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- 저자 : Junhua Du (검색결과 4 건)
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Yan Gao,Junhua Du,Jun Hu,Hui Yu 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.2
This paper is concerned with the robust resilient H∞ state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrence probability is assumed to be known. In addition, during the design of the state estimation algorithm, the additive variation of the estimator gain is considered to reflect the parameter deviation that may occur during the execution. The main purpose is to design a finite-horizon resilient state estimation algorithm such that, in the presence of probabilistic quantization and estimator gain perturbation, some sufficient criteria are obtained for the estimation error system to satisfy the prescribed H∞ performance requirement within the finite-horizon and the error variance boundedness. Finally, a numerical example is conducted to verify the feasibility of the presented estimation algorithm against the probabilistic quantization and estimator gain perturbation.
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Zhibin Hu,Jun Hu,Junhua Du,Hongjian Liu,Jun Qi 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.10
This paper investigates the optimized distributed fusion filtering (DFF) problem for a class of nonlinear discrete time-varying stochastic systems with randomly occurring uncertainty (ROU) and missing measurements (MMs). The stochastic nonlinearity is depicted in terms of statistical means. The phenomena of the ROU and MMs are considered during the modelling of state equation and measurement output respectively, which are characterized by Bernoulli distributed random variables. In order to deal with the effect of the parameter uncertainty, the method that the local estimation error covariances and cross-covariances from all estimators at every sample time are replaced by their upper bounds is adopted. Moreover, the minimum upper bounds for each filtering error covariance (FEC) are obtained by designing the corresponding filter gains. Based on the local filters, a new robust DFF algorithm is developed via the matrix-weighted fusion method. In addition, a sufficient condition concerning on the performance analysis of the developed algorithm is given, which can show that the boundedness of the upper bound for each FEC is guaranteed. Finally, a numerical example is provided to manifest the usefulness of the developed distributed fusion algorithm.
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Dongyan Chen,Ning Yang,Jun Hu,Junhua Du 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.12
This article gives primarily attention to the resilient set-membership state estimation problem for a class of discrete time-varying complex networks with distributed delays, uncertain inner coupling and sensor saturation under the round-robin (RR) protocol. The process and measurement noises are unknown but bounded, which are confined to the ellipsoidal sets. The RR protocol is utilized to assign the priority of accessing to the communication network to nodes in terms of a fixed circular order. By means of the recursive linear matrix inequality (RLMI) technique, a sufficient criterion is established to guarantee that the one-step ahead estimation error is confined to the ellipsoidal set. In term of the convex optimization approach and the recursive algorithm, the desired estimator gains can be derived by utilizing the sufficient condition and optimizing the corresponding constraint matrices. Finally, a simulation example is used to verify the effectiveness of the designed resilient set membership state estimation scheme.
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Yu Yang,Jun Hu,Dongyan Chen,Yunliang Wei,Junhua Du 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.7
This paper is concerned with the non-fragile protocol-based set-membership estimation problem for a class of discrete memristive neural networks (MNNs) with mixed time-delays, quantization and unknown but bounded noises. The nonlinear neural activation function satisfies the sector-bounded condition and the logarithmic quantization error is transformed to the norm-bounded uncertainty. In order to save the networks resources, the maximum-error-first (MEF) protocol is introduced to allocate the utilization order of the network channel. The focus is on the design of non-fragile state estimator to ensure such that, in the simultaneous presence of the mixed time-delays, quantization errors and estimator gain perturbations, real state is confined to the ellipsoid. In particular, a minimization problem is given to determine the radius of the designed ellipsoid and the estimator gain matrix by testifying the feasibility of some recursive matrix inequalities. Finally, some simulations are used to show the feasibility of the developed non-fragile suboptimal state estimation strategy.
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