A real-time control of maglev system using neural networks and genetic algorithms

Daghooghi, Z.; Menhaj, Mohammad Bagher; Zomorodian, Artin; Akramizadeh, Ali

doi:10.1109/icit.2012.6209992

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…• In order to achieve the stability of the controlled object in the EMFDS, the linear term must be contained in its characteristic function; that is, a differential link must be added to the control model. Neural network algorithm has good control and processing ability for nonlinear complex systems [3]. The algorithm can track the system response quickly.…”

Section: Control Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Liu,

Jiang,

Fan

et al. 2024

J. Phys.: Conf. Ser.

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The neural network PID control method of the external magnetic field driving system solves the shortcomings of the traditional control method. The controlled object is a spherical permanent magnet. Other detections or operation equipment can be attached to the sphere. In the process of external magnetic field driving, the model-based controller is prone to large fluctuations when the environmental parameters change, which reduces the control performance. The neural network algorithm has good control ability for the highly nonlinear magnetic levitation driving system. PID control system combined with a neural network algorithm has high stability and strong adaptability in the driving process. The test results show that the response output overshoot is 9.322%, and the system fluctuation is low. Self-tuning and adaptive environment changes of PID controller parameters can be realized by neural network control.

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Section: Control Methodsmentioning

confidence: 99%

“…Because of their good adaptability, neural networks have been frequently and widely used in various fields, and good control effects have been achieved. Mohammad Bagher Menhaj et al [3] of Amir Kabir University proposed a new magnetic levitation system control method. The identifier structure is modeled by a feed-forward neural network.…”

Section: Introductionmentioning

confidence: 99%

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Liu,

Jiang,

Fan

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…It is often used to solve nonlinear systems with uncertainties, which is its area of expertise. Because neural network has good adaptability to complex systems with nonlinear and ambiguous models [22,23], it has been widely used in the field of magnetic levitation technology [24,25].…”

Section: Control Methodsmentioning

confidence: 99%

Magnetic Levitation Actuation and Motion Control System with Active Levitation Mode Based on Force Imbalance

Liu

Lü

et al. 2023

Applied Sciences

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Accurate large-displacement magnetic levitation actuation and its stability remain difficult in non-liquid environments. A magnetic levitation actuation and motion control system with active levitation mode is proposed in this paper. The actuating force of the system is generated by the external magnetic field. A neural network proportion-integration-differentiation (PID) controller is designed for active actuation, and a force imbalance principle is built for the step motion mode. Dual electromagnetic actuators are configured to generate a superimposed magnetic field, ensuring that the electromagnetic force on the ball is more uniform and stable than single actuators. Dual-hall-structure sensors are used to measure displacement, thereby reducing overshoot and ensuring stability whilst motivating the ball. Due to the high adaptability of the neural network to complex systems with nonlinear and ambiguous models, the PID controller composed of neurons has stronger adaptability through tuning the PID controller parameters automatically. Furthermore, the proposed controller can solve the shortcoming that the deviation between the controlled object and the steady-state operating point increases and the tracking performance deteriorates rapidly. The strong robustness and stability in active levitation and motion control is achieved during both ascending and descending processes.

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“…[30] presents an adaptive neural‐modulus and SMC, which can effectively reduce the effects of disturbances and parameter changes of MS. A suspension control method based on NNGA is proposed in Ref. [31], in which the structure of identification is modelled by multilayer feedforward neural network, and the parameters of neural network are optimised by GA. A robust H ∞ controller is designed in Ref. [32], the nonlinear model and controller are synthesised by using parallel distributed compensation (PDC) technique, and the sufficient conditions are given to guarantee the robustness of the complex nonlinear system and improve the anti‐interference ability of the MS.…”

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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A real-time control of maglev system using neural networks and genetic algorithms

Cited by 5 publications

References 7 publications

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Magnetic Levitation Actuation and Motion Control System with Active Levitation Mode Based on Force Imbalance

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

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