This article is concerned with a recursive remote estimation problem for a class of nonlinear complex networks subject to the token bucket protocol (TBP) and amplify-and-forward (AF) relays. The influence of the TBP is considered, for the first time, in the context of networked state estimation, where the token consumptions are modeled in a stochastic manner, so as to describe the potential size variability of transmitted measurement signals. Once processed by the TBP, the signals, with stochastic channel coefficients, are transmitted to the remote estimator via AF relays, where a failure in transmission under the TBP may occur due to insufficient tokens in the bucket. An extended-Kalman-filter-based novel recursive estimator is proposed, and by solving Riccati-like difference equations, an upper bound of prediction/estimation error covariance is determined and further minimized through the design of an appropriate estimator gain. The impact of the TBP on estimation performance is also investigated. Some numerical simulations are presented to demonstrate the effectiveness of the proposed estimator and the effects of the TBP.

The prediction of remaining useful life (RUL) for aircraft engines stands as a critical process ensuring the safe operation of aircraft. Traditional methods for estimating RUL primarily mine temporal features within data, frequently overlooking the complex interdependencies among multivariate signals. Furthermore, in actual physical systems such as aircraft engines, there is a dynamic linkage among the sensor data. Ignoring these subtleties can result in a reduction of estimation accuracy. Addressing this challenge, the article proposes a novel graph attention reconcile network integrated with priori knowledge to predict aircraft engine RUL. It constructs a time-series graph by fusing sensor data with aerothermodynamics equations. The network employs dual attention mechanisms to capture spatio-temporal features: a spatial attention module to identify relationships between sensors, and a temporal attention module to highlight the importance of different time steps. Additionally, a LSTM layer is incorporated to enhance RUL prediction accuracy. Comparative analysis using the Commercial Modular Aero-Propulsion System Simulation datasets and new Commercial Modular Aero-Propulsion System Simulation datasets demonstrates the superior performance of the proposed model over other prevailing approaches.

In this article, the remote estimation problem is addressed for a class of discrete-time complex networks under the influence of probabilistic quantization and amplify-and-forward (AF) relays. The underlying complex network model, which is inherently nonlinear and stochastic, is affected by additive process and measurement noises. Owing to the limited bandwidth of the transmission channel, the measurement outputs are quantized by a probabilistic quantizer prior to transmission. To enhance the signal quality over long-distance transmissions, the quantized measurements are sent to AF relays and subsequently forwarded to the estimator. Utilizing the unscented Kalman filter approach, a novel state estimator is designed to minimize an upper bound on the estimation error covariance. Moreover, sufficient conditions are derived to ensure that the estimation error is exponentially bounded in the mean-square sense. Lastly, the efficacy of the proposed scheme is illustrated through numerical simulations.

This article investigates the issue of distributed state estimation for a nonlinear hydraulic system. Our objective is to propose a distributed algorithm that enables individual nodes to obtain a global state estimation by utilizing local measurements and information gathered from neighboring nodes. We ensure global optimality by designing a mechanism for information exchange between neighboring nodes. The state estimation acquired by our distributed algorithm is equivalent to that of the centralized extended Kalman filter within a finite time. Our proposed algorithm is effective for acyclic network graphs and can be applied to cyclic network graphs through the loop removal approach. In addition, we provide a stability analysis of the proposed distributed algorithm. Finally, experiments on the practical platform are conducted to verify the feasibility and merits of the proposed algorithm.

