The Experts below are selected from a list of 25689 Experts worldwide ranked by ideXlab platform

Faa-jeng Lin - One of the best experts on this subject based on the ideXlab platform.

  • fpga based elman neural network control system for linear ultrasonic motor
    IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, 2009
    Co-Authors: Faa-jeng Lin, Yingchih Hung
    Abstract:

    A field-programmable gate array (FPGA)-based Elman neural network (ENN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principle of the LUSM are introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and online learning algorithm using delta Adaptation Law of the ENN are described in detail. Then, a piecewise continuous function is adopted to replace the sigmoid function in the hidden layer of the ENN to facilitate hardware implementation. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.

  • Recurrent Radial Basis Function Network-Based Fuzzy Neural Network Control for Permanent-Magnet Linear Synchronous Motor Servo Drive
    Magnetics, IEEE Transactions on, 2006
    Co-Authors: Faa-jeng Lin, Po-Hung Shen, Song-lin Yang, Po-huan Chou
    Abstract:

    We propose a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structureand parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta Adaptation Law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties

  • a permanent magnet synchronous motor servo drive using self constructing fuzzy neural network controller
    IEEE Transactions on Energy Conversion, 2004
    Co-Authors: Faa-jeng Lin, Chihhong Lin
    Abstract:

    A self-constructing fuzzy neural network (SCFNN) is proposed to control the rotor position of a permanent-magnet synchronous motor (PMSM) drive to track periodic step and sinusoidal reference inputs in this study. The structure and the parameter learning phases are preformed concurrently and online in the SCFNN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method using a delta Adaptation Law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem under the occurrence of parameter variations and external disturbance.

  • self constructing fuzzy neural network speed controller for permanent magnet synchronous motor drive
    IEEE Transactions on Fuzzy Systems, 2001
    Co-Authors: Faa-jeng Lin, Chihhong Lin, Po-Hung Shen
    Abstract:

    A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta Adaptation Law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions.

  • sensorless induction spindle motor drive using fuzzy neural network speed controller
    Electric Power Systems Research, 2001
    Co-Authors: Faa-jeng Lin, Jyhchyang Yu, Maosheng Tzeng
    Abstract:

    Abstract A sensorless induction spindle motor drive using synchronous PWM and dead-time compensator with fuzzy neural network (FNN) speed controller is proposed in this study for advanced spindle motor applications. First, the operating principles of a new type synchronous PWM technique are described in detail. Then, a speed observer based on the model reference adaptive system (MRAS) theory is adopted to estimate the rotor speed. To increase the accuracy of the estimated speed, the speed estimation algorithm is implemented using a digital signal processor. Moreover, since the control characteristics and motor parameters for high speed operated induction spindle motor drive are time-varying, an FNN speed controller is developed to reduce the influence of parameter uncertainties and external disturbances. In addition, the FNN is trained on-line using a delta Adaptation Law. Finally, the performance of the proposed sensorless induction spindle motor drive system is demonstrated using some simulation and experimental results.

Franck Plestan - One of the best experts on this subject based on the ideXlab platform.

  • output feedback relay control in the second order sliding mode context with application to an electropneumatic system
    International Journal of Robust and Nonlinear Control, 2017
    Co-Authors: Xinming Yan, Muriel Primot, Franck Plestan
    Abstract:

    Summary In this paper, a new second-order sliding mode output feedback control Law is proposed. It amounts to approach the dynamic performance of the twisting algorithm, but the main advantage of this new control method is that it requires only the information of the sliding variable, and not its derivative. A gain Adaptation Law is also developed for this new control Law. Then this control strategy is applied to the position control of an electropneumatic system, and its performance is compared with other two very recent adaptive second-order sliding mode control Laws. Copyright © 2016 John Wiley & Sons, Ltd.

  • an adaptive solution for robust control based on integral high order sliding mode concept
    International Journal of Robust and Nonlinear Control, 2015
    Co-Authors: Mohammed Taleb, Franck Plestan, B Bououlid
    Abstract:

    Summary A new high-order sliding mode controller is proposed. The main features are gain adaptivity and the use of integral sliding mode concept. The gain Adaptation allows a reduction of the chattering and gives a solution to control uncertain nonlinear systems whose the uncertainties/perturbations have unknown bounds. The concept of real high-order sliding mode detector is introduced given that it plays a key role in the Adaptation Law of the gain. This new control approach is applied by simulation to an academic example to evaluate its efficiency. Copyright © 2014 John Wiley & Sons, Ltd.

