This paper presents a gradient-based fault detection method for pitch control systems in avionics. On the basis of the dynamic model of the airplane, the proposed method detects both operator and sensor faults by monitoring the online data. By integrating fault-related behaviors over an extended time window, the method effectively amplifies small changes caused by incipient faults, improving detectability. Theoretical analysis reveals that under normal flight conditions, the gradient has a zero expected value and a finite, analytically tractable variance. These characteristics make the method compatible with traditional fault detection approaches. Sufficient tests on real flight data verify its ability to detect hard-to-identify faults.

This study presents a data-based fault detection and adaptive estimation approach for multi-helicopter systems facing malicious behavior, where a helicopter intentionally disrupts the cooperation goal by modifying its controller signal. The framework integrates a Transformer-based model for identifying malicious helicopters through abnormal motion analysis, followed by an adaptive estimation mechanism to handle unpredictable parameters. Two fault-tolerant control strategies are introduced, enabling both independent and cooperative control actions to solve both leader-follower and leaderless cooperation problems. If the controller of the malicious helicopter can be adjusted, an individual estimation-based fault-tolerant control is designed to compensate for the influence. Otherwise, a cooperative fault-tolerant control is proposed by reconstructing controllers of neighboring helicopters. The proposed methods suppress the malicious behavior and maintain the cooperative control goal without removing it. Simulation results show the efficacy of the proposed approaches in maintaining coordination despite the malicious behavior.

As renewable energy sources and electric vehicles (EVs) gradually integrate into the power system, modern grids have evolved into complex large-scale networked control systems. The increasing complexity of the system internal structure presents greater challenges for load frequency control (LFC). To address these challenges, this paper proposes an enhanced model-free adaptive control (MFAC) approach based on system operating data. Initially, this study constructs a dynamic linearized relationship between system output, input and wind power. Then, leveraging pseudo-partial derivatives (PPD) of the I/O data and the partial derivatives of system output with respect to wind power data, a data-driven MFAC algorithm is designed using observer techniques. Given that additional communication burdens may result from data-driven methods, this paper introduces an event-triggered mechanism based on the system output saturation to reduce communication bandwidth. The theoretical analysis thoroughly outlines the design process of the proposed control algorithm and rigorously proves the system stability. Finally, multiple sets of experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the event-triggered MFAC (ETMFAC) algorithm effectively mitigates the impact of load disturbances on the power system, ensuring frequency stability. Sensor fault experiments are also carried out to further evaluate the algorithm’s robustness.

This article studies the distributed predefined-time formation control problem for a specific heterogeneous multiagent system. This system includes completely different unmanned autonomous helicopters (UAHs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs) in the presence of actuator faults. First, a distributed prescribed-time observer is developed for followers to estimate the leader states, which can decrease the network flow. Then, an adaptive predefined-time fault-tolerant optimal formation controller is constructed to continuously optimize performance index and approach optimal formation tracking. Moreover, in the designed control framework, adaptive updating laws are constructed for the unknown parameters of actuator loss of efficiency and the lumped uncertainty, respectively. Compared with the relevant finite/fixed-time cooperative tracking works, here the settling time is independent of any existing control gains and the initial conditions of the studied heterogeneous multiagent systems (MASs), thus it can be uniformly prescribed. Finally, the control performance of the designed method is further illustrated by a simulation experiment.

In most of the existing fault-tolerant control (FTC) results, only a uniform ultimate bound on the tracking error can be guaranteed, while achieving zero tracking error is a more desirable control objective. This article addresses the zero-error formation control problem for cross-dimensional heterogeneous fully actuated MASs subject to hybrid faults consisting of intermittent actuator faults and communication link faults (CLFs). To accurately estimate the leader's state when CLFs are present, distributed observers are developed specifically in the shape of an upper triangular chain of first-order low-pass filters. Then, for the agents with intermittent actuator faults, adaptive fault-tolerant tracking controllers are developed utilizing the fully actuated system (FAS) approach. Based on the Lyapunov stability theory, rigorous theoretical analysis is provided to prove that all signals in the closed-loop system are bounded and the formation errors asymptotically converge to zero. Finally, a simulation example is presented to prove the suggested control strategy's effectiveness.

Concurrent and complex aerial missions with multiple targets exceed the capabilities of a single cooperative formation of high-speed flight vehicles (HSFVs). To address this challenge, this article decomposes a fleet of HSFVs (subject to multiple compounding factors, including unknown aerodynamic disturbances, unmodeled or parametric uncertainties, actuator faults, and potential intervehicle collisions) into several subgroups and develops a recurrent neural network (RNN) online learning-based prescribed-time safe and robust cooperative group formation control protocol under dynamic event-triggered communication. A distributed prescribed-time event-triggered estimator (DP-TE-TE) is first developed to drive all HSFVs to acquire the convex hull information (i.e., input, velocity, and position) spanned by multiple virtual leader vehicles (VLVs) before grouping or the input, velocity, and position information of their respective single VLV within the group after grouping. Then, based on the constraint-following theory, the safety distance inequality between any potentially colliding pair of HSFVs, along with the first-order differential equation involving the formation position tracking error, is converted into collision motion constraints and prescribed-time trajectory tracking constraints, respectively. To enhance the flight control performance of the swarm, an RNN is constructed for each HSFV to learn the unknown nonlinear function induced by multiple compounding factors, thereby providing online compensation for the subsequent control design. Finally, by integrating the constraint-following errors derived from collision motion constraints and prescribed-time trajectory tracking constraints, the RNN compensation term, and the estimated information, the prescribed-time safe and robust cooperative group formation control scheme (P-TSRCGFCS) is proposed. In the simulation examples, the effectiveness of the proposed algorithms is verified by dividing 12 HSFVs and three VLVs into three subgroups to perform the desired cooperative group formation task.

In the aerospace field, equipment failures can lead to substantial economic losses and pose significant safety risks, making effective fault diagnosis crucial. Traditional fault diagnosis methods typically require large, precisely labeled datasets, which are challenging to obtain in aerospace applications due to the rarity and unpredictability of faults. To overcome these limitations, this article proposes a novel conditioned multiscale generative adversarial networks (GANs) approach designed to enhance fault diagnosis performance under small-sample conditions. Initially, raw vibration signals undergo preprocessing using the short-time Fourier transform, which expands frequency-domain features while preserving essential time-frequency characteristics. Subsequently, conditioned multiscale GANs are trained on these limited datasets, employing multiscale convolutional kernels to extract and fuse rich features, thus generating high-quality synthetic samples. Finally, these synthetic samples are combined with the original dataset to train a convolutional neural network offline, which can subsequently perform real-time online fault diagnosis. Extensive validation on two aerospace-related datasets demonstrates that the proposed method significantly enhances fault diagnosis accuracy and efficiency, even when the available training data is severely limited.

To elevate the robustness and optimality of helicopter formation, this article proposes the incremental fully actuated system approach (FASA) integrated with reinforcement learning (RL) for the formation control of multiple helicopters with faulty swash plates. First, the helicopter model encompassing aerodynamics, flapping dynamics, and swash plate dynamics under actuator faults is established. Then, the entire helicopter formation is reinterpreted and stabilized by the incremental FASA that offers the replacement of model information, the suppression of lumped uncertainty, and the rearrangement of system dynamics, with mitigated reliance on model accuracy and computing resources. Next, considering the influence of the actuator faults of a single helicopter on the convergence of the entire formation, RL is applied to pursue the optimal control strategy against fault impact through the critic network, which updates along the dynamics revised by the incremental FASA, ensuring satisfactory formation performance throughout the flight, augmenting the cost efficiency of the control scheme, and relieving any means of identification or approximation on helicopter dynamics. Finally, the stability of the control scheme is proved, and numerical simulations are conducted to illustrate it is efficiency.

This article presents a novel distributed attack-immune fault detection and identification observer (AI-FDIO) for the detection and isolation of sensor and actuator faults in multiagent systems that operate under an attack-immune consensus protocol and are subject to input disturbances. The protocol relies solely on relative output information between neighboring agents, directly measured by local sensors. Based on this locally acquired information, the AI-FDIO performs sensor and actuator fault detection without requiring interagent communication, thereby reducing vulnerability to cyberattacks. Traditional observer-based methods typically decouple residuals from control inputs, making them ineffective in detecting sensor faults that directly affect control signals. In contrast, the proposed AI-FDIO can effectively decouple sensor fault indicators from actuator faults and input disturbances—and vice versa—without relying on any prior knowledge of the faults. A systematic design methodology is presented, and simulation studies on transport aircraft with short-period dynamics and two-wheeled self-balancing robots demonstrate the effectiveness and robustness of the proposed AI-FDIO.

This paper develops an adaptive actuator fault estimation scheme for a class of Euler-Lagrange systems with relative degree two, which allows the loss of actuator effectiveness and generalised velocities to be estimated. In particular, a super-twisting observer is created for Euler-Lagrange systems with multiple degrees of freedom and the ‘equivalent output error injection’ signals, associated with the super-twisting sliding motion, are exploited to reconstruct the loss of effectiveness for all the actuators in an adaptive sense. The proposed scheme is then applied to a six-link underactuated snake robot model and the simulation results demonstrate the efficacy of the scheme.

