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Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results, there are still some challenges. For example, the spatial features and importance differences hidden in the raw monitoring data are not sufficiently addressed or highlighted. In this paper, a novel multi-head self-Attention fully convolutional network (MSA-FCN) is proposed for predicting the RUL of turbofan engines. MSA-FCN combines a fully convolutional network and multi-head structure, focusing on the degradation correlation among various components of the engine and extracting spatially characteristic degradation representations. Furthermore, by introducing dual multi-head self-attention modules, MSA-FCN can capture the differential contributions of sensor data and extracted degradation representations to RUL prediction, emphasizing key data and representations. The experimental results on the C-MAPSS dataset demonstrate that, under various operating conditions and failure modes, MSA-FCN can effectively predict the RUL of turbofan engines. Compared with 11 mainstream deep neural networks, MSA-FCN achieves competitive advantages in terms of both accuracy and timeliness for RUL prediction, delivering more accurate and reliable forecasts. [ABSTRACT FROM AUTHOR]

In recent decades, fault diagnosis methods based on machine learning and deep learning have achieved excellent results in fault diagnosis and are characterized by powerful automatic feature extraction and accurate identification capabilities. In many real-world scenarios, gathering enough samples of each fault type can be time-consuming and difficult. The scarcity of samples may significantly degrade the performance of these learning-based methods, making it extremely challenging to train a robust fault diagnosis classifier. In this paper, a few-shot fault diagnosis method based on the improved meta-relation network (IMRN) model is proposed to overcome the challenge of implementing fault diagnosis with limited data samples. First, a multiscale feature encoder module that utilizes two one-dimensional convolutional neural networks with different kernel sizes is used to automatically extract signal features from the original support dataset and query dataset. Then, a metric meta-learner module is designed to obtain relation scores between support samples and query samples. Finally, the feature vector output by the feature encoder module is input to the metric meta-learner module to determine the category of query samples by comparing the relation scores between the query dataset and support dataset, thus implementing the classification of fault categories. Experiments are conducted on three public datasets (TE, PU and CWRU), and the experimental results show that the proposed method outperforms other benchmark few-shot learning methods in terms of accuracy and exhibits remarkable robustness and adaptability in fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we examine the multi-sensor multi-target, bearings-only tracking problem with signal time delay. This problem is challenging due to the system incomplete observability and the signal time delay caused by the signal propagation. A new Gaussian Sum Shifted Rayleigh Filter (GS-SRF-N) is presented for the signal time delay by using parameter estimation method in Gaussian Sum Shifted Rayleigh Filter (GS-SRF). Furthermore, a forward forecasting method is proposed to solve the time-offset between the sensors due to the signal time delay. The simulation shows that the proposed method is superior to other nonlinear filters in signal propagation delay environment. [ABSTRACT FROM AUTHOR]

As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, wind speed is a nonlinear and nonstationary system, and traditional statistical methods and classical intelligent algorithms struggle to cope with dynamically updating operating conditions based on sampled data. Therefore, from the perspective of optimizing intelligent algorithms, a wind speed prediction model for wind farms was researched. In this study, we propose the Deterministic Broad Learning System (DBLS) algorithm for wind farm wind speed prediction. It effectively addresses the issues of data saturation and local minima that often occur in continuous-time system modeling. To adapt to the continuous updating of sample data, we improve the sample input of the Broad Learning System (BLS) by using a fixed-width input. When new samples are added, an equivalent number of old samples is removed to maintain the same input width, ensuring the feature capture capability of the model. Additionally, we construct a dataset of wind speed samples from 10 wind farms in Gansu Province, China. Based on this dataset, we conducted comparative experiments between the DBLS and other algorithms such as Random Forest (RF), Support Vector Regression (SVR), Extreme Learning Machines (ELM), and BLS. The comparison analysis of different algorithms was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among them, the DBLS algorithm exhibited the best performance. The RMSE of the DBLS ranged from 0.762 m/s to 0.776 m/s, and the MAPE of the DBLS ranged from 0.138 to 0.149. [ABSTRACT FROM AUTHOR]