  • an adaptive solution for robust control based on integral high order sliding mode concept
    International Journal of Robust and Nonlinear Control, 2015
    Co-Authors: Mohammed Taleb, Franck Plestan, B Bououlid
    Abstract:

    A new high-order sliding mode controller is proposed. The main features are gain adaptivity and the use of integral sliding mode concept. The gain Adaptation allows a reduction of the chattering and gives a solution to control uncertain nonlinear systems whose the uncertainties/perturbations have unknown bounds. The concept of real high-order sliding mode detector is introduced given that it plays a key role in the Adaptation Law of the gain. This new control approach is applied by simulation to an academic example to evaluate its efficiency.

B Bououlid - One of the best experts on this subject based on the ideXlab platform.

  • an adaptive solution for robust control based on integral high order sliding mode concept
    International Journal of Robust and Nonlinear Control, 2015
    Co-Authors: Mohammed Taleb, Franck Plestan, B Bououlid
    Abstract:

    Summary A new high-order sliding mode controller is proposed. The main features are gain adaptivity and the use of integral sliding mode concept. The gain Adaptation allows a reduction of the chattering and gives a solution to control uncertain nonlinear systems whose the uncertainties/perturbations have unknown bounds. The concept of real high-order sliding mode detector is introduced given that it plays a key role in the Adaptation Law of the gain. This new control approach is applied by simulation to an academic example to evaluate its efficiency. Copyright © 2014 John Wiley & Sons, Ltd.

  • an adaptive solution for robust control based on integral high order sliding mode concept
    International Journal of Robust and Nonlinear Control, 2015
    Co-Authors: Mohammed Taleb, Franck Plestan, B Bououlid
    Abstract:

    A new high-order sliding mode controller is proposed. The main features are gain adaptivity and the use of integral sliding mode concept. The gain Adaptation allows a reduction of the chattering and gives a solution to control uncertain nonlinear systems whose the uncertainties/perturbations have unknown bounds. The concept of real high-order sliding mode detector is introduced given that it plays a key role in the Adaptation Law of the gain. This new control approach is applied by simulation to an academic example to evaluate its efficiency.

Chihhong Lin - One of the best experts on this subject based on the ideXlab platform.

  • blend modified recurrent gegenbauer orthogonal polynomial neural network control for six phase copper rotor induction motor servo driven continuously variable transmission system using amended artificial bee colony optimization
    Transactions of the Institute of Measurement and Control, 2017
    Co-Authors: Chihhong Lin
    Abstract:

    Because the non-linear and time-varying characteristics of the continuously variable transmission (CVT) system driven by using a six-phase copper rotor induction motor (IM) are unknown, improving the control performance of the linear control design is time consuming. To overcome difficulties in the design of a linear controller for the six-phase copper rotor IM servo-driven CVT system with lumped non-linear load disturbances, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the non-linear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control and recompensed control. Moreover, the Adaptation Law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee c...

  • a permanent magnet synchronous motor servo drive using self constructing fuzzy neural network controller
    IEEE Transactions on Energy Conversion, 2004
    Co-Authors: Faa-jeng Lin, Chihhong Lin
    Abstract:

    A self-constructing fuzzy neural network (SCFNN) is proposed to control the rotor position of a permanent-magnet synchronous motor (PMSM) drive to track periodic step and sinusoidal reference inputs in this study. The structure and the parameter learning phases are preformed concurrently and online in the SCFNN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method using a delta Adaptation Law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem under the occurrence of parameter variations and external disturbance.

  • self constructing fuzzy neural network speed controller for permanent magnet synchronous motor drive
    IEEE Transactions on Fuzzy Systems, 2001
    Co-Authors: Faa-jeng Lin, Chihhong Lin, Po-Hung Shen
    Abstract:

    A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta Adaptation Law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions.

Po-Hung Shen - One of the best experts on this subject based on the ideXlab platform.

  • Recurrent Radial Basis Function Network-Based Fuzzy Neural Network Control for Permanent-Magnet Linear Synchronous Motor Servo Drive
    Magnetics, IEEE Transactions on, 2006
    Co-Authors: Faa-jeng Lin, Po-Hung Shen, Song-lin Yang, Po-huan Chou
    Abstract:

    We propose a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structureand parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta Adaptation Law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties

  • self constructing fuzzy neural network speed controller for permanent magnet synchronous motor drive
    IEEE Transactions on Fuzzy Systems, 2001
    Co-Authors: Faa-jeng Lin, Chihhong Lin, Po-Hung Shen
    Abstract:

    A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta Adaptation Law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions.