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

  • synchronization control for discrete time delayed dynamical networks with switching topology under actuator saturations
    IEEE Transactions on Neural Networks, 2020
    Co-Authors: Yonggang Chen, Zidong Wang, Jun Hu
    Abstract:

    This article is concerned with the synchronization control problem for a class of discrete-time dynamical networks with mixed delays and switching topology. The saturation phenomenon of physical actuators is specifically considered in designing feedback controllers. By exploring the mixed-delay-dependent sector conditions in combination with the piecewise Lyapunov-like functional and the average-dwell-time switching, a sufficient condition is first established under which all trajectories of the error dynamics are bounded for admissible initial conditions and nonzero external disturbances, while the l₂-l∞ Performance Constraint is satisfied. Furthermore, the exponential stability of the error dynamics is ensured for admissible initial conditions in the absence of disturbances. Second, by using some congruence transformations, the explicit condition guaranteeing the existence of desired controller gains is obtained in terms of the feasibility of a set of linear matrix inequalities. Then, three convex optimization problems are formulated regarding the disturbance tolerance, the l₂-l∞ Performance, and the initial condition set, respectively. Finally, two simulation examples are given to show the effectiveness and merits of the proposed results.

  • variance constrained state estimation for networked multi rate systems with measurement quantization and probabilistic sensor failures
    International Journal of Robust and Nonlinear Control, 2016
    Co-Authors: Yong Zhang, Zidong Wang
    Abstract:

    Summary This paper is concerned with the variance-constrained state estimation problem for a class of networked multi-rate systems (NMSs) with network-induced probabilistic sensor failures and measurement quantization. The stochastic characteristics of the sensor failures are governed by mutually independent random variables over the interval [0,1]. By applying the lifting technique, an augmented system model is established to facilitate the state estimation of the underlying NMSs. With the aid of the stochastic analysis approach, sufficient conditions are derived under which the exponential mean-square stability of the augmented system is guaranteed, the prescribed H∞ Performance Constraint is achieved, and the individual variance Constraint on the steady-state estimation error is satisfied. Based on the derived conditions, the addressed variance-constrained state estimation problem of NMSs is recast as a convex optimization one that can be solved via the semi-definite program method. Furthermore, the explicit expression of the desired estimator gains is obtained by means of the feasibility of certain matrix inequalities. Two additional optimization problems are considered with respect to the H∞ Performance index and the weighted error variances. Finally, a simulation example is utilized to illustrate the effectiveness of the proposed state estimation method. Copyright © 2016 John Wiley & Sons, Ltd.

  • event based h_ infty filter design for a class of nonlinear time varying systems with fading channels and multiplicative noises
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Hongli Dong, Zidong Wang, Steven X Ding, Huijun Gao
    Abstract:

    In this paper, a general event-triggered framework is developed to deal with the finite-horizon $H_{\infty}$ filtering problem for discrete time-varying systems with fading channels, randomly occurring nonlinearities and multiplicative noises. An event indicator variable is constructed and the corresponding event-triggered scheme is proposed. Such a scheme is based on the relative error with respect to the measurement signal in order to determine whether the measurement output should be transmitted to the filter or not. The fading channels are described by modified stochastic Rice fading models. Some uncorrelated random variables are introduced, respectively, to govern the phenomena of state-multiplicative noises, randomly occurring nonlinearities as well as fading measurements. The purpose of the addressed problem is to design a set of time-varying filter such that the influence from the exogenous disturbances onto the filtering errors is attenuated at the given level quantified by a $H_{\infty}$ -norm in the mean-square sense. By utilizing stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the $H_{\infty}$ filtering Performance Constraint, and then a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter gains. Simulation results demonstrate the effectiveness of the developed filter design scheme.

