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

Theoretical analysis and experimental validation of a simplified fractional order controller for a magnetic levitation system

Fractional order (FO) controllers are among the emerging solutions for increasing closed-loop performance and robustness. However, they have been applied mostly to stable processes. When applied to unstable systems, the tuning technique uses the well-known frequency-domain procedures or complex genetic algorithms. This brief proposes a special type of an FO controller, as well as a novel tuning procedure, which is simple and does not involve any optimization routines. The controller parameters may be determined directly using overshoot requirements and the study of the stability of FO systems. The tuning procedure is given for the general case of a class of unstable systems with pole multiplicity. The advantage of the proposed FO controller consists in the simplicity of the tuning approach. The case study considered in this brief consists in a magnetic levitation system. The experimental results provided show that the designed controller can indeed stabilize the magnetic levitation system, as well as provide robustness to modeling uncertainties and supplementary loading conditions. For comparison purposes, a simple PID controller is also designed to point out the advantages of using the proposed FO 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

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

Model predictive control of magnetic levitation system

In this work, we suggest a technique of controller design that applied to systems based on nonlinear. We inform the sufficient conditions for the stability of closed loop system. The asymptotic stability of equilibrium and the nonlinear controller can be applied to improvement the stability of Magnetic Levitation system(MagLev). The MagLev nonlinear nodel can be obtained by state equation based on Lagrange function and Model Predictive Control has been used for MagLev system

Control for an active magnetic bearing machine with two hybrid electromagnet actuators

This thesis work begins with the revision of state of the art about active magnetic bearings (AMB), the mathematical methods used to obtain geometric and physical parameters that will influence in the mechanical, electrical design and control system proposed by this prototype. The control system will activate the magnetic bearing to center its shaft, for which it is joined a variable load in order to study the best control performance under different load over the rotor proposed by requirements. When the rotor is not controlled in its own axis even though variable load, a position error will occur that will be corrected by the program of a control system that will center the shaft (rotor). For this design was evaluated generalized AMB models [2], [3], [4] to validate the best identification for this design, furthermore as a consequence to get the best performance for the control system as it was achieved by generalized models and it was evaluated the advantage of this AMB machine through “Two hybrid electromagnet actuators” and variable load fixed to its shaft. For this reason, it was necessary to test a simple AMB with only one electromagnet actuator [4], due to compare enhancement of hybrid characteristics for the electromagnet actuators, for which, also it was evaluated how many actuators could be necessary to join to an AMB system with the target to get the control. It means, in this work there are comparisons between a simple AMB, generalized AMB models and this design, owing to show the achievements of this design. In order to show experimental results in state of the art, it is known that Siemens presented Simotics Active Magnetic Bearings technology for wear free operation in large – machine applications, regulated magnetic fields hold the rotor in suspension precisely without oil or contact, to make this task, sensors capture the position of the shaft 16000 times per second and a regulator adjusts the magnetic field to keep the rotor hovering precisely in the bearing center [1]. By other side the author [4] describes the experimental results in which is proposed that at low speed the bearing parameters are mainly determined by the controller characteristics. While at high speed, the bearing parameters are not only related to the control rule but also related to the speed. This may be due to the influence of eddying effect. [4] Furthermore, by author [3], the algorithm to get fast responses in front of disturbances, the disadvantages of these algorithms are given by not enough memory space to execute them, due to computing time is short compared with rotor displacement response time, and it is defined that it could be possible to execute the control algorithm through a real-time operating system to obtain the desired response [3]. Finally, in reference [6] it is described about filtering every noise as additive white Gaussian noise, by a predictive filter, which is obtained by analyzing Least Mean Square (LMS) and feedback/feedforward algorithm

Approximate Dual Controller by Information Matrix Maximization for Self-Sensing Electromagnetic Suspension System

The 10th International Symposium on Linear Drives for Industry Applications (LDIA 2015), Aachen, Germany, on July 27-29 2015This paper presents a design methodology to apply the approximate dual controller using the information matrix maximization for self-sensing electromagnetic suspension systems, in which the gap estimate is given based on the speed electromotive force. The system is an unstable non-minimum phase system, and we employ the dual control system. Simulations are presented to show that the dual control system follows with the reference while the electromagnet is excited to establish its quality identification for self-sensing electromagnetic levitation system

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Model Identification, Updating, and Validation of an Active Magnetic Bearing High-Speed Machining Spindle for Precision Machining Operation

High-Speed Machining (HSM) spindles equipped with Active Magnetic Bearings (AMBs) are envisioned to be capable of autonomous self-identification and performance self-optimization for stable high-speed and high quality machining operation. High-speed machining requires carefully selected parameters for reliable and optimal machining performance. For this reason, the accuracy of the spindle model in terms of physical and dynamic properties is essential to substantiate confidence in its predictive aptitude for subsequent analyses.This dissertation addresses system identification, open-loop model development and updating, and closed-loop model validation. System identification was performed in situ utilizing the existing AMB hardware. A simplified, nominal open-loop rotor model was developed based on available geometrical and material information. The nominal rotor model demonstrated poor correlation when compared with open-loop system identification data. Since considerable model error was realized, the nominal rotor model was corrected by employing optimization methodology to minimize the error of resonance and antiresonance frequencies between the modeled and experimental data.Validity of the updated open-loop model was demonstrated through successful implementation of a MIMO u-controller. Since the u-controller is generated based on the spindle model, robust levitation of the real machining spindle is achieved only when the model is of high fidelity. Spindle performance characterization was carried out at the tool location through evaluations of the dynamic stiffness as well as orbits at various rotational speeds. Updated model simulations exhibited high fidelity correspondence to experimental data confirming the predictive aptitude of the updated model. Further, a case study is presented which illustrates the improved performance of the u-controller when designed with lower uncertainty of the model\u27s accurac