The Experts below are selected from a list of 17094 Experts worldwide ranked by ideXlab platform

Tasawar Hayat - One of the best experts on this subject based on the ideXlab platform.

  • modeling and control with neural networks for a Magnetic Levitation system
    Neurocomputing, 2017
    Co-Authors: Jose De Jesus Rubio, Lixian Zhang, Edwin Lughofer, Panuncio Cruz, Ahmed Alsaedi, Tasawar Hayat
    Abstract:

    Abstract This study presents the model and control of the Magnetic Levitation system. The model considers the angular position of the ball, also a neural network approximates the electroMagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the Magnetic Levitation system.

Jose De Jesus Rubio - One of the best experts on this subject based on the ideXlab platform.

  • modeling and control with neural networks for a Magnetic Levitation system
    Neurocomputing, 2017
    Co-Authors: Jose De Jesus Rubio, Lixian Zhang, Edwin Lughofer, Panuncio Cruz, Ahmed Alsaedi, Tasawar Hayat
    Abstract:

    Abstract This study presents the model and control of the Magnetic Levitation system. The model considers the angular position of the ball, also a neural network approximates the electroMagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the Magnetic Levitation system.

Po-huang Shieh - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System
    IEEE Transactions on Industrial Electronics, 2007
    Co-Authors: Faa-jeng Lin, Li-tao Teng, Po-huang Shieh
    Abstract:

    An intelligent sliding-mode control system using a radial basis function network (SMCRBFN) is proposed to control the position of a levitated object of a Magnetic Levitation system to compensate the uncertainties including the friction force in this study. First, the dynamic model of the Magnetic Levitation system is derived. Then, a sliding-mode approach is proposed to compensate the uncertainties that occurred in the Magnetic Levitation system. Moreover, to relax the requirement of uncertainty bound in the design of a traditional sliding-mode control system and further increase the robustness of the Magnetic Levitation system, a radial basis function network estimator is proposed to estimate the uncertainties of the system dynamics online. The effectiveness of the proposed control scheme is verified by some experimental results. With the proposed SMCRBFN system, the position of the levitated object of the Magnetic Levitation system possesses the advantages of good transient control performance and robustness to uncertainties for tracking periodic trajectories

Ahmed Alsaedi - One of the best experts on this subject based on the ideXlab platform.

  • modeling and control with neural networks for a Magnetic Levitation system
    Neurocomputing, 2017
    Co-Authors: Jose De Jesus Rubio, Lixian Zhang, Edwin Lughofer, Panuncio Cruz, Ahmed Alsaedi, Tasawar Hayat
    Abstract:

    Abstract This study presents the model and control of the Magnetic Levitation system. The model considers the angular position of the ball, also a neural network approximates the electroMagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the Magnetic Levitation system.

Panuncio Cruz - One of the best experts on this subject based on the ideXlab platform.

  • modeling and control with neural networks for a Magnetic Levitation system
    Neurocomputing, 2017
    Co-Authors: Jose De Jesus Rubio, Lixian Zhang, Edwin Lughofer, Panuncio Cruz, Ahmed Alsaedi, Tasawar Hayat
    Abstract:

    Abstract This study presents the model and control of the Magnetic Levitation system. The model considers the angular position of the ball, also a neural network approximates the electroMagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the Magnetic Levitation system.