Speed inconsistency in multi-motor systems may lead to mechanical structure damage, low cooperative precision, and even cause equipment breakdown. Traditional control methods may induce pronounced consensus error fluctuations and prolonged convergence time under complex conditions such as system parameter mismatch, sudden load changes, and inaccurate mechanism models. To address these challenges, we propose a fixed-time cooperative model-free sliding mode control (FCM-SMC) method based on a fractional-order ultra-local model (FOULM). Specifically, the proposed approach integrates fractional-order dynamics into the system model to better capture the non-integer order characteristics of multi-motor systems. Then, to estimate the unknown terms of FOULM, a novel fixed-time disturbance observer (FTDO) is designed. Additionally, grounded in the framework of FOULM, the FCM-SMC protocol is designed to ensure that the consensus error of the multi-motor systems converges within a fixed time. Taking motor #1 as an example, compared with existing methods, the error metrics (ME, MAE, and RMSE) of the proposed method are reduced by up to 64.8%, 70.2%, and 69.7%, respectively.

This paper aims to investigate the challenging problem of a multi-agent game with multiple pursuers and a single evader in an environment with multiple unknown uncertainties. A coupled approach combining decentralized observers and reinforcement learning (RL) controllers is proposed to deal with this scenario. Firstly, decentralized observers driven by auxiliary control laws are introduced to estimate the states of uncertain systems, with their best responses obtained through the adaptive dynamic programming (ADP) method. The estimated states, which reflect the actual states of the pursuers’ systems, are concurrently transmitted to the RL controllers. Subsequently, the controllers are trained with observer-based heterogeneous-agent proximal policy optimization (OHAPPO) algorithm, in which a novel global multi-function cost is designed. The algorithm utilizes the advantage decomposition for policy updates in the way of credit assignment, resulting in more stable and efficient updates compared to traditional value decomposition. Moreover, to further enhance the performance of both observers and controllers, a sequential game is established between them, where observers’ policies are influenced by controllers’ optimal control and vice versa. Finally, the simulation results verify the effectiveness of the designed OHAPPO algorithm in the pursuit-evasion game.

This paper studies practical prescribed-time control of fully actuated heterogeneous multiagent systems subjected to actuator faults. A new observer-based containment control structure is given in the scenario that only a small number of followers can access the knowledge of leaders. Firstly, prescribed-time distributed input, velocity, and position observers are designed to estimate the information of the convex hull that leaders have spanned. Then, the original practical prescribed-time fault-tolerant control issue is transformed into one with a deferred constraint on tracking errors by introducing a time-varying constraining function, and decentralized tracking controllers are designed based on the system’s fully actuated system model. With the prescribed-time prescribed performance function, the proposed method allows for the advance determination of the settling time and the final tracking accuracy as needed, as well as the reduction of the impact of actuator faults on the system without the need for actuator fault parameter estimations. The efficacy of the suggested approach is shown through simulation results. Note to Practitioners—The containment control issue of heterogeneous multiagent systems, as a popular topic in the control field, is crucial to practical engineering. It is worth noting that actuator faults often exist and they have the potential to spread throughout networks in practical applications. A novel observer-based containment control structure is developed and a robust fault-tolerant control algorithm is given to reduce the impact of actuator faults on the system without the need for actuator fault parameter estimations. What’s more, achieving containment control in a brief amount of time is highly desirable. Therefore, a practical prescribed-time control protocol is given to ensure containment errors converge to the predefined region within an assignable prescribed settling time interval. In summary, a practical prescribed-time fault-tolerant containment control technique is proposed, which promotes the advancement of containment control for MASs in practical applications.

In multi-agent systems (MASs), the information connections between agents present significant challenges for the fault detection of the system. This paper studies MASs from the perspective of information, constructs and explores the significance and connotation of the information Lagrangian, and relates it to the operational status of MAS as an index for detecting whether a fault has occurred in MAS. In this process, the distribution of the potential function on MAS information manifold is analyzed, the relationship between geodesics, Fisher information distance (FID), and information Lagrangian on the information manifold is studied. The distribution of the MAS information Lagrangian is explained in depth from a perspective of information amount and least action principle. The simulation application verified the effectiveness of the proposed method. As an operating status and fault detection index of MAS, information Lagrangian has clear physical significance, and has scalability potential.

