The Experts below are selected from a list of 20355 Experts worldwide ranked by ideXlab platform

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

  • a permanent magnet synchronous motor servo drive using self constructing fuzzy Neural Network Controller
    IEEE Transactions on Energy Conversion, 2004
    Co-Authors: Faajeng 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.

  • a supervisory fuzzy Neural Network Controller for slider crank mechanism
    Mechatronics, 2001
    Co-Authors: Faajeng Lin, Rongfong Fung, Hsinhai Lin, Chihming Hong
    Abstract:

    A supervisory fuzzy Neural Network (FNN) Controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position Controller. The supervisory FNN Controller comprises a sliding mode FNN Controller and a supervisory Controller. The sliding mode FNN Controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory Controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN Controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.

  • a fuzzy Neural Network Controller for parallel resonant ultrasonic motor drive
    IEEE Transactions on Industrial Electronics, 1998
    Co-Authors: Faajeng Lin, Rongjong Wai, Shenglong Wang
    Abstract:

    A newly designed driving circuit for the traveling-wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a current-source two-phase parallel-resonant inverter, is presented in this study. Moreover, since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a fuzzy Neural Network (NN) Controller is proposed to control the USM drive system. In the proposed Controller, a fuzzy model-following Controller is implemented to control the rotor position of the USM, and an online trained NN with variable learning rates is implemented to tune the output scaling factor of the fuzzy Controller. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the desired variable learning rates. From the experimental results, accurate tracking response can be obtained by the proposed Controller, and the influences of parameter variations and external disturbances on the USM drive also can be reduced effectively.

  • a pm synchronous servo motor drive with an on line trained fuzzy Neural Network Controller
    IEEE Transactions on Energy Conversion, 1998
    Co-Authors: Faajeng Lin, Rongjong Wai, Hongpong Chen
    Abstract:

    A permanent magnet (PM) synchronous servo motor drive with integral-proportional (IP) position Controller and a proposed on-line trained fuzzy Neural Network (FNN) Controller is introduced in this paper. First, an IP position Controller is designed according to the estimated plant model to match the time-domain command tracking specifications. Then the resulting closed-loop tracking transfer function is used as the reference model, and an adaptive signal generated from the proposed FNN Controller, whose membership functions and connective weights are trained on-line according to the model-following error of the states, is added to the control system to preserve a favorable model-following characteristics under various operating conditions.

  • a fuzzy Neural Network Controller with adaptive learning rates for nonlinear slider crank mechanism
    Neurocomputing, 1998
    Co-Authors: Rongjong Wai, Faajeng Lin
    Abstract:

    A fuzzy Neural Network (FNN) Controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the Network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN Controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN Controller with adaptive learning rates is demonstrated by simulation and experimental results.

Seul Jung - One of the best experts on this subject based on the ideXlab platform.

  • hardware implementation of a real time Neural Network Controller with a dsp and an fpga for nonlinear systems
    IEEE Transactions on Industrial Electronics, 2007
    Co-Authors: Seul Jung, Sungsu Kim
    Abstract:

    In this paper, we implement the intelligent Neural Network Controller hardware with a field programmable gate array (FPGA)-based general purpose chip and a digital signal processing (DSP) board to solve nonlinear system control problems. The designed intelligent control hardware can perform real-time control of the backpropagation learning algorithm of a Neural Network. The basic proportional-integral-derivative (PID) control algorithms are implemented in an FPGA chip and a Neural Network Controller is implemented in a DSP board. By using a high capacity of an FPGA chip, the additional hardware such as an encoder counter and a pulsewidth modulation (PWM) generator is implemented in a single FPGA chip. As a result, the Controller becomes cost effective. It was tested for controlling nonlinear systems such as a robot finger and an inverted pendulum on a moving cart to show performance of the Controller

  • experimental studies of a fuzzy Controller compensated by Neural Network for humanoid robot arms
    Journal of Institute of Control Robotics and Systems, 2007
    Co-Authors: Deokhui Song, Jinseok Noh, Seul Jung
    Abstract:

    In this paper, a novel neuro-fuzzy Controller is presented. The generic fuzzy Controller is compensated by a Neural Network Controller so that an overall control structure forms a neuro-fuzzy Controller. The proposed neuro-fuzzy Controller solves the difficulty of selecting optimal fuzzy rules by providing the similar effect of modifying fuzzy rules simply by changing crisp input values. The performance of the proposed Controller is tested by controlling humanoid robot arms. The humanoid robot arm is analyzed and implemented. Experimental studies have shown that the performance of the proposed Controller is better than that of a PID Controller and of a generic fuzzy PD Controller.

