Dongyan Chen

State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. The norm-bounded uncertainties enter into the system in a randomly way, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequence with known conditional probability. By constructing a new Lyapunov–Krasovskii functional, sufficient conditions are proposed to guarantee the convergence of the estimation error for all discrete time-varying delays, ROUs and distributed sensor delays. Subsequently, the explicit form of the estimator parameter is derived by solving two linear matrix inequalities (LMIs) which can be easily tested by using standard numerical software. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed estimation scheme.

A recursive approach to non-fragile filtering for networked systems with stochastic uncertainties and incomplete measurements ☆

Abstract In this paper, the non-fragile recursive filtering problem is investigated for a class of networked time-varying nonlinear systems with stochastic uncertainties and incomplete measurements. By employing a stochastic Kronecker delta function, the phenomena of the incomplete measurements are characterized in a unified framework which contain the randomly occurring signal quantization and the missing measurements. Based on the available probability information of the incomplete measurements, a new filtering compensation scheme is proposed to ensure that, for all stochastic uncertainties, incomplete measurements and stochastic perturbations of the filter gain, an upper bound of the filtering error covariance is guaranteed and such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter gain can be obtained by solving two Riccati-like difference equations, and the proposed filtering algorithm is of a recursive form which is suitable for online applications. Finally, an illustrative example is provided to demonstrate the feasibility of the developed filtering approach.

Recursive approach to networked fault estimation with packet dropouts and randomly occurring uncertainties

In this paper, we discuss the fault estimation problem for a class of time-varying networked systems in the simultaneous presence of randomly occurring uncertainties, stochastic nonlinearities and packet dropouts. The phenomena of the randomly occurring uncertainties and packet dropouts are characterized by utilizing mutually independent random variables with known occurrence probabilities. The stochastic nonlinearities are also considered which can cover many known nonlinearities as special cases. The major focus is on the design of the fault estimation algorithm such that, for all randomly occurring uncertainties, stochastic nonlinearities and packet dropouts, an optimized upper bound of the estimation error covariance is derived at each time step and the explicit form of the estimator gain is provided. As a by-product, the unknown system state is estimated simultaneously. It should be noted that a new compensation scheme is introduced to improve the estimation performance by properly using the statistical property of the imperfect measurements. In addition, the monotonicity of the trace of such an optimal upper bound with respect to the missing probability is revealed from theoretical perspective. Finally, the usefulness of the proposed estimation compensation scheme is demonstrated by a simulation example. HighlightsThe addressed system includes network-induced uncertainties and packet dropouts.A new compensation scheme is given by using the statistical information of the packet dropouts.The proposed fault estimation method is capable of estimating the system state as a by-product.The developed fault estimation algorithm is of a recursive form suitable for online applications.

Reliable guaranteed-cost control for networked systems with randomly occurring actuator failures and fading performance output

This paper is concerned with the reliable guaranteed-cost control problem for a class of time-varying networked systems with randomly occurring actuator failures and fading performance output. The randomly occurring actuator failure, which describes the phenomenon of the actuator failure appearing in a random way, is modelled by a Bernoulli distributed white sequence with a known conditional probability. The fading performance output is characterized by a random variable obeying any discrete-time probability distribution over a known interval. The main purpose is to design a reliable controller over a given finite horizon such that, an optimized upper bound of the predefined quadratic performance index is guaranteed for the addressed systems in the presence of both the randomly occurring actuator failures and the fading performance output. It is shown that the desired controller gain can be obtained in terms of the solution to a Riccati-like difference equation. A numerical example is provided to illustrate the feasibility and effectiveness of the proposed control scheme.

Coordination of a supply chain with consumer return under vendor-managed consignment inventory and stochastic demand

In this paper, the problem of the coordination policy is investigated for vendor-managed consignment inventory supply chain subject to consumer return. Here, the market demand is assumed to be affected by promotional effort and consumer return policy. The optimal consignment inventory and the optimal promotional effort level are proposed under the decentralized and centralized decisions. Based on the optimal decision conditions, the markdown allowance-promotional cost-sharing contract is investigated to coordinate the supply chain. Subsequently, the comparison between the two extreme policies shows that full-refund policy dominates the no-return policy when the returning cost and the positive effect of return policy are satisfied certain conditions. Finally, a numerical example is provided to illustrate the impacts of consumer return policy on the coordination contract and optimal profit as well as the effectiveness of the proposed supply chain decision.

