The Experts below are selected from a list of 12228 Experts worldwide ranked by ideXlab platform

Dexing Feng - One of the best experts on this subject based on the ideXlab platform.

Ge Zhaoqiang - One of the best experts on this subject based on the ideXlab platform.

  • pole assignment for a singular Distributed Parameter System coupled with a singular lumped Parameter System pole assignment for a singular Distributed Parameter System coupled with a singular lumped Parameter System
    SCIENTIA SINICA Informationis, 2017
    Co-Authors: Ge Zhaoqiang, Dexing Feng
    Abstract:

    The pole assignment for a first order singular Distributed Parameter control System coupled with a first order singular lumped Parameter control System is discussed via functional analysis and operator theory in Hilbert space. The solutions of the problem and the constructive expressions of the solutions are given by the bounded inner inverse of the bounded linear operator. This research is theoretically important for studying the pole assignment and stabilizability of singular Distributed Parameter control Systems.

  • Mild Solution and Exact Controllability of Semilinear Singular Distributed Parameter Systems
    2014 International Conference on Automatic Control Theory and Application, 2014
    Co-Authors: Ge Zhaoqiang
    Abstract:

    Mild solution and exact controllability of semilinear singular Distributed Parameter System are discussed in Hilbert space, some results are obtained by functional analysis and generalized operator semigroup. First, we study the classical solution concerning the homogeneous linear singular Distributed Parameter System by generalized operator semigroup. Second, the existence and uniqueness for the mild solution of semilinear singular Distributed Parameter System is proved. Third, a new result concerning the exact controllability of linear singular Distributed Parameter System is obtained. At last, the exact controllability for the semilinear singular Distributed Parameter System is discussed. This research is theoretical important for studying the controllability of nonlinear singular Distributed Parameter Systems. Keywords-mild solution; exact controllability; semilinear singular Distributed Parameter System; generalized operator semigroup.

  • Exponential stability of time varying singular Distributed Parameter System
    2013 25th Chinese Control and Decision Conference (CCDC), 2013
    Co-Authors: Ge Zhaoqiang
    Abstract:

    Exponential stability of time varying singular Distributed Parameter System is discussed in Hilbert space. One sufficient condition for the exponential stability are obtained by using functional analysis and GE-evolution operator (i.e., generalized evolution operator). This result is theoretically important for studying the stability of time varying singular Distributed Parameter Systems.

  • the control synthesis problem for the second order singular Distributed Parameter System with multiple inputs
    Scientia Sinica Informationis, 2012
    Co-Authors: Feng Liu, Ge Zhaoqiang, Guodong Shi
    Abstract:

    Singular Distributed Parameter System is widely applicable, for example, to the studies of the temperature distribution of composites, signal propagation in electrical cables, voltage distribution of electromagnetically coupled superconductive circuits, and aerocraft attitude control. Pole assignment and feedback stability are two important research aspects of the System. This paper, first of all, defines the pole assignment and feedback stability for the second order singular Distributed Parameter System with multiple inputs in Hilbert space, the subnegative definite operator, and the generalized inverse of the bounded linear operators, and presents some relevant properties; and then, it Systemizes the second order singular Distributed Parameter System with multiple inputs into the first order singular Distributed Parameter System with multiple inputs by using the suitable transformation, which makes the two Systems be of the same generalized eigenvalues, and discusses the pole assignment and feedback stability problems of the first order singular Distributed Parameter System with multiple inputs via functional analysis, operator theory and subnegative definite operator. Based on it, some sufficient conditions of the pole assagnment and stability of the second order singular Distributed Parameter System with multiple inputs in Hilbert space are obtained. It establishes the constructive expressions of the feedback control laws by using complex analysis, complex series and the generalized inverse of the bounded linear operators. The research results are not only of practical significance, but of important theoretical values for studying the control synthesis problem of the singular Distributed Parameter System.

  • GE-semigroup methods for the stability of the singular Distributed Parameter Systems
    2012
    Co-Authors: Ge Zhaoqiang
    Abstract:

    Weak, asymptotic and exponential stability of the singular Distributed Parameter System are discussed in the light of GE-semigroup (i.e., generalized operator semigroup), functional analysis and operator theory in Hilbert space. The necessary and sufficient conditions concerning the weak, asymptotic and exponential stability of the singular Distributed Parameter System are given.