In this paper, a novel approach for detecting anomalies in the non-linear fully-submerged hydrofoil attitude control system (HACS) is proposed, even in the presence of time-varying disturbances. To address the trade-off between robustness against disturbances and optimality in terms of estimation error, the extended sliding innovation filter (SIF) is employed as a state estimator for the target non-linear HACS. By utilizing a switching gain with a sliding boundary layer, the SIF inherently possesses a degree of robustness to estimation issues that may involve fault conditions or factors of disturbances. A residual framework is subsequently established to achieve state tracking and comparison. The residual evaluation for the fault detection (FD) scheme is then easily conducted using statistical methods such as the modified Z-Score and the peak signal-to-noise ratio (PSNR). Finally, the effectiveness of the developed FD strategy is substantiated through experiments conducted on a hardware-in-loop (HIL) platform. Comparative analysis with state-of-the-art robust UKF algorithms reveals the impressive fault detection proficiency of the proposed strategy. Note to Practitioners—This paper was motivated by the challenges of state estimation and FD for hydrofoil crafts under stochastic ocean wave disturbances. The extended SIF-based approach enhances robustness in the estimation and FD against disturbances by introducing a dynamic layer. Moreover, the adaptive layer indicates system anomalies as significant changes, which can assist engineers in promptly identifying anomalies. Furthermore, the modified statistics used in the FD scheme effectively reduce interference in the results. The developed FD method is easy to implement without linearizing the non-linear target system. The experimental validation conducted on the dSPACE platform utilizing the PCH-1 model illustrates the practicality of the proposed strategy for pertinent practitioners.

This paper addresses the attitude consistency problem in multi-hydrofoil crafts, considering actuator faults, false data injection, stochastic ocean wave disturbances, and topological propagation effects. We propose a game-theoretic fault-tolerant control (FTC) framework integrating a composite controller with a specific performance index. In this framework, both local and neighboring node information is considered. The desired controller, along with non-ideal anomalies and disturbances, is treated as participants, transforming the FTC problem into a multi-player non-cooperative game. Moreover, a single critic neural network (NN) is utilized to alleviate the challenges associated with solving a partial differential equation, thereby facilitating the derivation of the ideal control law. Compared to traditional local information-based control methods, the proposed approach incorporates neighborhood information. It integrates faults, disturbances, and propagation effects within a unified framework for optimal control, enhancing FTC effectiveness. We conduct comparative experiments using a four-craft scenario on the dSPACE platform. The results demonstrate that the proposed method reduces the mean absolute deviation (MAD) of local tracking error by at least 28.00% and the standard deviation (STD) by at least 7.76% compared to the other two methods.

This paper addresses the problem of optimal fault-tolerant attitude control with prescribed performance for a tailless flying wing aircraft under actuator faults and saturation. Firstly, by using the time-scale separation principle, the considered state variables of the aircraft are divided into two groups, the angular rate loop and the attitude loop, with different response speeds. Secondly, after prescribed performance bound (PPB) transformation, a novel error tracking nominal system is obtained, based on which an incremental neural network observer is designed to approximate the actuator faults. Thirdly, a functional performance index in terms of the actuator saturation is designed for the nominal system and the corresponding Hamilton-Jacobi-Bellman (HJB) equation is derived. A new single-network incremental adaptive dynamic programming (SIADP) algorithm is proposed to solve the optimal control problem. Moreover, the uniformly ultimately bounded stability of the closed-loop incremental system is proved by using the Lyapunov theory. Finally, simulations are given to illustrate the effectiveness of the proposed control approach.Note to Practitioners—The aim of this paper is to develop a new control scheme to realize active fault-tolerant control for tailless flying wing aircraft with actuator faults and saturation. The absence of vertical and horizontal tail fins results in a short longitudinal control moment arm, insufficient longitudinal control efficiency, deep coupling between longitudinal and lateral dynamics and poor stability of the tailless flying wing aircraft. Fault tolerance ability is necessary in flight control design, and the optimal and transient performance with control surface saturation should be considered in control practice. To solve these problems, a PPB-SIADP scheme combined with incremental adaptive observer approach is proposed, the tracking error can converge in an optimal way with prescribed performance. The proposed approach in this paper can be used as a valuable scheme for the tailless flying wing aircraft control law design in engineering practice.

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 paper explores the consensus control problem of nonlinear multi-agent systems (MASs) under complex cyber-physical threats (CPTs), which encompass sensor/actuator faults, input/output channel noises, and random cyber-attacks. The multiple sensor/actuator faults are uniformly modeled as an exponential type, while random cyber-attacks are characterized by a Markov chain. To enhance the safety and security of MASs under CPTs, the distributed normalized observers are first developed, enabling precise estimations of unknown state and fault information. Subsequently, the distributed fault-tolerant consensus control (FTCC) scheme with a positive reconstruction mechanism is proposed to maintain resilience against attacks, compensation for faults, and robustness to noises in MASs under adverse CPTs. The two notable innovations can be outlined as follows: i) The achievement of FTCC objectives under complex CPTs, demonstrating strong algorithmic transferability in both non-attack and random attack scenarios. ii) The adoption of a double-layer distributed framework in the estimation layer and control layer, balancing computational complexity and efficiency improvements compared to a combination of decentralized and distributed approaches. Simulation results finally confirm the efficacy and feasibility of the proposed FTCC algorithm.

