This article investigates the resilient security control problem of nonlinear network systems under randomly activated deception attacks and a two-phase data transfer mechanism (TP-DTM). Initially, in order to solve the problem of high conservatism caused by the one-order free-weighting matrix (OFM) method in previous research results, this study introduces a high-order multi-mode switching free-weighting matrix (HMSFM) mechanism to establish free-weighting matrix groups under various switching modes, At the same time, sometime-varying balance matrices (TVBMs) are proposed to cooperate with HMSFM to collect proprietary features about each switching mode. Furthermore, in the context of the system subjected to deception attacks, a TP-DTM employing adaptive event-triggered mechanism (AETM) and round robin (RR) protocol is proposed to alleviate the impact of limited communication resources. Then, considering the above factors, the asymptotic stability of the augmented fuzzy system can be guaranteed under the given sufficient criteria. Consequently, a numerical example and a tunnel diode circuit example are executed to verify the advancement of the developed results. Note to Practitioners—The motivation of this paper comes from the problem that nonlinear networked control systems are vulnerable to malicious network attacks in the actual environment. In particular, malicious attacks will bring security risks such as data tampering and functional damage to the actual communication circuit system, and many state variables cannot be directly monitored due to objective conditions or financial constraints. Based on this, this paper proposes a HMSFM-based co-design mechanism for nonlinear NCSs under malicious attacks. The improved co-design scheme can deal with some difficult-to-measure state variables through observer-assisted tracking control. The main difficulty of this paper is how to reduce the conservatism of resilient control design. The improved co-design scheme can be adjusted according to the dynamic characteristics of the system by combining with TVBMs, making the constraints more relaxed and the state estimation more accurate.

This paper focuses on the fault-tolerant consensus problem of multi-agent systems under an encoding-decoding framework with Markovian switching topologies. Due to the influence of complex environments, the communication network changes over time, and thus, a Markov chain is introduced to describe the random topology switching. Under the stochastic characteristics and encoding-decoding framework, Lyapunov’s second method is applied for the first time instead of the original matrix analysis method, with both uniform and logarithmic quantizers considered. To obtain the unmeasurable states and the unknown sensor faults in the form of first-order differences, augmented estimation is performed, followed by encoding-transmission-reception-decoding for controller design. Under uniform quantization, the relationship between the scaling function and the parameters in the Riccati inequality is established, enabling boundedness analysis of data transmission. Under logarithmic quantization, a quantization-dependent Lyapunov function is constructed, and its rationality is rigorously proven. Finally, numerical simulations under both uniform and logarithmic quantization are provided to demonstrate the achievement of consensus and poly-quadratically stable in mean square sense.

This article proposes a new hierarchical Bayesian semantic-guided attribute transfer (HBSAT)-based data-physics dual-driven fault diagnosis (FD) method for uncrewed aerial vehicle (UAV) swarm systems with unseen faults. First, based on the designed hierarchical fault attributes of UAV swarm systems, the HBSAT is developed to progressively learn highly matched correspondences between fault features and attribute semantics from available fault samples for learning attribute knowledge and attribute-related feature representations, which can be transferred to diagnose unseen faults. Furthermore, a mathematical model of the UAV swarm system is established to generate simulated unseen fault data consistent with fault attributes, which can help the FD model to learn more features and attributes related to unseen faults and improve the diagnostic performance. Besides, the proposed network is extended into the Bayesian deep learning framework to quantify uncertainty. The validity and advantages of the proposed approach are verified based on a semiphysical platform of a fixed-wing UAV swarm system.

In industrial remaining useful life (RUL) prediction, the uncertainties can deteriorate generalization and cause low trustworthiness and accuracy of RUL prognostics results. To address this issue, a novel probabilistic multiscale Bayesian Convolutional Transformer (PMBCT) is proposed for trustworthy RUL prognostics with uncertainty quantification. Specifically, we design a Bayesian convolutional probsparse self-attention to integrate local context into global modeling and a multiscale representation learning mechanism to fuse scale-aware information, which can help the PMBCT to both globally and locally quantify uncertainty information and capture degradation features from diverse temporal scales. Moreover, to reduce the adverse effects induced by uncertainties on RUL prediction, we develop a Bayesian backpropagation training algorithm in which uncertainty information can be feedback to train the proposed model, improving its generalization. Finally, comprehensive RUL prediction experiments are carried out based on a bearings dataset for validating the effective and competitive performances of the PMBCT-based RUL prognostic approach.