This article is devoted to the state estimation issue under probability constraint for a class of time‐varying stochastic complex networks with uncertain inner couplings and cyber‐attacks. To save the communication energy, the dynamic event‐triggered strategy is deployed in each individual sensor‐to‐estimator channel to govern the data transmissions. A multi‐attack model incorporating two different deception signals is considered to describe the phenomenon of randomly occurring cyber‐attacks. The saturation‐type function is involved to reduce the possible innovation outliers resulting from the hostile deception signals. A sufficient condition is first developed to guarantee the probability H∞$$ {H}_{\infty } $$ performance constraint on estimation error dynamics over certain finite horizon, and then the suitable state estimator gains are derived by means of a set of matrix inequalities. Subsequently, a recursive algorithm is put forward for the addressed finite‐horizon state estimator design problem. Finally, the validity of the proposed state estimation scheme is testified by a numerical example. [ABSTRACT FROM AUTHOR]

This article presents an effective method to address the tracking control problem arising in uncertain switched high-order nonlinear system in strict-feedback form. The system under consideration contains unknown functions, which causally make the asymptotic tracking performance difficult to be achieved. By adopting the adding a power integrator approach in the framework of backstepping, a novel tracking controller is developed to guarantee an asymptotic tracking performance in the presence of the approximation error cased by neural networks (NNs) under arbitrary switching. The main contributions lie in: 1) the article for the first time embeds the backstepping technique in designing a kind of discontinuous controller with proportional integral (PI) compensation and 2) with the help of Filippov’s theory, a new defined system described by differential inclusions can be first obtained by taking some transformations, and then a novel nonsmooth Lyapunov function approach along with its upper right Dini derivative technique is applied to complete the construction of the discontinuous controller. Finally, two simulation examples are exhibited to verify the validity of the proposed design techniques. [ABSTRACT FROM AUTHOR]

The problem of distributed event-triggered H ∞ filtering for semi-Markov jump systems is investigated in this paper. The system measurements are collected through a sensor network subject to quantization and random cyber-attacks. Taking the influence of event-triggered scheme, quantization, and cyber-attacks into account, a distributed event-triggered filtering error system is established. A distributed H ∞ filter design scheme is outlined by explicitly characterizing the filter gains through some matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the designed distributed event-triggered H ∞ filters. [ABSTRACT FROM AUTHOR]

Summary: In this article, the adaptive tracking control issue is addressed for uncertain switched nonstrict‐feedback nonlinear systems with unknown time‐varying delay and backlash‐like hysteresis under neural networks (NNs) estimation framework. In contrast to most existing results of nonlinear systems with time‐delay, the time‐varying delay we consider depends entirely on the number of switching subsystems. Based on the inherent properties of radial basis function NNs, the nonstrict‐feedback structure is effectively compensated. Meanwhile, with the help of the combination of Razumikhin function method, adaptive backstepping scheme, and dynamic surface control approach, the quadratic common Lyapunov function is established for all subsystems, and then an adaptive common control strategy is developed. For arbitrary switching signals, it is shown that all the signals in the closed‐loop system can be remained bounded and the system output can follow a desired signal under the designed controller. Finally, the validity of the presented strategy is verified via two simulation examples. [ABSTRACT FROM AUTHOR]

During decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This article proposes two smart train operation (STO) algorithms by integrating the expert knowledge with reinforcement learning algorithms. Compared with previous works, the proposed algorithms can realize the control of continuous action for the subway system and optimize multiple critical objectives without using an offline speed profile. First, through learning historical data of experienced subway drivers, we extract the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality, and the safety of the subway system. Then we develop two algorithms for optimizing the energy efficiency of train operation. One is the STO algorithm based on deep deterministic policy gradient named (STOD) and the other is the STO algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data from the Yizhuang Line of the Beijing Subway and illustrate that the developed STO algorithm are better than expert manual driving and existing ATO algorithms in terms of energy efficiency. Moreover, STOD and STON can adapt to different trip times and different resistance conditions. [ABSTRACT FROM AUTHOR]