Quadrotor helicopters have been widely applied in numerous fields and are increasingly attracting attention in many application areas. It is challenging to ensure the safety and reliability of quadrotor helicopter when faults occur in sensors or actuators, which may lead to catastrophic crashes. Recently, Transformer and its variants demonstrate powerful feature extraction capabilities, while a fault diagnosis (FD) model based on Transformer usually has a high demand for parameters and computations which limits its applications. To address this issue, this article proposes a novel lightweight Transformer with selective patches and channels modules (SPCFormer) method for quadrotor helicopter FD. First, the flight data is split into nonoverlapping patches for each channel. A selective patches module with a lightweight attention architecture is designed to extract critical local feature information from patches and mitigate multichannel coupling effects. Second, the selective channel attention is developed to form an attention vector rather than a matrix. This mechanism is integrated into the selective channels module to capture important global channel features while reducing the complexity of the model. Finally, a high-fidelity quadrotor helicopter fault simulator is developed to simulate different types of faults (i.e., actuator fault and sensor fault) under three different flight statuses and no extra sensors. The effectiveness of the proposed FD method is verified through the cross-validation on the above developed software-in-the-loop (SIL) and hardware-in-the-loop (HIL) simulators.

Bearing skidding is the primary factor restricting the development of aeroengines toward ultrahigh speed, low friction, and lightweight. Compared to typical bearing faults, analysis of bearing skidding presents greater challenges due to the weak signal properties, significant time-varying characteristics and coupling influence of multiple factors. It is crucial to fully utilize multisource signals to enhance skidding features and capture time-varying characteristics. This article proposes a prior knowledge-embedded dual feedback spatial-temporal graph convolutional network (DFSTGCN) for skidding assessment. Unlike existing adjacency matrix construction strategies, the correlation between multisource signals is described based on multiple prior knowledge, which includes dynamic model, structural dynamics, and expert experience. Furthermore, a DFSTGCN is designed to simultaneously focus on the spatial and temporal dependencies of time-varying skidding data. Specifically, a dual feedback mechanism that includes prediction error ratio and uncertainty loss function is employed to improve the generalization performance of skidding prediction model. The effectiveness of the proposed strategy is validated under different working conditions.

Actuator and sensor faults are among the most common factors affecting the stability of multiagent systems (MASs). This article proposes a dynamic event-triggered fault-tolerant control (FTC) algorithm based on descriptor sliding-mode observers to address actuator and sensor faults in MASs. First, the MAS dynamics are reformulated into a descriptor form, enabling an observer to simultaneously achieve state estimation and fault diagnosis. Using the estimation results, an adaptive FTC algorithm is developed to maintain the stability of MASs in the presence of concurrent faults, with control gains updated based on the observer consensus error. A dynamic event-triggered mechanism is incorporated to manage data transmission and update neighboring agents' information for the controller, thereby reducing communication overhead. Finally, a numerical simulation involving multiple quadrotors is conducted to validate the effectiveness of the proposed method.

This article aims to address the formation control issue of the unknown nonaffine nonlinear heterogeneous multiagent system (MAS) considering formation tracking accuracy and computational cost. A novel dynamic prescribed boundary-based event-triggered mechanism is proposed first to flexibly adjust the emphasis on these two indicators, and applied to data modeling and controller design simultaneously to reduce their computational cost. On one hand, an observer-based pseudo gradient estimation algorithm is designed under event-triggered framework for model reconfiguration with only input/output data of system rather than mathematical dynamics. On the other hand, an event-triggered constrained control strategy is developed with several modules to cope with complex scenarios. Concretely, a data-driven anti-windup compensator is designed in case of input constraint, and an improved prescribed performance-based fractional order terminal sliding mode controller is explored for enhancement of the formation tracking accuracy and robustness of the controlled MAS with rigorous stability analysis. Both numerical simulation and hard-in-the-loop experiment on distributed energy storage systems are performed to attest the efficacy of the proposed formation control strategy.

This article investigates the fixed-time faulty parameter estimation problem for an interconnected fuzzy system with multiple time-varying delays. Based on the persistent excitation condition, an adaptive observer with a faulty parameter identification algorithm is constructed, to provide the accurate information of partial loss of actuator effectiveness within a fixed settling-time, and to guarantee the boundedness of state estimation error by mitigating the influence of external disturbance. Accordingly, several sufficient conditions for the existence of fuzzy observer gain, and the convergence proof of the input-to-state stability are also presented by utilizing the small-gain technique. Afterwards, an active fault-tolerant controller is synthesized to maintain the faulty interconnected system by compensating the actuator fault. Finally, simulation results on an inverted-pendulum system and a numerical example show the feasibility and advantage of the proposed approaches.