A resilience approach to state estimation for discrete neural networks subject to multiple missing measurements and mixed time-delays

Abstract In this paper, the resilient state estimation problem is investigated for a class of discrete recurrent neural networks (RNNs) subject to mixed time-delays, missing measurements and stochastic disturbance. The mixed time-delays consist of randomly occurring time-delay and distributed sensor delays, where a random variable obeying the Bernoulli distribution is employed to characterize the phenomenon of randomly occurring time-delay. In addition, the phenomena of the multiple missing measurements are characterized by introducing a set of mutually independent random variables, which reflect that each sensor could have individual missing probability. Meanwhile, the additive variation of the estimator gain is considered to reflect the possible parameter deviations when implementing the state estimation algorithm. Our main purpose is to design a resilient state estimator such that, in the presence of multiple missing measurements, randomly occurring time-delay and distributed sensor delays, the estimation error dynamics is exponentially stable in the mean square. A sufficient condition is established to guarantee the existence of the resilient state estimator and the explicit expression of the desired estimator gain is given based on the solutions to some matrix inequalities. Finally, we use a numerical example to verify the validity of the presented resilient state estimation method.

Event-triggered resilient filtering with stochastic uncertainties and successive packet dropouts via variance-constrained approach

Abstract In this paper, we discuss the event-triggered resilient filtering problem for a class of time-varying systems subject to stochastic uncertainties and successive packet dropouts. The event-triggered mechanism is employed with hope to reduce the communication burden and save network resources. The stochastic uncertainties are considered to describe the modelling errors and the phenomenon of successive packet dropouts is characterized by a random variable obeying the Bernoulli distribution. The aim of the paper is to provide a resilient event-based filtering approach for addressed time-varying systems such that, for all stochastic uncertainties, successive packet dropouts and filter gain perturbation, an optimized upper bound of the filtering error covariance is obtained by designing the filter gain. Finally, simulations are provided to demonstrate the effectiveness of the proposed robust optimal filtering strategy.

Distributed variance-constrained robust filtering with randomly occurring nonlinearities and missing measurements over sensor networks

Abstract This paper is concerned with the distributed variance-constrained robust filtering problem for a class of time-varying stochastic systems subject to both randomly occurring nonlinearities and missing measurements. The target plant is disturbed by the multiplicative noises, randomly occurring nonlinearities as well as additive noises. The phenomena of the randomly occurring nonlinearities and missing measurements are modeled by the Bernoulli distributed random variables with known occurrence probabilities. The available measurements of each sensor node and its neighbor nodes can be communicated based on the network topology structure. Attention is focused on the design of a new distributed variance-constrained robust filtering algorithm such that, in the simultaneous presence of the missing measurements, multiplicative noises and randomly occurring nonlinearities, an upper bound of the filtering error covariance is obtained via the solutions to two recursive matrix equations. Subsequently, the filter parameters are designed to minimize the obtained upper bound of the filtering error covariance. Furthermore, by utilizing the mathematical induction method, a sufficient condition is provided to guarantee the boundedness of the upper bound of the filtering error covariance. At last, we provide a numerical simulation to illustrate the effectiveness of distributed variance-constrained robust filtering method.

Robust state estimation for delayed genetic regulatory networks using sampled-data

In this paper, we investigate the robust state estimation problem for a class of genetic regulatory networks (GRNs) subject to time-varying delays and parameter uncertainties via utilizing sampled-data method. By substituting the continuous measurements, we use the sampled measurements to estimate the concentrations of mRNAs and proteins. Based on the extended Wirtinger inequality, a new discontinuous Lyapunov functional is constructed. Via using Jensen inequality and the Lower bounds theorem, a sufficient criterion is derived, which guarantees the globally robustly asymptotic stability of the augmented system. Further, the required state estimators can be designed by solving a set of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to demonstrate the effectiveness and less conservatism of the obtained results.

Finite-time H ∞ bounded estimation for memristive recurrent neural networks with randomly occurring time-delay and missing measurements

In this paper, the finite-time H∞ state estimation problem is investigated for a class of discrete-time memristive recurrent neural networks (DMRNNs) subject to randomly occurring time-delay and missing measurements. Two random variables obeying the Bernoulli distribution are employed to characterize the randomly occurring time-delay and missing measurements, where the corresponding occurrence probabilities are assumed to be known. The main purpose of this paper is to design an H∞ state estimator such that the estimation error dynamics is finite-time bounded with respect to some prescribed parameters and the H∞ performance is achieved simultaneously. In view of the semi-definite programming approach, sufficient conditions are given to guarantee the existence of desired state estimator and provide the explicit form of the estimator gain. Finally, a numerical simulation example is given to verify the feasibility and effectiveness of the developed estimation approach.