Hongbo Shi - One of the best experts on this subject based on the ideXlab platform.

  • hybrid neural network predictor for Distributed Parameter System based on nonlinear dimension reduction
    Neurocomputing, 2016
    Co-Authors: Mengling Wang, Huaicheng Yan, Hongbo Shi
    Abstract:

    In this study, a hybrid neural network predictor is proposed to predict spatiotemporal dynamics of the nonlinear Distributed Parameter Systems (DPSs) with unwanted disturbance or slow set point changes. First, a nonlinear principal component analysis (NL-PCA) network is designed to transform the high-dimensional spatiotemporal data into a low-dimensional time domain, which can better represent the nonlinearity of the System compared to the linear time/space separation method. Then the hybrid NN models are built to identify the low-dimensional temporal data. To capture the spatiotemporal dynamics of DPS, the four-step recursive algorithm is used to obtain the time-varying weights of the model, while the Parameters of NN model does not need to online update. The simulations demonstrated show that the proposed approach can achieve a good performance on prediction with System slow time-varying dynamics.

  • an adaptive neural network prediction for nonlinear parabolic Distributed Parameter System based on block wise moving window technique
    Neurocomputing, 2014
    Co-Authors: Mengling Wang, Hongbo Shi
    Abstract:

    This paper proposes an efficient adaptive artificial neural network (ANN) model for nonlinear parabolic Distributed Parameter Systems (DPSs) with changes in operating condition. To obtain the complex spatiotemporal dynamics of DPS, the ANN model is updated via applying block-wise recursive formula. The improved group search optimization (IGSO) approach is proposed to optimize the connection weights and thresholds of the ANN to solve the problem of falling into the local optima. Meanwhile, when the number of the new data does not reach the threshold of the block-wise, the ANN does not need to update. And, the predictive output consists of the ANN model predictive output and the compensated output obtained from the real time predictive errors by recursive least squares method. The proposed method can effectively capture the slowly changing of process dynamics and decrease the computational cost. Simulations are presented to demonstrate the accuracy and effectiveness of the proposed methods.

  • spatiotemporal prediction for nonlinear parabolic Distributed Parameter System using an artificial neural network trained by group search optimization
    Neurocomputing, 2013
    Co-Authors: Mengling Wang, Xingdi Yan, Hongbo Shi
    Abstract:

    A spatiotemporal variable of Distributed Parameter Systems (DPSs) can be expressed by an infinite number of spatial basis functions and the corresponding temporal coefficients. For parabolic type DPSs, the first finite basis functions can provide a good approximation because of their slow/fast separation properties. This paper proposes an artificial neural network (ANN) based time/space separation modeling approach to predict nonlinear parabolic DPSs. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by PCA method. Then an ANN is identified by low-dimensional time series, where the group search optimization (GSO) is proposed to optimize the connection weights and thresholds to solve the problem of falling into the local optima. Finally, the nonlinear spatiotemporal dynamics is determined after the time/space reconstruction. Simulations are presented to demonstrate the accuracies and effectiveness of the proposed methodologies.

  • embedded interval type 2 t s fuzzy time space separation modeling approach for nonlinear Distributed Parameter System
    Industrial & Engineering Chemistry Research, 2011
    Co-Authors: Mengling Wang, Hongbo Shi
    Abstract:

    Challenging modeling problems include how to obtain a simple and accurate model for a partial differential equation (PDE) unknown nonlinear Distributed Parameter System (DPS). In this paper, a time/space separation modeling approach based on the interval type-2 T-S fuzzy model (IT2 T-S model) is proposed for nonlinear DPS. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by a linear time/space separation method. Second, the interval type-2 T-S fuzzy model is determined from the low-dimensional time series to reconstruct the System dynamics through the spatial basis functions. As the IT2 T-S model employs an interval linear model in the consequent part, the relationship between the input and spatial output can be determined by linear expressions through type reduction and linear time/space reconstruction. Thus, the obtained model is suitable for control design. The simulation presents the accuracies and effectiveness of the proposed modeling methodologies.