Applying an event-triggered protocol, this paper proposes a distributed H∞ filter design for nonlinear perturbed systems under fading measurements with random topology. Nonlinearities in this system obey the one-sided Lipschitz constraint, which embraces the conventional Lipschitz condition as a special case. The sensor network allows random variations of the interconnection strengths between adjacent nodes, and the connection coefficient is determined as the product of a constant and a stochastic variable with a known probabilistic feature. To reduce the unnecessary data transmission and efficiently use the limited bandwidth, the transmissions are orchestrated by an event-triggered regulating strategy. A stochastic bounded real lemma is established for the resulting error dynamics. Based on the presented matrix decomposition, which removes the direct coupling between the statistical information of interconnection strengths and the filter gain, the distributed H∞ filter gain can be explicitly expressed and easily solved. The usefulness of the theoretical method is demonstrated in a simulation study. [ABSTRACT FROM AUTHOR]

This article aims to design an optimization-based controller for a discrete-time Dubins vehicle to approach a target with unknown position as fast as possible by only using bearing measurements. To this end, we propose a bi-objective optimization problem, which jointly considers the performance of estimating the unknown target position and controlling the mobile vehicle to a known position, and then adopt a weighted sum method with normalization to solve it. The controller is given based on the solution of the optimization problem in ties with a least-square estimate of the target position. Moreover, the controller does not need the vehicle’s global position information. Finally, the simulation results are included to validate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This brief studies the set stabilization and optimal control problem of switched multi-valued logical control networks (SMLCNs) with state-dependent switching signals. Firstly, based on the algebraic expression of SMLCNs, a sufficient and necessary condition for the set stabilization of SMLCNs is given. Then by using the principle of Dijkstra algorithm in graph theory, the Lagrange-type optimization problem for the set stabilization of SMLCNs is considered, and an algorithm for the design of optimal control sequence is presented, by which the state of the SMLCN can transfer to the given set with the minimal cost. Finally, a numerical example is given to demonstrate validity and effectiveness of the obtained results, in where we can see that by using the above results, one can not only judge the set stabilization of SMLCNs but also obtain an optimal control sequence on the premise that the system is set stabilization. [ABSTRACT FROM AUTHOR]

This paper is concerned with the H ∞ filtering problem for discrete-time Markov jump systems with repeated scalar nonlinearities in an event-triggered communication scheme. An event-triggered scheme is proposed to determine whether the current data should be transmitted to the filter or not. Based on the time interval analysis approach, the discrete-time Markov jump system with network-induced delays under event-triggered communication scheme is converted into the discrete-time Markov jump time-delay system. By employing the positive-definite diagonally-dominant-type Lyapunov function technique, criteria are derived to guarantee the system is stochastically stable with a prescribed H ∞ performance level. The correspondent filter and the event-triggered parameters are also solved. Finally, a numerical example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The recent correntropy criterion based semi-supervised random neural network extreme learning machine (RC-SSELM) achieved outstanding performance in dealing with datasets with large outliers and non-Gaussian noises. To further improve the effectiveness and flexibility of the algorithm in combating large and complex outliers, we explore a more effective semi-supervised ELM data learning algorithm with the robust maximum mixture correntropy criterion (MMCC) based optimization scheme in this brief. Meanwhile, the generalized correntropy criterion kernel function with the variable kernel center is applied to MMCC, and the resultant novel semi-supervised learning algorithm is abbreviated as MC-SSELM $^{vc}$. The fixed-point iteration learning algorithm is adopted for the output weight optimization of MC-SSELM $^{vc}$. Experiment conducted on many benchmark datasets are given to show the effectiveness of MC-SSELM $^{vc}$ and comparisons to several state-of-the-art semi-supervised learning algorithms are provided for the superiority demonstration. [ABSTRACT FROM AUTHOR]