  • Hardware implementation of a real time Neural Network Controller with a DSP and an FPGA
    Proceedings of the 2004 IEEE International Conference on Robotics & Automation, 2004
    Co-Authors: Sung-su Kim, Seul Jung
    Abstract:

    In this paper, we implement the intelligent Controller hardware such as a Neural Network Controller with an FPGA based general purpose Controller and a DSP board to solve nonlinear control problems. The designed control hardware can perform a real time control of the backpropagation learning algorithm of a Neural Network. The basic PID control algorithms are implemented in an FPGA chip and a Neural Network Controller is implemented in a DSP board. By using high capacity of an FPGA, the additional hardware such as an encoder counter and a PWM generator can be implemented in a single FPGA device. As a result, the Controller is very cost effective. In order to show the performance of the Controller, it was tested for controlling nonlinear systems such as an inverted pendulum.

Frank L Lewis - One of the best experts on this subject based on the ideXlab platform.

  • design and implementation of industrial Neural Network Controller using backstepping
    IEEE Transactions on Industrial Electronics, 2003
    Co-Authors: Ognjen Kuljaca, Frank L Lewis, N Swamy, Chiman Kwan
    Abstract:

    In this paper, a novel Neural Network (NN) backstepping Controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping Controller is implemented on an actual motor drive system using a two-PC control system developed at The University of Texas at Arlington. The implementation results show that the NN backstepping Controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN Controller gives better results on actual systems than a standard backstepping Controller developed assuming full knowledge of the dynamics. Moreover, the NN Controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.

  • design and implementation of industrial Neural Network Controller using backstepping
    Conference on Decision and Control, 2001
    Co-Authors: Ognjen Kuljaca, Frank L Lewis, N Swamy, Chiman Kwan
    Abstract:

    A novel Neural Network (NN) backstepping Controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee the stability and performance of the closed-loop system. The NN backstepping Controller is implemented on an actual motor drive system using a two-PC control system developed at the authors' university. The implementation results show that the NN backstepping Controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN Controller gives better results on actual systems than a standard backstepping Controller developed assuming full knowledge of the dynamics. Moreover, the NN Controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.

  • optimal design of cmac Neural Network Controller for robot manipulators
    Systems Man and Cybernetics, 2000
    Co-Authors: Frank L Lewis
    Abstract:

    This paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using cerebellar model arithmetic computer (CMAC) Neural Networks. Explicit solutions to the Hamilton-Jacobi-Bellman (H-J-B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how the CMAC can cope with nonlinearities through optimization with no preliminary off-line learning phase required. The adaptive-learning algorithm is derived from Lyapunov stability analysis, so that both system-tracking stability and error convergence can be guaranteed in the closed-loop system. The filtered-tracking error or critic gain and the Lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic-performance index. Simulation results from a two-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances.

  • optimal design of cmac Neural Network Controller for robot manipulators
    Systems Man and Cybernetics, 2000
    Co-Authors: Y H Kim, Frank L Lewis
    Abstract:

    This paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using cerebellar model arithmetic computer (CMAC) Neural Networks. Explicit solutions to the Hamilton-Jacobi-Bellman (H-J-B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how the CMAC can cope with nonlinearities through optimization with no preliminary off-line learning phase required. The adaptive-learning algorithm is derived from Lyapunov stability analysis, so that both system-tracking stability and error convergence can be guaranteed in the closed-loop system. The filtered-tracking error or critic gain and the Lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic-performance index. Simulation results from a two-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances.

Rongjong Wai - One of the best experts on this subject based on the ideXlab platform.

  • a pm synchronous servo motor drive with an on line trained fuzzy Neural Network Controller
    IEEE Transactions on Energy Conversion, 1998
    Co-Authors: Faajeng Lin, Rongjong Wai, Hongpong Chen
    Abstract:

    A permanent magnet (PM) synchronous servo motor drive with integral-proportional (IP) position Controller and a proposed on-line trained fuzzy Neural Network (FNN) Controller is introduced in this paper. First, an IP position Controller is designed according to the estimated plant model to match the time-domain command tracking specifications. Then the resulting closed-loop tracking transfer function is used as the reference model, and an adaptive signal generated from the proposed FNN Controller, whose membership functions and connective weights are trained on-line according to the model-following error of the states, is added to the control system to preserve a favorable model-following characteristics under various operating conditions.