Jun-wei Wang - One of the best experts on this subject based on the ideXlab platform.

  • abnormal spatio temporal source estimation for a linear unstable parabolic Distributed Parameter System an adaptive pde observer perspective
    Journal of The Franklin Institute-engineering and Applied Mathematics, 2021
    Co-Authors: Yun Feng, Yaonan Wang, Jun-wei Wang
    Abstract:

    Abstract Detection and estimation of abnormalities for Distributed Parameter System (DPS) have wide applications in industry, e.g., battery thermal fault diagnosis, quality monitoring of hot-rolled strip laminar cooling process. In this paper, the abnormal spatio-temporal (S-T) source detection and estimation problem for a linear unstable DPS is first studied. The proposed methodology consists of two steps: first, an abnormality detection filter (ADF) which generates a residual signal for abnormality detection in the time domain is constructed using pointwise measurement; Then, an adaptive Luenberger-type PDE observer including an adaptive estimation algorithm is designed and triggered only when an alarm raises from the ADF. Theoretic analysis based on the spatial domain decomposition approach is presented to show the convergence of the estimation errors. Finally, an illustrative example is presented to show the performance of the proposed method.

  • Adaptive Neural Boundary Control Design for Nonlinear Flexible Distributed Parameter Systems
    IEEE Transactions on Control Systems Technology, 2019
    Co-Authors: Jun-wei Wang, Ya-qiang Liu, Changyin Sun
    Abstract:

    This paper proposes an adaptive neural boundary feedback control design for a nonlinear flexible Distributed Parameter System described by a nonuniform wave equation containing an unknown nonlinear function in the boundary conditions. In the proposed design method, a radial-basis-function neural network (NN) with an adaptive weight update law is used to approximate the unknown nonlinear term. By using this adaptive NN, an adaptive boundary feedback controller is constructed to ensure that the resulting closed-loop System is practically exponentially stable. The closed-loop stability of the System is analyzed by the Lyapunov direct method and technique of integration by parts, and the update law of an NN weight vector is determined simultaneously. The closed-loop well-posedness analysis is also provided by applying the $C_{0}$ -semigroup approach. Moreover, the proposed design method is extended for a nonlinear flexible Distributed Parameter System modeled by a nonuniform Euler–Bernoulli beam equation containing an unknown nonlinear function in the boundary conditions. Finally, extensive numerical simulation results are given to demonstrate the performance of the proposed adaptive neural boundary control design method.

  • a spatial domain decomposition approach to Distributed h observer design of a linear unstable parabolic Distributed Parameter System with spatially discrete sensors
    International Journal of Control, 2017
    Co-Authors: Jun-wei Wang, Yanyan Hu
    Abstract:

    ABSTRACTThis paper discusses the design problem of Distributed H∞ Luenberger-type partial differential equation (PDE) observer for state estimation of a linear unstable parabolic Distributed Parameter System (DPS) with external disturbance and measurement disturbance. Both pointwise measurement in space and local piecewise uniform measurement in space are considered, that is, sensors are only active at some specified points or applied at part thereof of the spatial domain. The spatial domain is decomposed into multiple subdomains according to the location of the sensors such that only one sensor is located at each subdomain. By using Lyapunov technique, Wirtinger’s inequality at each subdomain, and integration by parts, a Lyapunov-based design of Luenberger-type PDE observer is developed such that the resulting estimation error System is exponentially stable with an H∞ performance constraint, and presented in terms of standard linear matrix inequalities (LMIs). For the case of local piecewise uniform meas...

  • Luenberger observer design for state estimation of a linear parabolic Distributed Parameter System with discrete measurement sensors
    2016 12th World Congress on Intelligent Control and Automation (WCICA), 2016
    Co-Authors: Jun-wei Wang
    Abstract:

    This paper discusses the problem of Luenberger-type PDE observer design for state estimation of a linear parabolic Distributed Parameter System with discrete measurement sensors. Both pointwise measurement and piecewise uniform measurement are considered. Lyapunov-based design methods of a Luenberger-type PDE observer using the measurement output are derived for the PDE System, and presented in terms of standard linear matrix inequalities (LMIs). Numerical simulation results are presented to show the effectiveness of the proposed design methods.

