Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria

A prototype of an energy-efficient MAGLEV train : a step towards cleaner train transport

The magnetic levitation (MAGLEV) train uses magnetic field to suspend, guide, and propel vehicle onto the track. The MAGLEV train provides a sustainable and cleaner solution for train transportation by significantly reducing the energy usage and greenhouse gas emissions as compared to traditional train transportation systems. In this paper, we propose an advanced control mechanism using an Arduino microcontroller that selectively energizes the electromagnets in a MAGLEV train system to provide dynamic stability and energy efficiency. We also design the prototype of an energy-efficient MAGLEV train that leverages our proposed control mechanism. In our MAGLEV train prototype, the levitation is achieved by creating a repulsive magnetic field between the train and the track using magnets mounted on the top-side of the track and bottom-side of the vehicle. The propulsion is performed by creating a repulsive magnetic field between the permanent magnets attached on the sides of the vehicle and electromagnets mounted at the center of the track using electrodynamic suspension (EDS). The electromagnets are energized via a control mechanism that is applied through an Arduino microcontroller. The Arduino microcontroller is programmed in such a way to propel and guide the vehicle onto the track by appropriate switching of the electromagnets. We use an infrared-based remote-control device for controlling the power, speed, and direction of the vehicle in both the forward and the backward direction. The proposed MAGLEV train control mechanism is novel, and according to the best of our knowledge is the first study of its kind that uses an Arduino-based microcontroller system for control mechanism. Experimental results illustrate that the designed prototype consumes only 144 W-hour (Wh) of energy as compared to a conventionally designed MAGLEV train prototype that consumes 1200 Wh. Results reveal that our proposed control mechanism and prototype model can reduce the total power consumption by 8.3 x as compared to the traditional MAGLEV train prototype, and can be applied to practical MAGLEV trains with necessary modifications. Thus, our proposed prototype and control mechanism serves as a first step towards cleaner engineering of train transportation systems

Design and Control of EMS Magnetic Levitation Train using Fuzzy MRAS and PID Controllers

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnetbased totally system that permits to levitate vertically up and down. Fuzzy logic, PID and MRAS controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A Matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria. Keywords: - Maglev train, NARMA-L2 controller, model reference controller, predictive controller DOI: 10.7176/IKM/10-4-03 Publication date:May 31st 202

Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria. Keywords: - Maglev train, NARMA-L2 controller, model reference controller, predictive controller DOI: 10.7176/CTI/10-02 Publication date:July 31st 202

Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria

Comparisons of Fuzzy MRAS and PID Controllers for EMS Maglev Train

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnet-based totally system that permits to levitate vertically up and down. Fuzzy logic, PID and MRAS controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller. Keywords: - Magnetic Levitation (MAGLEV) train, Fuzzy logic, PID, MRAS DOI: 10.7176/IKM/10-4-04 Publication date:May 31st 202

Comparisons of Fuzzy MRAS and PID Controllers for EMS Maglev Train

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnet-based totally system that permits the teach to levitate vertically up and down. Fuzzy logic, PID and Mras controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller. Index Terms:Magnetic Levitation (MAGLEV) train, Fuzzy logic, PID, MRAS DOI: 10.7176/JIEA/10-2-01 Publication date:March 31st 202

Comparisons of Fuzzy MRAS and PID Controllers for EMS Maglev Train

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a single degree of freedom electromagnet-based system that allows the train to levitate vertically up and down. Fuzzy logic, PID and Mras controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller

The dynamic simulation analysis of the coupling vibration of a maglev levitation frame and the levitation control system

To study the characteristics of the coupling vibration between the maglev vehicle and the track beam system, a new type of vibration test bench model was developed. Take the single maglev levitation frame as the study object, the structural characteristics of the maglev vehicle can be adequately considered in the research. Further more, the variable stiffness of the track beam makes the frequency conversion simulation possible. In addition, all types of real track irregularity excitations can be simulated through the test bench dynamics model. Based on the model, the influence of the track beam support stiffness, the track irregularity and the local support deficiency on the levitation control performance was determined using the dynamics simulation method, and the vibration resonance phenomenon of the vehicle/track system was reproduced by the dynamics simulation, too. The developed of the model can increase the research progress of the maglev dynamics research