In this article, a novel task search and allocation strategy is developed for heterogeneous multiagent systems with limited search range and communication constraints, which includes three processes: 1) task search; 2) task allocation; and 3) formation recovery. In order to optimize task search efficiency under communication constraints, a multigroup task search strategy is proposed by minimizing the average overlap degree between agents’ search ranges, which divides agents into multiple groups and establishes intragroup communication links. According to the communication link and group allocation results, an optimal search formation is designed for each group to maximize their individual search ranges. For transmitting information between different groups, by employing the agent with the highest communication efficiency within the discovery agent’s group as the relay agent, a communication relay strategy is proposed to transmit the task information to other groups. Then, a task allocation strategy based on communication relays is designed to achieve global task allocation by using the estimated state information of all agents. Moreover, to ensure the sustainability of task search and allocation, an intergroup scheduling strategy is proposed to recover the optimal search formation after agents complete the task-related works. Simulation results verify the effectiveness of the proposed task search and allocation strategy.

This paper considers the distributed fault-tolerant consensus for heterogeneous nonlinear multiagent systems (HNMASs) with actuator faults and full-state constraints via adaptive dynamic programming (ADP). In order to handle the state constraint problem with multiple constraint types, a unified universal barrier function is introduced to convert the original constrained system into a non-constrained system. For the purpose of improving control efficiency and guaranteeing system reliability, a novel value function including fault estimations and control inputs is established. Considering the limitation of the computation and communication resources, a simplified ADP method incorporating the dynamic event-triggered strategy is developed to learn a distributed event-triggered fault-tolerant control policy. It is strictly proven that the HNMASs’ stability and the neural network weights’ convergence are guaranteed by the Lyapunov theory in the sense of uniform ultimate boundedness. Simulations are presented to verify the proposed control policy.

In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.

In this article, a novel approach of prescribed performance synchronization control is developed for heterogeneous nonlinear multiagent systems (MASs) subject to unknown actuator faults. Considering that not all followers are able to access the information of the leader, a distributed auxiliary perception system is proposed to estimate the state information of the leader to guarantee that the estimation errors converge to zero within fixed time. Then, based on the estimated states, a prescribed performance fault-tolerant control (FTC) approach is proposed, which achieves the user-defined performance specifications even in the presence of system faults. Moreover, as accurate system dynamic models are perhaps hard to acquire in practical engineering, a data-based method is proposed by using the reinforcement learning (RL) algorithm to design the fault-tolerant controller, which only needs the off-policy online data and is independent of the model dynamics of followers. The stability and synchronization with the prescribed behavior are guaranteed through the Lyapunov stability theorem. Finally, simulation results are presented to illustrate the effectiveness of the developed controller.

This article investigates adaptive output formation tracking control of nonlinear multiagent systems with time-varying actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies. Considering the dynamic changes of communication connections in uncertain environments, a double semi-Markov process is first introduced into the leader-follower structure to describe the random switching of communication topologies. Then, a novel adaptive distributed fault-tolerant output formation tracking control framework is established using the backstepping and Nussbaum gain technique to address matched/mismatched uncertainties and disturbances, time-varying actuator faults, and unknown nonidentical control directions. In this control framework, the independent variable of the Nussbaum function is designed as a non-negative function that monotonically increases with respect to time, thereby overcoming the presence of the absolute value of its derivative in the integration process. Based on the distributed structure, an adaptive fault-tolerant controller is further proposed to achieve the asymptotic output formation tracking in mean-square sense. The stability of the closed-loop nonlinear multiagent systems is analysed through the contradiction argument and Lyapunov theorem. The simulation example verifies the effectiveness of the proposed control strategy.

This article proposes a new distributed–collaborative domain adversarial network (DCDAN)-based incipient fault diagnosis method for satellite attitude control system under variable operating conditions. The designed DCDAN contains a new distributed domain classifier and collaborative domain classifier to provide the features of incipient faults for fault classifier. In the distributed domain classifier, the designed relative importance weight between the global distribution of all data and the conditional distribution of different faults can be adaptively adjusted by the contributions of different distributions. For the collaborative domain classifier, the weight constraint factor is introduced to deal with the loss of incipient fault information in the process of network forward propagation as the increasing of network layers. The experiments in a ground semiphysical platform are carried out, and the results show that the solution achieves over 95% accuracy for incipient faults and over 98% accuracy for the total test samples.