Battery energy storage systems (BESS) are essential for modern energy management, supporting renewable integration and grid stability. However, fault diagnosis for BESS requires extensive manual network tuning. To overcome this, we introduce a zero-shot neural architecture search approach for BESS fault diagnosis. First, the neural network is broken down into piecewise linear functions, and the Rademacher complexity is calculated for this class of functions. To prevent batch normalization (BN) layers from repeatedly scaling the Rademacher complexity and invalidating network comparisons, the Rademacher complexity is approximated using the variance of the BN layers. Finally, the selected models are then compressed via 8-bit quantization to facilitate deployment on mobile devices. This approach achieves 99.42% accuracy in just 0.51 GPU h, significantly reducing model search time without needing pretrained models. We validate this method on a self-developed BESS platform featuring a battery management system and custom mobile app, accessible online.

The multifactor coupling influence on the skidding behavior of aeroengine rotors presents significant challenges in locating the skidding causes and developing effective skidding suppression measures. However, ongoing research into fault mechanism and knowledge graph (KG) facilitates the accurate tracing of complex faults. We propose a heterogeneous KG inference-assisted skidding tracing and regulation strategy for aeroengine rotor. First, a skidding heterogeneous KG is constructed based on the text and data knowledge, in which the skidding level classification rules are determined for the first time. Second, we design an adaptive distributed metalearning algorithm to extract data features by combining the structural characteristics of the skidding KG. Third, few-shot knowledge inference is performed using the relation-metalearning graph convolutional network. Finally, we develop skidding suppression measures by tracing the input knowledge under unknown working states, enabling effective regulation of skidding behavior.

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.

This article presents a comprehensive analysis of the limitations associated with single ES in regulating CL voltage, specifically highlighting their restricted adjustment range and suboptimal performance in high-power applications. To address these challenges, a novel parallel configuration of ESs is proposed for the first time, with an in-depth examination of its design principles and critical technical issues. To overcome the energy supply limitations inherent in traditional ES systems, the study integrates PV systems and BESS, replacing the conventional assumption of an ideal dc power source. This integration establishes a PV/BESS model that not only enhances the utilization efficiency of renewable energy but also ensures effective stabilization of the dc bus voltage. Under diverse disturbances, including solar irradiance fluctuations and microgrid voltage variations, the proposed parallel PV/BESS energy storage system achieves seamless coordinated operation via MPC, substantially expanding the CL voltage regulation range. However, disparities in internal parameters and switching states among parallel ESs may induce significant circulating currents, posing a threat to system stability. To mitigate this issue, a multiagent-based MPC approach is introduced. This method incorporates a circulating current suppression term into the cost function alongside the CL voltage regulation objective, ensuring balanced currents distribution across all units while significantly enhancing system stability and coordination. Simulation and experimental results demonstrate the effectiveness of the proposed multiagent MPC strategy, confirming its capability to significantly improve the stability and performance of the parallel PV/BESS ESs system.

In rotating machinery, accurate fault diagnosis is crucial for efficiency and preventing failures. Traditional models often struggle with imbalanced datasets. This study introduces strategies that use feature fusion deep convolutional generative adversarial network (DCGAN) architectures to improve fault diagnosis accuracy. Initially, we pretrain the DCGAN using a comprehensive dataset encompassing various general faults to robustly capture the underlying features. Then, we use rare fault samples to refine the DCGAN, enhancing its capability to extract features from these minority classes. Random noise is input into the feature fusion deep convolutional generative adversarial network (FFDCGAN) model to obtain pseudosamples of the rare faults. The generated faults are then combined with the original dataset and analyzed by a convolutional neural network to classify fault types. Based on experimental results from the ZHS-2 and HIT aero-engine fault datasets, comparative analysis with existing studies shows that the proposed FFDCGAN method generates samples with significantly greater diversity. In addition, the proposed imbalanced fault diagnosis approach achieves higher accuracy, thereby validating its efficacy in handling imbalanced datasets.

The series includes two parts, articulating the two novel avenues of research on intelligent fault diagnosis (FD) for nonlinear feedback control systems. In Part I of the series, we design a novel FD paradigm by elaborating an invertible neural network (INN) for feedback control systems. With the aid of a left manifold, the core idea behind the INN-based FD scheme is as follows: 1) formulation of residual generator used for FD as a projection of system data onto the null space that has the same dimension as system outputs; 2) in a topological space, elaboration of a homeomorphism that delivers an invertible relationship between system outputs and residual signals when the system input is given; and 3) skillful introduction of both the master and slave objective functions to achieve system/parameter identification with information loseless property. Comparing with the existing FD approaches, the three superior strengths of the proposed FD scheme deserving mentation are as follows: 1) it specializes in nonlinear feedback control systems; 2) it can effectively avoid the overfitting problem when approximating or learning nonlinear system dynamics; and 3) control theory guides the whole design, ensuring the interpretability of the learning process. Finally, two studies on nonlinear systems demonstrate the feasibility of the invertible left manifold (ILM)-based FD strategy. Part I would contribute to the future development of machine learning (ML)-based system identification and explainable FD approaches, and also benefits the right manifold-based FD designs in Part II.