  • FINAL VERSION 1 Quantized H ∞ Control for Nonlinear Stochastic Time-delay Systems with Missing Measurements
    2014
    Co-Authors: Zidong Wang, Bo Shen, Huisheng Shu, Guoliang Wei
    Abstract:

    Abstract—In this paper, the quantized H ∞ control problem is investigated for a class of nonlinear stochastic timedelay network-based systems with probabilistic data missing. A nonlinear stochastic system with state delays is employed to model the networked control systems where the measured output and the input signals are quantized by two logarithmic quantizers, respectively. Moreover, the data missing phenomena are modeled by introducing a diagonal matrix composed of Bernoulli distributed stochastic variables taking values of 1 and 0, which describes that the data from different sensors may be lost with different missing probabilities. Subsequently, a sufficient condition is first derived in virtue of the method of sector-bounded uncertainties, which guarantees that the closedloop system is stochastically stable and the controlled output satisfies H ∞ Performance Constraint for all nonzero exogenous disturbances under the zero-initial condition. Then, the sufficient condition is decoupled into some inequalities for the convenience of practical verification. Based on that, quantized H ∞ controllers are designed successfully for some special classes of nonlinear stochastic time-delay systems by using Matlab linear matrix inequality toolbox. Finally, a numerical simulation example is exploited to show the effectiveness and applicability of the results derived. Index Terms—Nonlinear systems; stochastic systems; discrete time-delay systems; networked control systems; H ∞ control; quantized control; data Missing. I

  • distributed filtering in sensor networks with randomly occurring saturations and successive packet dropouts
    International Journal of Robust and Nonlinear Control, 2014
    Co-Authors: Hongli Dong, Zidong Wang
    Abstract:

    SUMMARY This paper is concerned with the distributed filtering problem for a class of nonlinear systems with randomly occurring sensor saturations (ROSS) and successive packet dropouts in sensor networks. The issue of ROSS is brought up to account for the random nature of sensor saturations in a networked environment of sensors, and accordingly, a novel sensor model is proposed to describe both the ROSS and successive packet dropouts within a unified framework. Two sets of Bernoulli distributed white sequences are introduced to govern the random occurrences of the sensor saturations and successive packet dropouts. Through available output measurements from not only the individual sensor but also its neighboring sensors, a sufficient condition is established for the desired distributed filter to ensure that the filtering dynamics is exponentially mean-square stable and the prescribed Performance Constraint is satisfied. The solution of the distributed filter gains is characterized by solving an auxiliary convex optimization problem. Finally, a simulation example is provided to show the effectiveness of the proposed filtering scheme. Copyright © 2013 John Wiley & Sons, Ltd.

Hongli Dong - One of the best experts on this subject based on the ideXlab platform.

  • event based h_ infty filter design for a class of nonlinear time varying systems with fading channels and multiplicative noises
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Hongli Dong, Zidong Wang, Steven X Ding, Huijun Gao
    Abstract:

    In this paper, a general event-triggered framework is developed to deal with the finite-horizon $H_{\infty}$ filtering problem for discrete time-varying systems with fading channels, randomly occurring nonlinearities and multiplicative noises. An event indicator variable is constructed and the corresponding event-triggered scheme is proposed. Such a scheme is based on the relative error with respect to the measurement signal in order to determine whether the measurement output should be transmitted to the filter or not. The fading channels are described by modified stochastic Rice fading models. Some uncorrelated random variables are introduced, respectively, to govern the phenomena of state-multiplicative noises, randomly occurring nonlinearities as well as fading measurements. The purpose of the addressed problem is to design a set of time-varying filter such that the influence from the exogenous disturbances onto the filtering errors is attenuated at the given level quantified by a $H_{\infty}$ -norm in the mean-square sense. By utilizing stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the $H_{\infty}$ filtering Performance Constraint, and then a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter gains. Simulation results demonstrate the effectiveness of the developed filter design scheme.