The problems of stability and stabilisation for switched systems with two subsystems are investigated in both nonlinear and linear cases. By proposing an improved average dwell time (ADT) scheme, some new stability conditions for the considered system are given. Those new conditions are more practical in practice than the existing ones with given conditions, and both mode-dependent ADT (MDADT) scheme and classical ADT scheme can be regarded as some special cases of the new scheme of this paper. Finally, a numerical example with comparisons to the previous results is included to show the validity of the developed techniques. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-based finite-time H ∞ control problem for a class of networked state-saturated switched systems subject to time delays, nonlinear perturbations and exogenous disturbances. The event-triggered transmission protocol is introduced to schedule the data transmission and so the communication network burdens can be mitigated. The state saturation restraints can be removed by means of transforming the original model into a new polyhedron system via a convex hull description approach. By considering two coupling cases between the mode switching signal and the event triggering signal, the sufficient finite-time H ∞ performance analysis criterion is provided based on average dwell time (ADT) analysis technique. Then, an event-triggered state-feedback controller is designed to ensure that the closed-loop system is finite-time bounded and satisfies the specified H ∞ disturbance attenuation performance. The desired controller can be obtained by solving a set of convex optimisation problems. Finally, a numerical example shows the usefulness of the theoretical results. [ABSTRACT FROM AUTHOR]

The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R 2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R 2 of 0.8114 in continuous synchronous movement of the shoulder and elbow. [ABSTRACT FROM AUTHOR]

This paper focuses on the passive control problem for Markov jump systems with piecewise-constant transition rates, random nonlinearities and random communication delays. An observer is used to obtain the system state, and communication delay with probabilistic distribution is considered. The probabilistic properties of the nonlinear perturbations and communication delays are described by two mutually independent Bernoulli sequences. Based on average dwell time method and Lyapunov stability theory, a sufficient stochastic passivity condition is presented. By singular value decomposition technique the gain matrices can be computed via strict linear matrix inequalities, and the desired controller guarantees the stochastic passivity of the closed-loop augmented system. The effectiveness of the method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of mean-square finite-time passivity of discrete-time stochastic Markov jump neural networks with distributed delays and sensor nonlinearities. The distributed delays and sensor nonlinearities are randomly varying and described by mode-dependent random sequences with known statistical information. The mean-square finite-time boundedness and mean-square finite-time passivity results are obtained based on Lyapunov-like functional method. And the mean-square finite-time passivity is also considered for neural networks with random infinite-distributed delays. Finally, a numerical example verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper has proposed a robust control for the spacecraft rendezvous system by considering the parameter uncertainties and actuator unsymmetrical saturation based on the discrete gain scheduling approach. By changing of variables, we transform the actuator unsymmetrical saturation control problem into a symmetrical one. The main advantage of the proposed method is improving the dynamic performance of the closed-loop system with a region of attraction as large as possible. By the Lyapunov approach and the scheduling technology, the existence conditions for the admissible controller are formulated in the form of linear matrix inequalities. The numerical simulation illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the sliding mode control for a class of uncertain discrete-time singular system with H∞ performance constraint is studied. By taking the singular matrix E into consideration, a new type of integral sliding mode surface is firstly introduced, based on which a sufficient condition is derived to guarantee the sliding mode dynamics admissible with a given γ-level disturbance attenuation of the unmatched disturbance. A controller law is also given to keep the system trajectory staying in a neighborhood of the ideal sliding surface. Finally, a numerical example is given to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Underground pipelines suffered severe external breakage caused by excavation devices due to arbitral road excavation. Acoustic signal-based recognition has recently shown effectiveness in underground pipeline network surveillance. However, merely relying on recognition may lead to a high false alarm rate. The reason is that underground pipelines are generally paved along a fixed direction and excavations out of the region also trigger the surveillance system. To enhance the reliability of the surveillance system, the direction-of-arrival (DOA) estimation of target sources is combined into the recognition algorithm to reduce false detections in this paper. Two hybrid recognition algorithms are developed. The first one employs extreme learning machine (ELM) for acoustic recognition followed by a focusing matrix-based multiple signal classification algorithm (ELM-MUSIC) for DOA estimation. The second introduces a decision matrix (DM) to characterize the statistic distribution of results obtained by ELM-MUSIC. Real acoustic signals collected by a cross-layer sensor array are conducted for performance comparison. Four representative excavation devices working in a metro construction site are used to generate the signal. Multiple scenarios of the experiments are designed. Comparisons show that the proposed ELM-MUSIC and DM algorithms outperform the conventional focusing matrix based MUSIC (F-MUSIC). In addition, the improved DM method is capable of localizing multiple devices working in order. Two hybrid acoustic signal recognition and source direction estimation algorithms are developed for excavation device classification in this paper. The novel recognition combining DOA estimation scheme can work efficiently for underground pipeline network protection in the real-world complex environment. [ABSTRACT FROM AUTHOR]