Considering the challenge of deep mining of root causes in machine failures, a knowledge aggregation fault diagnosis (KAFD) model is proposed, in which the graph convolutional network (GCN) GraphSAGE is improved and introduced into the knowledge graph (KG)-based fault diagnosis. Historical maintenance data of machines is used to construct a fault phenomenon-FBG, which is then combined with the fault diagnosis knowledge graph (FDKG) to form a collaborative FDKG. A single-layer knowledge aggregation network (KAN) that incorporates sensitivity factors and configures different types of GCN aggregators is constructed in the proposed KAFD. Based on deep neighbor aggregation operations on collaborative FDKG, KAFD obtained by stacking multiple KANs, can capture the higher order structural information and semantic information, which results in the multihop reasoning, improvement of the rationality and diversity of fault cause tracing. The KAFD is experimentally validated through two fault diagnosis datasets, which are constructed by the maintenance data of an industrial enterprise, and the results demonstrate the excellent performance.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

This article proposes a distributed adaptive fault-tolerant formation-containment control with prescribed performance for heterogeneous multiagent systems (MASs) consisting of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. First, utilizing the neighborhood formation error information, the distributed fault-tolerant formation control strategy is developed for the trajectory dynamics of each UAV to achieve the formation tracking, that is, all UAVs track the virtual leader and perform the prespecified formation configuration. Then, the adaptive fault-tolerant containment algorithm, independent of the positions of the leaders, is proposed to guarantee the UGVs converge to the convex hull formed by the leader UAVs. The adaptive estimation scheme is constructed to compensate for the unknown system parameters and actuator loss-of-effectiveness and bias faults. The formation-containment tracking performance is analyzed based on Lyapunov theory with the synchronization errors satisfying the prescribed performance. A simulation example based on UAVs-UGVs systems is adopted to verify the effectiveness of the proposed control strategy.

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%.

In this paper, a novel adaptive fault-tolerant sliding-mode control scheme is proposed for high-speed trains, where the longitudinal dynamical model is focused, and the disturbances and actuator faults are considered. Considering the disturbances in traction force generated by the traction system, a dynamic model with actuator uncertainties modeled as input distribution matrix uncertainty is established. Then, a new sliding-mode controller with design conditions is proposed for the healthy train system, which can drive the tracking error dynamical system to a predesigned sliding surface in finite time and maintain the sliding motion on it thereafter. In order to deal with the actuator uncertainties and unknown faults simultaneously, the adaptive technique is combined with the fault-tolerant sliding-mode control design together to guarantee that the asymptotical convergence of the tracking errors is achieved. Furthermore, the proposed adaptive fault-tolerant sliding-mode control scheme is extended to the cases of the actuator uncertainties with unknown bounds and the unparameterized actuator faults. Finally, the case studies on a real train dynamic model are presented to explain the developed fault-tolerant control scheme. The simulation results show the effectiveness and feasibility of the proposed method.

This paper proposes a stator-winding incipient shorted-turn fault detection method for the traction motors used in China high-speed railways. First, a mathematical description for incipient shorted-turn faults is given from the quantitative point of view to preset the fault detectability requirement. Then, an interval sliding mode observer is proposed to deal with the uncertainties caused by measuring errors from motor speed sensors. The active robust residual generator and the corresponding passive robust threshold generator are proposed based on this particularly designed observer. Furthermore, design parameters are optimized to satisfy the fault detectability requirement. This developed technique is applied to an electrical traction motor to verify its effectiveness and practicability.

In this paper, an adaptive failure compensation problem is addressed for high-speed trains with longitudinal dynamics and traction system actuator failures. Considered the time-varying parameters of the train motion dynamics caused by time-varying friction characteristics, a new piecewise constant model is introduced to describe the longitudinal dynamics with variable parameters. For both the healthy piecewise constant system and the system with actuator failures, the adaptive controller structure and conditions are derived to achieve the plant-model matching. The adaptive laws are designed to update the adaptive controller parameters, in the presence of the system piecewise constant parameters and actuator failure parameters which are unknown. Based on Lyapunov functions, the closed-loop stability and asymptotic state tracking are proved. Simulation results on a high-speed train model are presented to illustrate the performance of the developed adaptive actuator failure compensation control scheme.

This paper is concerned with H∞filter design for a class of networked control systems (NCSs) with multiple state-delays via Takagi-Sugeno (T-S) fuzzy model. The transfer delays and packet loss which are induced by the limited bandwidth of communication networks, are considered. The focus of this paper is on the analysis and design of a full-order H∞filter such that the filtering error dynamics is stochastically stable and a prescribed H∞attenuation level is guaranteed. Sufficient conditions are established for the existence of the desired filter in terms of linear matrix inequalities (LMIs). An example is given to illustrate the effectiveness and applicability of the proposed design method.

This paper is concerned withH∞-design for a class of networked control systems (NCSs) with multiple state-delays via the Takagi-Sugeno (T-S) fuzzy model. The transfer delays and packet loss that are induced by the limited bandwidth of communication networks are considered. The focus of this paper is on the analysis and design of a full-orderH∞filter, such that the filtering-error dynamics are stochastically stable, and a prescribedH∞attenuation level is guaranteed. Sufficient conditions are established for the existence of the desired filter in terms of linear-matrix inequalities (LMIs). An example is given to illustrate the effectiveness and applicability of the proposed design method.