Assessment of Slime Mould Algorithm Based Real PID Plus Second-order Derivative Controller for Magnetic Levitation System

İzci, Davut; Ekinci, Serdar; Eker, Erdal; Dundar, Ahmet

doi:10.1109/ismsit52890.2021.9604620

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…This is due to the risk of amplifying control signals with sensor noise, which can negatively impact system performance. To mitigate this issue, a low-pass filter can be added to the derivative term, resulting in the transfer function of the RPIDD 2 controller as shown in the following equation [ 67 ]: where k p 1 , k i 1 , k d 1 , and k d 2 denote proportional, integral, derivative, and second-order derivative gains, respectively. n 1 and n 2 represent the filter coefficients.…”

Section: New Methodology For Transient Stability Enhancementmentioning

confidence: 99%

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Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

Ekinci,

Snášel,

Rizk-Allah

et al. 2024

PLoS ONE

Self Cite

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Maintaining stable voltage levels is essential for power systems’ efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO’s effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO’s superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm’s performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO’s superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

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Section: New Methodology For Transient Stability Enhancementmentioning

confidence: 99%

“…This is due to the risk of amplifying control signals with sensor noise, which can negatively impact system performance. To mitigate this issue, a lowpass filter can be added to the derivative term, resulting in the transfer function of the RPIDD 2 controller as shown in the following equation [67]:…”

Section: Proposed Novel Cascaded Rpidd 2 -Fopi Controllermentioning

confidence: 99%

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

Ekinci,

Snášel,

Rizk-Allah

et al. 2024

PLoS ONE

Self Cite

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“…As the maglev system is naturally unstable, electronic feedback control is necessary to stabilise the levitated magnetic item. In [28], the authors proposed the PIDD 2 -PID controller for a magnetic levitation system, which uses the slime mould algorithm to tune its control parameters. The simulation results of this design were compared with other controllers like ASO-FOPID, AEF-FOPID, ABC-FOPID, SCA-PID, WDO-PID, and ABC-PID controllers, and it outperformed them in terms of performance measures.…”

Section: Control System Applicationsmentioning

confidence: 99%

Design of PIDDα Controller for Robust Performance of Process Plants

Fawwaz,

Bingi,

Ibrahim

et al. 2023

Algorithms

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Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDDα that incorporates the second derivative term from PIDD2 while exclusively using fractional order parameters for the second derivative term. The controllers’ robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers’ performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD2 controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDDα outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal.

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“…Moreover, tuning the gains of the proposed controller's for two-legged robots were highly researched through the recent years, such that the authors in [12] present particle swarm optimization to tune the parameters of the PID controller, in [13], the authors proposed a hybrid salp swarm algorithm with grey wolf optimizer algorithm (HSSGWOA) to tune the integral sliding mode controller (ISMC), in [14], the authors present a hybrid dragonfly optimization and differential evolution algorithm for obstacle avoidance trajectory planning problem, in [15], the authors introduce a modified chaotic inverse weed optimization (MCIWO) for training the neural network with PID controller, in [16] the authors proposed an improved slime mould algorithm (ISMA) to adjust the parameters of FOPID controllers, while the authors in [17] present a slime mould algorithm to tune a magnetic levitation system.…”

Section: Introductionmentioning

confidence: 99%

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Wassef

Hadi

2023

Eng. rev. (Online)

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In this paper, an adaptive neuro-fuzzy based on fractional-order proportional-integral-derivative (ANFFOPID) controller with an improved slime mould algorithm (ISMA) for the two-legged robot (TLR) is proposed to achieve the minimum angular displacement error of the joint motors. Achieving such error is considered a challenging and time-consuming process due to the gain values set for the FOPID controller. Thus the neural-fuzzy network is used to provide the FOPID input signals by the adaptive magnitude gains. The adaptive mechanism depends on the ISMA to train the neural network weights. The outstanding properties of the ANFFOPID controller are evaluated by comparing the proposed controller with other existing work that is modified chaotic invasive weed optimization based on neural network (MCIWO-NN) for various walking terrains that are flat surface, stair ascending, and stair descending. Finally, the results obtained show the effectiveness of the ANFFOPID controller.

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Assessment of Slime Mould Algorithm Based Real PID Plus Second-order Derivative Controller for Magnetic Levitation System

Cited by 6 publications

References 14 publications

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

Design of PIDDα Controller for Robust Performance of Process Plants

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

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