In this paper, a reliable robust discrete gain scheduling controller is designed based on the linear matrix inequalities (LMIs) and gain scheduling technology for the systems with the input saturation, system uncertainty, external disturbance and actuator failures. By the designed discrete gain scheduling controller, we can use a series of nesting ellipsoid invariant sets to strengthen the disturbance attenuation ability as strong as possible. It means that the innermost invariant ellipsoid set needs to be minimized to strengthen the disturbance attenuation ability. The dynamic performance of the closed-loop system is improved by introducing a parameter. By the Lyapunov approach, the existing conditions for the admissible controller can be formulated in the form of LMIs. The numerical simulation illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the sliding mode control problem for networked control systems, which are influenced by the non-ideal network environment, such as network-induced delays, packet dropouts and quantization errors. The states of the system are assumed to be unavailable, and an observer is designed to estimate the state of the system, based on which a sliding mode controller is given to guarantee the closed-loop system to be stable. Furthermore, it is shown that the proposed control scheme ensures the reachability of the sliding surfaces in both the state estimate space and the estimation error space. Finally, a numerical example is given to illustrated the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Evidence filtering is a promising approach to infer the 'frequency' characteristics of various events of interest from temporally and spatially distributed sensor data based on Dempster-Shafer evidence theory. The design of evidence filters has several challenges due to the nonnegativity of the filters' coefficients. This paper presents an iterative constrained minimax method for the magnitude response design of finite impulse response evidence filters with a prescribed transition-band magnitude drop. The method iteratively converts the problem into a sequence of minimax magnitude error subproblems of the evidence filters with given minimum stopband attenuations. The convergence of the global solutions of these subproblems to the global solution of the original problem is established. By using a recently published algorithm to solve the subproblems, the proposed method has obtained better magnitude responses than existing methods in design examples of this paper. [ABSTRACT FROM AUTHOR]

This paper concerns the stability analysis for singular systems with time-varying delay and nonlinear perturbations. Two cases of time-varying delay, which is differentiable (Case 1) or not differentiable (Case 2), are considered. The considered nonlinear perturbations includes the norm-bounded uncertainties as a special case. Some delay-dependent stability criteria are derived by using a delay decomposition approach. In the delay decomposition approach, the entire delay interval is divided into multiple sub-intervals for which different energy functions are defined for building new Lyapunov–Krasovskii functional. Some numerical and practical examples are given to show the effectiveness and significant improvement of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes an enhanced model predictive control (MPC) using a new state space structure for temperature control of an industrial coke furnace. The advantage of the proposed controller lies in the fact that its implementation only requires a simple step-response process model, whereas controller design can be based on state space formulation to improve temperature regulation. To ensure control performance effectiveness under model/process mismatch and uncertainties, model predictions and the cost function optimization are done on the basis of a new improved state space model. The proposed MPC is applied to an industrial coke furnace, where the outlet temperature in the radiation room is regulated. Simulation comparisons with traditional state space MPC are illustrated first. Then experimental results are shown in comparison with the original proportional-integral differential (PID) controller, demonstrating the effectiveness of the proposed methodology. [ABSTRACT FROM PUBLISHER]