The Experts below are selected from a list of 37794 Experts worldwide ranked by ideXlab platform

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

  • fast and accurate Center of Gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions
    Fuzzy Sets and Systems, 2006
    Co-Authors: Ester Van Broekhoven, B De Baets
    Abstract:

    In this article three methods are presented to perform the Center of Gravity (COG) defuzzification method in the context of linguistic fuzzy models with t-norm-based inference: one well-known method, the discretisation method, and two new methods, the slope-based method and the modified transformation function method. The methods are worked out for trapezoidal membership functions forming a fuzzy partition in the sense of Ruspini. Experimental results show that the newly introduced methods exhibit excellent accuracy at an extremely low computational cost compared to the widely applied discretisation method.

  • a comparison of three methods for computing the Center of Gravity defuzzification
    IEEE International Conference on Fuzzy Systems, 2004
    Co-Authors: E Van Broekhoven, B De Baets
    Abstract:

    In this article three methods are presented to perform the Center of Gravity defuzzification method: one well known method, the discretisation method, and two new methods, the slope-based method and the modified transformation function method. The methods are worked out for trapezoidal membership functions forming a fuzzy (Ruspini) partition. Experimental results show that the newly introduced methods exhibit excellent accuracy at an extremely low computational cost compared to the widely applied discretisation method.

Xingqun Cheng - One of the best experts on this subject based on the ideXlab platform.

  • a novel h and ekf joint estimation method for determining the Center of Gravity position of electric vehicles
    Applied Energy, 2017
    Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun Cheng
    Abstract:

    In order to ensure the safety and reliability of electric vehicles (EVs), the accurate Center of Gravity (CG) position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.

  • estimation of Center of Gravity position for distributed driving electric vehicles based on combined h ekf method
    Energy Procedia, 2016
    Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle Gong
    Abstract:

    Abstract It is essential to get the accurate knowledge of the Center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG positions can be affected by the loading conditions. This paper focuses on a CG position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.

Xinle Gong - One of the best experts on this subject based on the ideXlab platform.

  • a novel h and ekf joint estimation method for determining the Center of Gravity position of electric vehicles
    Applied Energy, 2017
    Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun Cheng
    Abstract:

    In order to ensure the safety and reliability of electric vehicles (EVs), the accurate Center of Gravity (CG) position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.

  • estimation of Center of Gravity position for distributed driving electric vehicles based on combined h ekf method
    Energy Procedia, 2016
    Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle Gong
    Abstract:

    Abstract It is essential to get the accurate knowledge of the Center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG positions can be affected by the loading conditions. This paper focuses on a CG position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.

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

  • a novel h and ekf joint estimation method for determining the Center of Gravity position of electric vehicles
    Applied Energy, 2017
    Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun Cheng
    Abstract:

    In order to ensure the safety and reliability of electric vehicles (EVs), the accurate Center of Gravity (CG) position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.

  • estimation of Center of Gravity position for distributed driving electric vehicles based on combined h ekf method
    Energy Procedia, 2016
    Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle Gong
    Abstract:

    Abstract It is essential to get the accurate knowledge of the Center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG positions can be affected by the loading conditions. This paper focuses on a CG position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.

Richard Shiavi - One of the best experts on this subject based on the ideXlab platform.

  • simultaneous measurement of body Center of pressure and Center of Gravity during upright stance part i methods
    Gait & Posture, 1996
    Co-Authors: Samer S Hasan, Deborah W Robin, Dennis Szurkus, Daniel H Ashmead, Steven W Peterson, Richard Shiavi
    Abstract:

    Abstract Clinical and experimental assessments of balance use measures of Center of pressure (COP) excursion to quantify postural stability during standing. This assumes that the greater the COP excursions, the greater the imbalance. However, it is the position of the body Center of Gravity (COG), specifically in relation to the base of support, that determines static stability during upright standing. The reason the COG is frequently ignored is that it cannot be measured directly, whereas the COP is readily obtained from a force platform. We report here on a method for simultaneous measurement of the COP and COG displacements. To compute the COG displacements, an optoelectric imaging system operating in synchrony with a force platform was used to measure the three-dimensional positions of the body joints and body segment endpoints. The COG displacements were then computed from the segment kinematics using subject-specific anthropometric measurements. In Part II of this paper, we compare summary measures of the COP and COG excursions obtained from six subjects during three different upright stances.