Cascading failures represent a significant issue in production lines, as they can lead to process defects and safety incidents. An accurate risk assessment of cascading failures is crucial for ensuring both safety and operational efficiency. However, existing methods for assessing cascading failures typically focus only on exposed failures, neglecting hidden failures. Hidden failures are functional faults not apparent under normal operating conditions; they often remain undetected until triggered by another failure event. Considering solely exposed failures thus provides an incomplete picture, insufficient for accurately assessing cascading failure risks. To address this limitation, this article proposes a novel virtual node-based framework designed to assess cascading failure risks explicitly accounting for hidden failures. A Bayesian network approach, enhanced by leveraging connectivity information, is employed to effectively model the structure of the production line. Within this Bayesian network, a virtual node is integrated, thus representing the background impact of hidden failures. Specifically, the interactions between this virtual node and other network nodes explicitly capture the dynamics and mechanisms underlying hidden failures. Building upon this framework, we propose the virtual node-assisted inverse PageRank algorithm. The algorithm is rigorously defined, with mathematically guaranteed properties including positivity, convergence, and an analytical solution. The methodology is validated using a real-world case study involving an aerospace impeller production line. Experimental results demonstrate that the proposed algorithm successfully identifies hidden failures, delivering superior performance compared to traditional risk assessment approaches.

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 recent years, intelligent manufacturing has integrated industrial data and artificial intelligence technology, which has been a widely concerned development direction in the manufacturing industry. Industrial data integrity is the key factor for the successful implementation of intelligent manufacturing. However, in flexible manufacturing systems with multivariety and small-batch, it is hard to collect production data from all working conditions. Actually, for the purpose of status monitoring, data acquisition and annotation on complex mechanical components is also time-consuming and labor-intensive, which requires the assistance of professional domain knowledge. Faced with the challenge of incomplete data quantity and quality, federated learning is a promising paradigm of collaborative modeling, which ensures data privacy and fully utilizes distributed data information from different industrial users. However, due to the heterogeneity of data among industrial users, cooperation benefits cannot satisfy all industrial users. In this article, a novel federated learning cooperation framework is proposed to guide participants to choose the appropriate coalition and improve the benefits of participants. In this framework, the self-organizing incremental neural network is employed to generate prototypes that can effectively capture the distributional characteristics of raw data, obviating the necessity for industrial users to provide their raw data and labels. It offers recommendations for industrial users to foster collaboration by assessing the similarity among these prototypes. The collaborative tool wear prediction experiments demonstrate the effectiveness of the framework on industrial data.

This article proposes an adaptive fault-tolerant control (FTC) scheme for heterogeneous multiagent systems with time-varying communication link faults and actuator faults. First, the communication link faults, including the channel signal fading and cyber bias attack, is considered, and the communication link fault compensation controller is designed through introducing the adaptive signals with the estimate of the norms of the faulty matrix. Then, by using the minimum eigenvalue of the control gain matrix, the minimum-eigenvalue-based adaptive fault-tolerant controller is proposed to compensate for the time-varying actuator loss of effectiveness and bias faults. Moreover, the convergence performance analysis of the developed FTC algorithm is given based on the Lyapunov theory. The simulation results carried out on the quadrotors-unmanned ground vehicles formation systems validate the effectiveness of the theoretical results.

In this study, the fault-tolerant consensus control (FTCC) challenge is investigated for nonlinear multiagent systems (MASs) in the simultaneous occurrence of abrupt and incipient actuator/sensor faults in the physical level and hybrid Deception/Denial-of-Service (DoS) attacks in the cyber level. For security enhancement and/or safety maintenance purposes, an unknown state and fault decoupling-based augmented estimator is first devised, and a distributed event-triggered FTCC protocol is then developed to achieve strength against hostile attacks and faults, respectively, with the incorporation of augmented state estimation, neighboring sensor fault estimation, and latest successfully triggered output interaction. By constructing dual indicators along with average dwelling time and attack frequency technique, criteria of exponential mean-square consensus of the nonlinear MASs subject to hybrid attacks are obtained. In the end, simulation is outlined to illustrate the efficacy and improvements of the developed event-triggered FTCC methodology.

