The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform

Yung C. Shin - One of the best experts on this subject based on the ideXlab platform.

  • A Fuzzy Inverse Model Construction Method for General Monotonic Multi-Input-- Single-Output (MISO) Systems
    IEEE Transactions on Fuzzy Systems, 2008
    Co-Authors: Yung C. Shin
    Abstract:

    This paper presents a novel method of systematically constructing a fuzzy inverse model for general multi-input-single-Output (MISO) systems represented with triangular input Membership Functions, singleton Output Membership Function, and fuzzy-mean defuzzification. The fuzzy inverse model construction method has the ability of uniquely determining the inverse relationship for each input-Output pair. It is derived in a straightforward way and the required input variables can be simultaneously obtained by the fuzzy inferencing calculation to realize the desired Output value. Simulation examples are provided to demonstrate the effectiveness of the proposed method to find the inverse kinematics solutions for complex multiple degree-of-freedom industrial robot manipulators.

  • A Fuzzy Inverse Model Construction Method for a General MISO System with a Monotonic Input-Output Relationship
    NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society, 2007
    Co-Authors: Yung C. Shin
    Abstract:

    This paper presents a novel method of systematically constructing the fuzzy inverse model for a general multi-input single-Output (MISO) system represented with triangular input Membership Functions, singleton Output Membership Function and fuzzy-mean defuzzification. The fuzzy inverse model construction method has the ability of uniquely determining the inverse relationship for each input-Output pair. It is derived in a straightforward way and the required input variables can be simultaneously obtained by the fuzzy inferencing calculation to realize the desired Output value. Simulation examples are provided to demonstrate the effectiveness of the proposed method to find the inverse kinematics solutions for complex industrial robot manipulators.

Hicham Chaoui - One of the best experts on this subject based on the ideXlab platform.

  • An Online Energy Management Strategy for a Fuel Cell/Battery Vehicle Considering the Driving Pattern and Performance Drift Impacts
    IEEE Transactions on Vehicular Technology, 2019
    Co-Authors: Mohsen Kandidayeni, Alvaro Omar Macias Fernandez, Arash Khalatbarisoltani, Loïc Boulon, Sousso Kelouwani, Hicham Chaoui
    Abstract:

    Energy management strategy (EMS) has a profound influence over the performance of a fuel cell hybrid electric vehicle since it can maintain the energy sources in their high efficacy zones leading to efficiency and lifetime enhancement of the system. This paper puts forward an online multi-mode EMS to efficiently split the power among the components while embracing the effects of the driving conditions and performance degradation of the fuel cell system. In this regard, firstly, a self-organizing map (SOM) is trained to cluster the driving patterns. The SOM competitive layer in this work is composed of ten driving features as inputs and it classifies the driving patterns into three classes in the Output. Subsequently, a three-mode fuzzy logic controller (FLC) is designed and optimized offline by the genetic algorithm for each driving pattern. Unlike the other similar works, the Output Membership Function of the FLC is designed based on the online identification of the maximum power and efficiency of the fuel cell system which change over time. Finally, the SOM is utilized to recognize the driving mode at each sequence and accordingly activate the most suitable mode of the FLC to meet the requested power by efficient use of the energy sources. The performance of the proposed EMS has been validated by using the hardware-in-the-loop platform for several scenarios. The experimental results analyses indicate the promising performance of the suggested methodology in terms of ameliorating hydrogen economy and the fuel cell system lifetime.

Mohsen Kandidayeni - One of the best experts on this subject based on the ideXlab platform.

  • An Online Energy Management Strategy for a Fuel Cell/Battery Vehicle Considering the Driving Pattern and Performance Drift Impacts
    IEEE Transactions on Vehicular Technology, 2019
    Co-Authors: Mohsen Kandidayeni, Alvaro Omar Macias Fernandez, Arash Khalatbarisoltani, Loïc Boulon, Sousso Kelouwani, Hicham Chaoui
    Abstract:

    Energy management strategy (EMS) has a profound influence over the performance of a fuel cell hybrid electric vehicle since it can maintain the energy sources in their high efficacy zones leading to efficiency and lifetime enhancement of the system. This paper puts forward an online multi-mode EMS to efficiently split the power among the components while embracing the effects of the driving conditions and performance degradation of the fuel cell system. In this regard, firstly, a self-organizing map (SOM) is trained to cluster the driving patterns. The SOM competitive layer in this work is composed of ten driving features as inputs and it classifies the driving patterns into three classes in the Output. Subsequently, a three-mode fuzzy logic controller (FLC) is designed and optimized offline by the genetic algorithm for each driving pattern. Unlike the other similar works, the Output Membership Function of the FLC is designed based on the online identification of the maximum power and efficiency of the fuel cell system which change over time. Finally, the SOM is utilized to recognize the driving mode at each sequence and accordingly activate the most suitable mode of the FLC to meet the requested power by efficient use of the energy sources. The performance of the proposed EMS has been validated by using the hardware-in-the-loop platform for several scenarios. The experimental results analyses indicate the promising performance of the suggested methodology in terms of ameliorating hydrogen economy and the fuel cell system lifetime.

Yang Fan - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Rocket Launcher Position Servo System Based on Model Reference Fuzzy Neural Network
    Journal of Gun Launch & Control, 2010
    Co-Authors: Yang Fan
    Abstract:

    Considering the atrocious load property when the rocket is launched,an adaptive position controller based on model reference fuzzy neural network is designed.RBF network is used to recognize the Jacobian information of the object controlled.The parameters of input and Output Membership Function are modified online by a gradient method to make the model reference fuzzy neural network adjust the given speed in real time according to the load property of rocket launcher.Thus the influence of the change of system parameters and external disturbance on the rocket launcher position servo system could be minified.Simulation results show that this method could improve the stability and anti-disturbance ability of rocket launcher position servo system effectively.

  • Design of Fuzzy Neural Network Position Controller of Rocket Launcher Servo System Based on DSP
    2010
    Co-Authors: Yang Fan
    Abstract:

    Considering the atrocious load property when the rocket is launched,an adaptive position controller based on fuzzy neural network is designed.The parameters of input and Output Membership Function are modified online by a gradient method to make the fuzzy neural network adjust the given speed in real time according to the load property of rocket launcher.Thus the influence of the change of system parameters and external disturbance on the rocket launcher position servo system could be minified.An index table of fuzzy control rule is established and a novel algorithm for the implementation of fuzzy neural network position controller on the TMS320F2812 is proposed.Simulation and experiment results show that this method could improve the stability and anti-disturbance ability of rocket launcher position servo system effectively.

Arash Khalatbarisoltani - One of the best experts on this subject based on the ideXlab platform.

  • An Online Energy Management Strategy for a Fuel Cell/Battery Vehicle Considering the Driving Pattern and Performance Drift Impacts
    IEEE Transactions on Vehicular Technology, 2019
    Co-Authors: Mohsen Kandidayeni, Alvaro Omar Macias Fernandez, Arash Khalatbarisoltani, Loïc Boulon, Sousso Kelouwani, Hicham Chaoui
    Abstract:

    Energy management strategy (EMS) has a profound influence over the performance of a fuel cell hybrid electric vehicle since it can maintain the energy sources in their high efficacy zones leading to efficiency and lifetime enhancement of the system. This paper puts forward an online multi-mode EMS to efficiently split the power among the components while embracing the effects of the driving conditions and performance degradation of the fuel cell system. In this regard, firstly, a self-organizing map (SOM) is trained to cluster the driving patterns. The SOM competitive layer in this work is composed of ten driving features as inputs and it classifies the driving patterns into three classes in the Output. Subsequently, a three-mode fuzzy logic controller (FLC) is designed and optimized offline by the genetic algorithm for each driving pattern. Unlike the other similar works, the Output Membership Function of the FLC is designed based on the online identification of the maximum power and efficiency of the fuel cell system which change over time. Finally, the SOM is utilized to recognize the driving mode at each sequence and accordingly activate the most suitable mode of the FLC to meet the requested power by efficient use of the energy sources. The performance of the proposed EMS has been validated by using the hardware-in-the-loop platform for several scenarios. The experimental results analyses indicate the promising performance of the suggested methodology in terms of ameliorating hydrogen economy and the fuel cell system lifetime.