Delay Compensation-Based State Estimation for Time-Varying Complex Networks With Incomplete Observations and Dynamical Bias

Robust fault detection and isolation for uncertain neutral time-delay systems using a geometric approach

This paper proposes a new geometric fault detection and isolation (FDI) strategy for uncertain neutral time-delay systems (UNTDS). Firstly, the concept of unobservability subspace is extended to the considered system. Subsequently, utilizing the geometric properties of factor space and canonical projection, the fault is divided into different unobservability subspaces. Therefore, an algorithm for constructing the subspace is developed for fault isolation. Finally, a set of observers is designed for the subsystems, and generates a set of structured residuals which is sensitive only to a specific fault. Additionally, the H∞ technique is utilized to suppress the disturbances and error signals due to time-varying delays on the residual. The simulation examples verify the effectiveness of the proposed approach.

Nonasymptotic Fractional Derivative Estimation of the Pseudo-State for a Class of Fractional-Order Partial Unknown Nonlinear Systems

This work is devoted to the nonasymptotic and robust fractional derivative estimation of the pseudo-state for a class of fractional-order nonlinear systems with partial unknown terms in noisy environments. In particular, the estimation for the pseudo-state can be obtained by setting the fractional derivative's order to zero. For this purpose, the fractional derivative estimation of the pseudo-state is achieved by estimating both the initial values and the fractional derivatives of the output, thanks to the additive index law of fractional derivatives. The corresponding algorithms are established in terms of integrals by employing the classical and generalized modulating functions methods. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Moreover, error analysis in discrete noisy cases is discussed. Finally, two numerical examples are presented to verify the correctness of the theoretical results and the noise reduction efficiency.

Estimator design for complex networks with encoding decoding mechanism and buffer-aided strategy: A partial-nodes accessible case

This article focuses on the partial-nodes-based state estimation (PNBSE) issue for a complex network with the encoding-decoding mechanism (EDM) over the unreliable communication channel, where the signals are transmitted in an intermittent manner. A so-called EDM is exploited to convert the transmitted signals into a set of codewords with finite bits so as to facilitate the transmission efficiency between the complex networks and the estimator. To guarantee the state estimation (SE) performance subject to the intermittent communication nature of the channel, a buffer with limited capacity, which stores the recent measurement signals and sends them to the estimator simultaneously, is adopted to improve the utilization rate of the measurement signals in the estimation process. The main objective of the investigated problem is to construct a partial-nodes-based (PNB) estimator to generate the desired state estimates for the underlying complex networks. Considering the intermittent feature of signal transmission, the ultimate boundedness of the SE error under the constructed PNB estimator is discussed, and then, sufficient conditions are derived which ensure that the desired PNB estimator exists. An simulation example is given to confirm the correctness and effectiveness of the proposed estimator design strategy in the end.

Encoding-decoding strategy based resilient state estimation for bias-corrupted stochastic nonlinear systems

This paper is concerned with the resilient state estimation problem for a type of stochastic nonlinear systems, in which the possible dynamical bias is considered that is depicted by a dynamical equation. In pursuit of enhancing the robustness of the propagated data, a binary encoding strategy (BES) is exploited in the binary symmetric channel (BSC). While the random bit errors caused by the channel noise may take place during the propagation of the binary bit string via the memoryless BSC. To characterize the occurrence of the bit errors, a series of Bernoulli distributed random variables is adopted. More specifically, in order to deal with the possible gain fluctuation of the estimator in the execution process, a resilient state estimator is employed. This paper intends to put forward a novel resilient estimation scheme under the BES, which can assure that the estimation error dynamics is exponentially ultimately bounded in mean square. A sufficient criterion is first acquired for the existence of the expected resilient estimator and the estimator parameter is achieved by solving a convex optimization problem. Finally, an illustrative simulation example is provided to verify the validity of the theoretical results.