Optimization Algorithm Based PID Controller Design for a Magnetic Levitation System

Dey, Soham; Dey, Joykrishna; Banerjee, Subrata

doi:10.1109/calcon49167.2020.9106522

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…The equations can be proven to fulfill the stability condition as in (27) by substituting (32) into (31). The result of the substitution, which is the mathematical proof, is expressed in the equation as…”

Section: Sliding Mode Control Designmentioning

confidence: 99%

Sliding Mode Control Design for Magnetic Levitation System

Ma’arif

Márquez-Vera²,

Mahmoud³

et al. 2023

Journal of Robotics and Control

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This paper presents a control system design for a magnetic levitation system (Maglev) or MLS using sliding mode control (SMC). The MLS problem of levitating the object in the air will be solved using the controller. Inductors used in MLS make the system have nonlinear characteristics. Thus, a nonlinear controller is the most suitable control design for MLS. SMC is one of the nonlinear controllers with good robustness and can handle any model mismatch. Based on simulation results with a step as input reference, MLS provided good system performances: 0.0991s rise time, 0.1712s settling time, and 0.0159 overshoot. Moreover, a prominent tracking control for sine wave reference was also shown. Although the augmented system had a chattering effect on the control signal, the chattering control signal did not affect MLS performances.

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Section: Sliding Mode Control Designmentioning

confidence: 99%

Sliding Mode Control Design for Magnetic Levitation System

Ma’arif

Márquez-Vera²,

Mahmoud³

et al. 2023

Journal of Robotics and Control

View full text Add to dashboard Cite

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“…However, the effect of single PID control is not ideal when dealing with nonlinear system. In order to further improve the anti-interference performance and dynamic tracking performance of the MS, based on the modern optimization theories, the optimal PID controllers for the suspension air-gap tracking control have been developed using some well-known intelligent control algorithms such as bat-inspired algorithm [25], firefly algorithm [26], extremum seeking [27], NNGA [28] and particle swarm optimization [24].…”

Section: Introductionmentioning

confidence: 99%

Research on suspension control strategy based on finite control set model predictive control with state feedback control‐PID for maglev yaw system of wind turbine

Wang

Cai

Chu

et al. 2021

IET Electric Power Applications

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The maglev yaw system (MYS) of wind turbine adopts the maglev-driving technique instead of the traditional gear-driving technique, so it has the advantages of short downtime and low cost of operation and maintenance. However, it is vulnerable to the nonlinear time-varying disturbance caused by the random change of wind speed and direction. This paper proposes a dual-loop suspension control strategy based on the finite control set model predictive control (FCS-MPC) with state feedback control (SFC) to improve the dynamic response and anti-disturbance ability of the MYS. First, the mathematical models of the MYS are built. Second, in order to realise the stable suspension control, the outer loop controller for suspension air gap tracking is designed by combining SFC with proportional integral differential, and the inner loop controller for maglev current tracking is designed by adopting FCS-MPC with delay compensation. Finally, the simulation and experimental results show that the proposed control strategy has better robustness and dynamic response property comparing with the existing control strategies, and the maglev system of MYS can realise smooth and reliable operation. The proposed suspension control strategy is substantiated to be effective and feasible. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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