  • distributed filtering in sensor networks with randomly occurring saturations and successive packet dropouts
    International Journal of Robust and Nonlinear Control, 2014
    Co-Authors: Hongli Dong, Zidong Wang
    Abstract:

    SUMMARY This paper is concerned with the distributed filtering problem for a class of nonlinear systems with randomly occurring sensor saturations (ROSS) and successive packet dropouts in sensor networks. The issue of ROSS is brought up to account for the random nature of sensor saturations in a networked environment of sensors, and accordingly, a novel sensor model is proposed to describe both the ROSS and successive packet dropouts within a unified framework. Two sets of Bernoulli distributed white sequences are introduced to govern the random occurrences of the sensor saturations and successive packet dropouts. Through available output measurements from not only the individual sensor but also its neighboring sensors, a sufficient condition is established for the desired distributed filter to ensure that the filtering dynamics is exponentially mean-square stable and the prescribed Performance Constraint is satisfied. The solution of the distributed filter gains is characterized by solving an auxiliary convex optimization problem. Finally, a simulation example is provided to show the effectiveness of the proposed filtering scheme. Copyright © 2013 John Wiley & Sons, Ltd.

  • distributed filtering for a class of time varying systems over sensor networks with quantization errors and successive packet dropouts
    IEEE Transactions on Signal Processing, 2012
    Co-Authors: Hongli Dong, Zidong Wang
    Abstract:

    This paper is concerned with the distributed finite-horizon filtering problem for a class of time-varying systems over lossy sensor networks. The time-varying system (target plant) is subject to randomly varying nonlinearities (RVNs) caused by environmental circumstances. The lossy sensor network suffers from quantization errors and successive packet dropouts that are described in a unified framework. Two mutually independent sets of Bernoulli distributed white sequences are introduced to govern the random occurrences of the RVNs and successive packet dropouts. Through available output measurements from not only the individual sensor but also its neighboring sensors according to the given topology, a sufficient condition is established for the desired distributed finite-horizon filter to ensure that the prescribed average filtering Performance Constraint is satisfied. The solution of the distributed filter gains is characterized by solving a set of recursive linear matrix inequalities. A simulation example is provided to show the effectiveness of the proposed filtering scheme.

  • distributed h filtering for repeated scalar nonlinear systems with random packet losses in sensor networks
    International Journal of Systems Science, 2011
    Co-Authors: Hongli Dong
    Abstract:

    This article is concerned with the distributed H∞ filtering problem for sensor networks with repeated scalar nonlinearities and multiple probabilistic packet losses. The class of nonlinear systems is represented by a discrete-time state-space model involving repeated scalar nonlinearities that cover several types of frequently investigated nonlinearities as special cases. A number of stochastic variables, all of which are mutually independent but satisfy a certain probabilistic distribution in the interval [0, 1], are introduced to account for the packet dropout phenomena occurring in the channels from the original system to the networked sensors. The concept of average H∞ index is first introduced to measure the overall Performance of the sensor networks. Then, by utilising available measurement information from not only each individual sensor but also its neighbouring sensors according a given topology, stability analysis is carried out to obtain sufficient conditions for ensuring stochastic stability as well as the prescribed average H∞ Performance Constraint. The solution of the parameters of the distributed filters is characterised in terms of the feasibility of a convex optimisation problem. Finally, a simulation study is conducted for a factory production line in order to demonstrate the effectiveness of the developed theoretical results.

  • robust cal h _ infty filtering for markovian jump systems with randomly occurring nonlinearities and sensor saturation the finite horizon case
    IEEE Transactions on Signal Processing, 2011
    Co-Authors: Hongli Dong, Zidong Wang, Huijun Gao
    Abstract:

    This paper addresses the robust H∞ filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation. Two kinds of transition probability matrices for the Markovian process are considered, namely, the one with polytopic uncertainties and the one with partially unknown entries. The nonlinear disturbances are assumed to occur randomly according to stochastic variables satisfying the Bernoulli distributions. The main purpose of this paper is to design a robust filter, over a given finite-horizon, such that the H∞ disturbance attenuation level is guaranteed for the time-varying Markovian jump systems in the presence of both the randomly occurring nonlinearities and the sensor saturation. Sufficient conditions are established for the existence of the desired filter satisfying the H∞ Performance Constraint in terms of a set of recursive linear matrix inequalities. Simulation results demonstrate the effectiveness of the developed filter design scheme.