Mengling Wang - One of the best experts on this subject based on the ideXlab platform.

  • hybrid neural network predictor for Distributed Parameter System based on nonlinear dimension reduction
    Neurocomputing, 2016
    Co-Authors: Mengling Wang, Huaicheng Yan, Hongbo Shi
    Abstract:

    In this study, a hybrid neural network predictor is proposed to predict spatiotemporal dynamics of the nonlinear Distributed Parameter Systems (DPSs) with unwanted disturbance or slow set point changes. First, a nonlinear principal component analysis (NL-PCA) network is designed to transform the high-dimensional spatiotemporal data into a low-dimensional time domain, which can better represent the nonlinearity of the System compared to the linear time/space separation method. Then the hybrid NN models are built to identify the low-dimensional temporal data. To capture the spatiotemporal dynamics of DPS, the four-step recursive algorithm is used to obtain the time-varying weights of the model, while the Parameters of NN model does not need to online update. The simulations demonstrated show that the proposed approach can achieve a good performance on prediction with System slow time-varying dynamics.

  • an adaptive neural network prediction for nonlinear parabolic Distributed Parameter System based on block wise moving window technique
    Neurocomputing, 2014
    Co-Authors: Mengling Wang, Hongbo Shi
    Abstract:

    This paper proposes an efficient adaptive artificial neural network (ANN) model for nonlinear parabolic Distributed Parameter Systems (DPSs) with changes in operating condition. To obtain the complex spatiotemporal dynamics of DPS, the ANN model is updated via applying block-wise recursive formula. The improved group search optimization (IGSO) approach is proposed to optimize the connection weights and thresholds of the ANN to solve the problem of falling into the local optima. Meanwhile, when the number of the new data does not reach the threshold of the block-wise, the ANN does not need to update. And, the predictive output consists of the ANN model predictive output and the compensated output obtained from the real time predictive errors by recursive least squares method. The proposed method can effectively capture the slowly changing of process dynamics and decrease the computational cost. Simulations are presented to demonstrate the accuracy and effectiveness of the proposed methods.

  • spatiotemporal prediction for nonlinear parabolic Distributed Parameter System using an artificial neural network trained by group search optimization
    Neurocomputing, 2013
    Co-Authors: Mengling Wang, Xingdi Yan, Hongbo Shi
    Abstract:

    A spatiotemporal variable of Distributed Parameter Systems (DPSs) can be expressed by an infinite number of spatial basis functions and the corresponding temporal coefficients. For parabolic type DPSs, the first finite basis functions can provide a good approximation because of their slow/fast separation properties. This paper proposes an artificial neural network (ANN) based time/space separation modeling approach to predict nonlinear parabolic DPSs. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by PCA method. Then an ANN is identified by low-dimensional time series, where the group search optimization (GSO) is proposed to optimize the connection weights and thresholds to solve the problem of falling into the local optima. Finally, the nonlinear spatiotemporal dynamics is determined after the time/space reconstruction. Simulations are presented to demonstrate the accuracies and effectiveness of the proposed methodologies.

  • embedded interval type 2 t s fuzzy time space separation modeling approach for nonlinear Distributed Parameter System
    Industrial & Engineering Chemistry Research, 2011
    Co-Authors: Mengling Wang, Hongbo Shi
    Abstract:

    Challenging modeling problems include how to obtain a simple and accurate model for a partial differential equation (PDE) unknown nonlinear Distributed Parameter System (DPS). In this paper, a time/space separation modeling approach based on the interval type-2 T-S fuzzy model (IT2 T-S model) is proposed for nonlinear DPS. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by a linear time/space separation method. Second, the interval type-2 T-S fuzzy model is determined from the low-dimensional time series to reconstruct the System dynamics through the spatial basis functions. As the IT2 T-S model employs an interval linear model in the consequent part, the relationship between the input and spatial output can be determined by linear expressions through type reduction and linear time/space reconstruction. Thus, the obtained model is suitable for control design. The simulation presents the accuracies and effectiveness of the proposed modeling methodologies.