Distributed Filtering for Semi-Markov-Type Sensor Networks With Hybrid Sojourn-Time Distributions-A Nonmonotonic Approach

On Ordered Weighted Averaging Operator and Monotone Takagi-Sugeno-Kang Fuzzy Inference Systems

The necessary and/or sufficient conditions for a Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) to be monotone has been a key research direction in the last two decades. In this article, we first define fuzzy membership functions (FMFs) with single and continuous support; and consider TSK-FIS with a grid partition strategy for computing its firing strengths with product T-norm (here after denoted as TSK-FIS-product). We also define a more general joint necessary condition, whereby each constituent itself is a necessary condition for the TSK-FIS-product model. The first necessary condition indicates that the normalized firing strength must not be indeterminate (i.e., 0/0), i.e., susceptible to the tomato classification problem. The second necessary condition indicates that all restricted consequents of fuzzy if-then rules must be defined. Based on the principle of the ordered weighted averaging (OWA) operator as well as the concept of increasing orness in OWA and hyperboxes, a general joint sufficient condition for a TSK-FIS-product model to be monotone is derived. Three case studies of the developed methods for undertaking failure mode and effect analysis (FMEA) and image processing tasks are presented. The results are compared, analyzed, and discussed, demonstrating the usefulness of our developed methods.

Neural Network-Based Sliding Mode Control for Semi-Markov Jumping Systems With Singular Perturbation

The primary focus of this article centers around the application of sliding mode control (SMC) to semi-Markov jumping systems, incorporating a dynamic event-triggered protocol (ETP) and singular perturbation. The underlying semi-Markov singularly perturbed systems (SMSPSs) exhibit mode switching behavior governed by a semi-Markov process, wherein the variation of this process is regulated by a deterministic switching signal. To simultaneously reduce the triggering rate and uphold the system performance, a novel parameter-based dynamic ETP is established. This protocol incorporates weight estimation of a radial basis function neural network (RBFNN) and introduces two internal dynamic variables. Following the Lyapunov's theory, sufficient criteria are established for ensuring the mean-square exponential stability of the resulting system. Additionally, an SMC scheme based on the convergence factor is designed to fulfill reachability conditions. Finally, two examples are carried out to validate the solvability and applicability of the attained control methodology.

Two Novel Noise-Suppression Projection Neural Networks With Fixed-Time Convergence for Variational Inequalities and Applications

This article proposes two novel projection neural networks (PNNs) with fixed-time ( ) convergence to deal with variational inequality problems (VIPs). The remarkable features of the proposed PNNs are convergence and more accurate upper bounds for arbitrary initial conditions. The robustness of the proposed PNNs under bounded noises is further studied. In addition, the proposed PNNs are applied to deal with absolute value equations (AVEs), noncooperative games, and sparse signal reconstruction problems (SSRPs). The upper bounds of the settling time for the proposed PNNs are tighter than the bounds in the existing neural networks. The effectiveness and advantages of the proposed PNNs are confirmed by numerical examples.

A Hybrid Data Preprocessing-Based Hierarchical Attention BiLSTM Network for Remaining Useful Life Prediction of Spacecraft Lithium-Ion Batteries

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Adaptive Neural Sliding-Mode Control for Fuzzy Singularly Perturbed Systems: Sojourn-Probability-Based Stochastic Communication Protocol

This article addresses an adaptive neural network (NN) sliding-mode control (SMC) strategy for fuzzy singularly perturbed systems against unrestricted deception attacks and stochastic communication protocol (SCP). Instead of relying on the traditional transition probability, a sojourn-probability-based SCP is efficiently established to characterize the stochastic nature more precisely. In response to unrestricted deception attacks, an NN-based technique is deployed to estimate and counteract their detrimental impacts on system performance. Moreover, the design of the sliding-mode controller integrates the singular perturbation parameter and fuzzy rules, addressing the challenge of imperfect premise matching. The proposed controller guarantees exponential ultimate boundedness in the mean square sense and ensures the reachability of the specified sliding surface for the closed-loop system. Finally, the efficacy of the proposed theoretical framework is validated through two illustrative examples, confirming its practical applicability and robustness.

Asynchronous Stabilization for Two Classes of Stochastic Switching Systems with Applications on Servo Motors

This paper addresses the asynchronous stabilization problem of two typical stochastic switching systems, i.e., dual switching systems and semi-Markov jump systems. By dual switching, it means that the systems contain both deterministic and stochastic switching dynamics. New stability criteria are firstly proposed for these two switched systems, which can well handle the asynchronous phenomenon. The conditional expectation of Lyapunov functions is allowed to increase during some unmatched interval to reduce the conservatism. Next, we present numerically testable asynchronous controller design methods for the dual switching systems. The proposed method is suitable for the situation where the asynchronous modes come from both inaccurate mode detection and time varying delay. Meanwhile, the transition probabilities are both uncertain and partly accessible. Finally, novel asynchronous controller design methods are proposed for the semi-Markov jump systems. The sojourn time of the semi-Markov jump systems can have both lower and upper bounds, which could be more practical than previous scenarios. Examples are utilized to demonstrate the effectiveness of the proposed methods.