Huijun Gao - One of the best experts on this subject based on the ideXlab platform.

  • event based h_ infty filter design for a class of nonlinear time varying systems with fading channels and multiplicative noises
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Hongli Dong, Zidong Wang, Steven X Ding, Huijun Gao
    Abstract:

    In this paper, a general event-triggered framework is developed to deal with the finite-horizon $H_{\infty}$ filtering problem for discrete time-varying systems with fading channels, randomly occurring nonlinearities and multiplicative noises. An event indicator variable is constructed and the corresponding event-triggered scheme is proposed. Such a scheme is based on the relative error with respect to the measurement signal in order to determine whether the measurement output should be transmitted to the filter or not. The fading channels are described by modified stochastic Rice fading models. Some uncorrelated random variables are introduced, respectively, to govern the phenomena of state-multiplicative noises, randomly occurring nonlinearities as well as fading measurements. The purpose of the addressed problem is to design a set of time-varying filter such that the influence from the exogenous disturbances onto the filtering errors is attenuated at the given level quantified by a $H_{\infty}$ -norm in the mean-square sense. By utilizing stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the $H_{\infty}$ filtering Performance Constraint, and then a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter gains. Simulation results demonstrate the effectiveness of the developed filter design scheme.

  • robust cal h _ infty filtering for markovian jump systems with randomly occurring nonlinearities and sensor saturation the finite horizon case
    IEEE Transactions on Signal Processing, 2011
    Co-Authors: Hongli Dong, Zidong Wang, Huijun Gao
    Abstract:

    This paper addresses the robust H∞ filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation. Two kinds of transition probability matrices for the Markovian process are considered, namely, the one with polytopic uncertainties and the one with partially unknown entries. The nonlinear disturbances are assumed to occur randomly according to stochastic variables satisfying the Bernoulli distributions. The main purpose of this paper is to design a robust filter, over a given finite-horizon, such that the H∞ disturbance attenuation level is guaranteed for the time-varying Markovian jump systems in the presence of both the randomly occurring nonlinearities and the sensor saturation. Sufficient conditions are established for the existence of the desired filter satisfying the H∞ Performance Constraint in terms of a set of recursive linear matrix inequalities. Simulation results demonstrate the effectiveness of the developed filter design scheme.

  • vibration control for active seat suspension systems via dynamic output feedback with limited frequency characteristic
    Mechatronics, 2011
    Co-Authors: Weichao Sun, Ye Zhao, Huijun Gao
    Abstract:

    Abstract This paper investigates the problem of H ∞ control for active seat suspension systems via dynamic output feedback control. A vertical vibration model of human body is introduced in order to make the modeling of seat suspension systems more precise. Meantime, different from the existing H ∞ control methods which conduct disturbance attenuation within the entire frequency domain, this paper addresses the problem of H ∞ control for active seat suspension systems in finite frequency domain to match the characteristics of the human body. By using the generalized Kalman–Yakubovich–Popov (KYP) lemma, the H ∞ norm from the disturbance to the controlled output is decreased over the chosen frequency band between which the human body is extremely sensitive to the vibration, to improve the ride comfort. Considering a practical situation of active seat suspension systems, a dynamic output feedback controller of order equal to the plant is designed, where an effective multiplier expansion is used to convert the controller design to a convex optimization problem. Compared with the entire frequency approach for active seat suspension systems, the finite frequency approach achieves better disturbance attenuation for the concerned frequency range, while the Performance Constraint is guaranteed in the controller design, which is verified by a practical example with certain and random road disturbances.