Efficient fault-tolerant control (FTC) is necessary for the safe operation of aero-engine control system. In this paper, a high performance active FTC method based on zonotope for actuator fault in aero-engine control systems is proposed. Parameter uncertainties are considered to describe linearization error and identification error of system model for reducing the gap between theory and practice. Firstly, a zonotopic observer satisfying the peak-bounded index is proposed to reduce the influence of uncertainties and improve the accuracy of fault estimation. Moreover, with the aid of the zonotopic observer, the range of the sliding surface affected by the estimation errors and model uncertainties can be evaluated, and the dynamic quasi-sliding mode domain (QSMD) can be obtained. As a result, the dynamic QSMD can help design the parameters of the sliding mode fault-tolerant controller, ensure the stability and convergence of the entire closed-loop control system. Meanwhile, the conservative problem caused by manual parameters setting is avoided. Finally, the feasibility of the proposed method is verified by the aero-engine Hardware-in-the-loop (HIL) experiment platform.

This article is concerned with the stabilization problem of switched Takagi–Sugeno fuzzy systems via the stabilizing switching behavior. Different from the situations considered in the existing literature, the stabilizing-switching-dependent average dwell-time method is applied to break through the limitation of the noncoexistence of dwell-time-dependent and dwell-time-independent destabilizing switching behavior. Then, the stability criteria are established by balancing the state divergence produced from the unstable mode and destabilizing switching behavior through the stabilizing switching behavior. The obtained results are suitable for the situation where all modes are unstable and applied to the design of the state feedback controller. The designed controller relaxes the stabilization assumption for the mode of the fuzzy system. Finally, the simulation illustrates the feasibility and superiority of the proposed method.

This article investigates the exponential stability of generalized neural networks (GNNs) with a time-varying delay. Different from the literatures on the similar topic, the considered time delay contains a few intermittent large-delay periods (LDPs). A new approach is proposed to determine how frequent and how long the LDPs are allowed for guaranteeing the exponential stability by using switching techniques. The GNN is first modeled as a switched time-delay system which may include an unstable subsystem. Then, based on a novel Lyapunov–Krasovskii functional with LDP-based terms, a delay-dependent exponential stability criterion and associated evaluation algorithm are developed. Finally, two numerical examples are provided to show the effectiveness of the proposed method.

In this paper, the stability problem of delayed neural networks is investigated by adopting some switching methods. First, the delay interval is divided into many smaller variable intervals, and when the smaller variable interval is regarded as a mode of the delay, delayed neural networks are modeled as switched systems. Then, by using some switching methods, less conservative stability criteria are derived to ensure the stability of delayed neural networks. Finally, one example is provided to show the effectiveness of the obtained criteria.

This paper investigates the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems with unstable inverse dynamics via time-dependent switching. Adaptive backstepping technique and fast average dwell time switching are exploited to set up the adaptive fuzzy control scheme for the systems under consideration. It is shown that the designed controllers and switching signals can ensure that all the signals remain bounded and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed design method.

Science of Neural Networks, and even much more so computing applications, have undergone developments beyond any predictions since McCullock-Pitts artificial neuron (1943) up via Hopfield's neurons (1982, 1984) to Kasabov spiking-neurons neucube (2014) and evolving connectionist systems (2003). Still computational functionality of all kinds of neural network implies guaranteed operating steady-state equilibrium is fast-reached first. On the other side of this spectrum Science of Neurophysiology yielded insights converging to Systems Biology approach Gayton-Hall (2006). It appeared, on the crossroad of these findings with Kolmogorov's representation superposition and Hilbert's Thirteen problem certain rater delicate subtle issues emerged Sprecher (2017). This paper gives one perception of these issues and suggested a revised view on the foundations of past developments, possibly by re-thinking own stability results for recurrent neural networks which possess time-varying delays.

This paper deals with the problem of guaranteed cost robust control for a class of uncertain switched delay systems composed of controllable and uncontrollable subsystems. By making use of the scheme of average dwell time and under the condition that the activation time ratio between controllable subsystems and uncontrollable ones is not less than a specified constant, the state feedback controllers are derived in terms of linear matrix inequalities (LMIs) such that the resulting closed-loop system is exponentially stable for all admissible uncertainties and has a weighted guaranteed cost upper bound. As an example, the controller design for an AV-8A Harrier VTOL aircraft illustrates the effectiveness of the proposed approach.