed, the effectiveness of the PID controller tends to deteriorate, due to the significant nonlinearities observed in the motor's behavior, particularly in the weak field region [3,6,[8][9][10]. In [6], a PID controller using the artificial bee colony (ABC) algorithm was proposed, to enhance the speed control of a motor.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, researchers have been exploring various new control techniques to enhance the system's performance. Nonlinear control techniques, such as feedback linearization (FBL), sliding mode control (SMC), and backstepping control (BSC), have been proposed and applied, to control the speed of a SEDCM above base speed [3,10,14]. These controllers can handle the nonlinearities in the behavior of the motor more effectively than PID controllers, resulting in better performance in the weak field region [3,15,16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Integral Sliding Mode Control of a MIMO Separately Excited DC Motor

Afifa,

Ali,

Pervaiz

et al. 2023

Robotics

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This research proposes a robust nonlinear hybrid control approach to the speed control of a multi-input-and-multi-output separately excited DC motor (SEDCM). The motor that was under consideration experienced parametric uncertainties and load disturbances in the weak field region. The proposed technique aims to merge the benefits of adaptive backstepping (AB) and integral sliding mode control (ISMC) to enhance the overall system’s robustness. The unknown parameters with load disturbances are estimated using an adaptation law. These estimated parameters are incorporated into the controller design, to achieve a highly robust controller. The theoretical stability of the system is proved using the Lyapunov stability criteria. The effectiveness of the proposed AB–ISMC was demonstrated by simulation, to track the reference speed under parametric uncertainties and load disturbances. The control performance of the proposed technique was compared to that of feedback linearization (FBL), conventional sliding mode control (SMC), and AB control laws without and with the adaptation law. Regression parameters, such as integral square error, integral absolute error, and integral time absolute error, were calculated to quantitatively analyze the tracking performance and robustness of the implemented nonlinear control techniques. The simulation results demonstrated that the proposed controller could accurately track the reference speed and exhibited robustness, with steady-state error accuracy. Moreover, AB–ISMC overperformed, compared to the FBL, SMC, AB controller without adaptation law and AB controller with adaptation law, in reducing the settling time by factors of 27%, 67%, 23%, and 21%, respectively, thus highlighting the superior performance of the proposed controller.

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