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

  • event triggered h state estimation for discrete time stochastic genetic regulatory networks with markovian jumping parameters and time varying delays
    Neurocomputing, 2016
    Co-Authors: Qi Li, Bo Shen, Fuad E Alsaadi
    Abstract:

    In this paper, the event-triggered H ∞ state estimation problem is investigated for a class of discrete-time stochastic genetic regulatory networks with both Markovian jumping parameters and time-varying delays. The jumping parameters are governed by a homogeneous Markovian chain and the time-varying delays under consideration occur in both the feedback regulatory process and transcription process. The aim of this paper is to estimate the concentrations of mRNA and protein in such genetic regulatory networks by using the available measurement outputs. In order to reduce the information communication burden, the event-triggered mechanism is adopted and the measurement outputs are only transmitted to the estimator when a certain triggered condition is met. By constructing an appropriate Lyapunov functional, some sufficient conditions are derived under which the estimation error dynamics is stochastically stable and the H ∞ Performance Constraint is satisfied. Based on the analysis results, the desired H ∞ estimator parameters are designed in terms of the solution to a set of matrix inequalities that can be easily solved by the Matlab toolboxes. Finally, a simulation example is provided to illustrate the effectiveness of the proposed event-triggered state estimation scheme.

  • FINAL VERSION 1 Quantized H ∞ Control for Nonlinear Stochastic Time-delay Systems with Missing Measurements
    2014
    Co-Authors: Zidong Wang, Bo Shen, Huisheng Shu, Guoliang Wei
    Abstract:

    Abstract—In this paper, the quantized H ∞ control problem is investigated for a class of nonlinear stochastic timedelay network-based systems with probabilistic data missing. A nonlinear stochastic system with state delays is employed to model the networked control systems where the measured output and the input signals are quantized by two logarithmic quantizers, respectively. Moreover, the data missing phenomena are modeled by introducing a diagonal matrix composed of Bernoulli distributed stochastic variables taking values of 1 and 0, which describes that the data from different sensors may be lost with different missing probabilities. Subsequently, a sufficient condition is first derived in virtue of the method of sector-bounded uncertainties, which guarantees that the closedloop system is stochastically stable and the controlled output satisfies H ∞ Performance Constraint for all nonzero exogenous disturbances under the zero-initial condition. Then, the sufficient condition is decoupled into some inequalities for the convenience of practical verification. Based on that, quantized H ∞ controllers are designed successfully for some special classes of nonlinear stochastic time-delay systems by using Matlab linear matrix inequality toolbox. Finally, a numerical simulation example is exploited to show the effectiveness and applicability of the results derived. Index Terms—Nonlinear systems; stochastic systems; discrete time-delay systems; networked control systems; H ∞ control; quantized control; data Missing. I

  • brief paper distributed h consensus filtering in sensor networks with multiple missing measurements the finite horizon case
    Automatica, 2010
    Co-Authors: Bo Shen, Zidong Wang, Y S Hung
    Abstract:

    This paper is concerned with a new distributed H"~-consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements. The so-called H"~-consensus Performance requirement is defined to quantify bounded consensus regarding the filtering errors (agreements) over a finite-horizon. A set of random variables are utilized to model the probabilistic information missing phenomena occurring in the channels from the system to the sensors. A sufficient condition is first established in terms of a set of difference linear matrix inequalities (DLMIs) under which the expected H"~-consensus Performance Constraint is guaranteed. Given the measurements and estimates of the system state and its neighbors, the filter parameters are then explicitly parameterized by means of the solutions to a certain set of DLMIs that can be computed recursively. Subsequently, two kinds of robust distributed H"~-consensus filters are designed for the system with norm-bounded uncertainties and polytopic uncertainties. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the proposed distributed filters design scheme.

Branko G Celler - One of the best experts on this subject based on the ideXlab platform.

  • neural adaptive backstepping control of a robotic manipulator with prescribed Performance Constraint
    IEEE Transactions on Neural Networks, 2019
    Co-Authors: Qing Guo, Yi Zhang, Branko G Celler
    Abstract:

    This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed Performance Constraint, a weighted Performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional–integral-derivative and TBC methods.