  • a fuzzy Neural Network Controller for parallel resonant ultrasonic motor drive
    IEEE Transactions on Industrial Electronics, 1998
    Co-Authors: Faajeng Lin, Rongjong Wai, Shenglong Wang
    Abstract:

    A newly designed driving circuit for the traveling-wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a current-source two-phase parallel-resonant inverter, is presented in this study. Moreover, since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a fuzzy Neural Network (NN) Controller is proposed to control the USM drive system. In the proposed Controller, a fuzzy model-following Controller is implemented to control the rotor position of the USM, and an online trained NN with variable learning rates is implemented to tune the output scaling factor of the fuzzy Controller. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the desired variable learning rates. From the experimental results, accurate tracking response can be obtained by the proposed Controller, and the influences of parameter variations and external disturbances on the USM drive also can be reduced effectively.

  • a fuzzy Neural Network Controller with adaptive learning rates for nonlinear slider crank mechanism
    Neurocomputing, 1998
    Co-Authors: Rongjong Wai, Faajeng Lin
    Abstract:

    A fuzzy Neural Network (FNN) Controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the Network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN Controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN Controller with adaptive learning rates is demonstrated by simulation and experimental results.

Soheil Ganjefar - One of the best experts on this subject based on the ideXlab platform.

  • variable structure fuzzy wavelet Neural Network Controller for complex nonlinear systems
    Applied Soft Computing, 2018
    Co-Authors: Younes Solgi, Soheil Ganjefar
    Abstract:

    Abstract Controlling a complex nonlinear system has always been an important problem. A neuro fuzzy Controller may be an appropriate Controller for such systems. There have been always problems with large number of neurons which cause heavy complex computations in neuro fuzzy Controllers. In this paper, a variable structure neuro fuzzy Controller is introduced to prevent number of neurons from rising. The proposed variable structure Controller, adapts itself on-line to the system. Therefore, because of the adaptive structure, there is no need to have a large number of neurons. As a result, calculations and Controller complexity will decrease in this structure. The proposed Controller also uses an improved gradient descent method to update different varying parameters. In this improved method, adaptive learning rates are used to prevent parameters getting stuck in local optima. Improved gradient descent method also takes the advantages of Lyapunov stability method in updating the parameters. The use of Lyapunov method in updating the Network parameters will guarantee the stability of the Controller, too. A complex nonlinear time delay system is introduced in this paper to evaluate the proposed variable structure fuzzy wavelet Neural Network Controller. Simulation results illustrate the efficacy of this new Controller.

  • An online trained fuzzy Neural Network Controller to improve stability of power systems
    Neurocomputing, 2015
    Co-Authors: Mohsen Farahani, Soheil Ganjefar
    Abstract:

    The purpose of this paper is to improve the stability in a power system using a new intelligent Controller. This Controller is an online trained fuzzy Neural Network Controller (OTFNNC) in which adaptive learning rates derived by the Lyapunov stability are employed to guarantee the convergence of the proposed Controller. During the online control process, the identification of system is not necessary, because of learning ability of the proposed Controller. One of the proposed Controller features is robustness to different operating conditions and disturbances. Moreover, the Prony method is used to obtain the exponential damping of power system oscillations in this paper.The test power system is a two-area four-machine system power. The simulation results show that the oscillations are satisfactorily damped out by the OTFNNC. The proposed approach is effective to mitigate power system oscillations and improve the stability. Literature review show that no method is proposed to compute the damping of power system oscillation if adaptive and online Controllers like fuzzy and Neural Network Controller are utilized for damping power system oscillations. In this paper, the damping rate of power system oscillations is estimated by the Prony method.

  • improving efficiency of two type maximum power point tracking methods of tip speed ratio and optimum torque in wind turbine system using a quantum Neural Network
    Energy, 2014
    Co-Authors: Soheil Ganjefar, Ali Akbar Ghassemi, Mohamad Mehdi Ahmadi
    Abstract:

    In this paper, a quantum Neural Network (QNN) is used as Controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID Controller and conventional Neural Network Controller (CNNC).