Guaranteed system performance under various circumstances continues to be a challenge in technique and practice. Based on this, this article investigates the optimal fault-tolerant control strategy for a large-scale interconnected system with the intermittent actuator faults. Since the subsystem state is enforced to a restricted range, an asymmetric integral barrier Lyapunov function is incorporated into the principle of Bellman optimality to avoid the violation of state constraints. Also, it can conquer a conservative limitation that the bounds of the transformed error-constraints are known. Subsequently, the critic-actor–identifier framework is constructed in the backstepping step to evaluate the objective function, control behavior and unknown dynamic, respectively, wherein the decentralized controller derived from the learning process and the fault-tolerant controller are separated by introducing an intermediate controller. Meanwhile, it is illustrated that the trajectory tracking errors will approach to a small region nearby the origin, and the system states may not beyond the given asymmetric constraint bounds, even in the presence of faults. Finally, results are presented to exhibit the effectiveness and the advantage of the optimal approach through appropriate comparative simulations.

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 study investigates the fixed-time and prescribed-time optimal formation control strategies for heterogeneous multiagent systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) unde actuator faults. In the proposed control framework, the critic-actor framework is designed to finish the optimization tracking. Radial basis function neural network (RBFNN) is implemented to derive the tracking control, in which the actor RBFNN is utilized to make up for the actuator faults and generate the formation control action, and the critic RBFNN is utilized to evaluate the execution cost. Then, a Lyapunov-based tracking technique is designed to ensure the fixed-time stability of the tracking error. Since the adaptive updating protocols are developed by deriving the gradient descent of the cost function, the optimized control algorithm can be obtained. In addition, a prescribed-time fault-tolerant optimal controller is further proposed, which renders the convergence time fully independent of any other parameter and the initial states, thus the convergence time can be uniformly prespecified. Finally, the validity of the proposed algorithms are demonstrated via the simulation experiments. Note to Practitioners—Actuator faults often occur in the operation of industrial automation equipment. Hence, it is of great practical significance for control systems to have faster fault-tolerant performance. In addition, the optimization effect of performance indicator often denotes the quality of the expected task completion. Therefore, this study proposes a faster optimal fault-tolerant formation strategy for heterogeneous unmanned formation system based on fixed-time and prescribed-time theorems under actuator faults. The effectiveness of the developed control approach is verified by designed simulation.

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.

The fault-tolerant formation control problem for unmanned helicopters (UHs) with actuator faults under fixed and switching communication topologies is investigated in this paper. Firstly, the high-order fully actuated system model of the 6-DOF UH is established, which is divided into the position outer-loop subsystem and the attitude inner-loop subsystem. Secondly, a prescribed-time disturbance observer is constructed to rapidly and accurately estimate the composite disturbances composed of external disturbances and actuator faults. Then, with the aid of the fully actuated system approach, the formation fault-tolerant controller and attitude tracking fault-tolerant controller are designed for the inner and outer loops respectively, which can guarantee the prescribed-time stability of multiple UHs under fixed and switching topologies. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed control strategy.

In this paper, a reinforcement learning (RL) based fault tolerant control (FTC) strategy is investigated for the bleed air temperature control system (BATCS) of an aero-engine with time delays, parameter uncertainties and multiple types of faults. By the design of offline training networks, the deep deterministic policy gradient (DDPG) algorithm is employed to update the parameters of training networks. Then a data driven FTC input combined with exponential weighted moving average (EWMA) filter can be obtained to achieve the fault tolerant tracking control of BATCS. Finally, a comparison simulation between PID and RL based FTC scheme is demonstrated to verify the feasibility of the research.

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 the problem of fixed-time fault estimation and fault-tolerant control (FTC) for interconnected systems subject to both multiplicative and additive actuator faults. On the basis of the bilimit homogeneous theory, the proposed fault estimation observer can acquire the exact system state and fault information in a specified time, and such a time is determined by a constant upper bound, independent of the initial observation errors. Next, the prescribed performance function (PPF) is employed to impose the anticipant performance criterion on the trajectory tracking errors, for the purpose of preserving both desirable transient and steady-state responses. Afterward, we incorporate the recursive fast terminal sliding-mode technique into the active FTC (AFTC) design procedure to eliminate the influence of faults. In such a way, the fixed-time convergence property of tracking errors can be guaranteed without any restriction on the initial conditions. Finally, comparative simulation results are provided to illustrate the feasibility and superiority of the proposed strategy.

The leader-following cooperative problem in heterogeneous multiagent systems (HMASs) with unmodeled dynamics and actuator faults is investigated in this article. The HMASs, which include unmanned ground vehicles and unmanned aerial vehicles, are first described using a fully actuated system model (FASM). The FASM, as opposed to the first-order state-space model, preserves the physical significance of original systems and makes it feasible to apply the control rule entirely. In order to approximate unknown system dynamics, novel neuroadaptive laws with few learning parameters are then suggested. To counteract the negative effects of actuator faults, the Nussbaum function and adaptive approach are utilized. In addition, a cooperative fault-tolerant protocol is suggested, wherein consensus errors are uniformly ultimately bounded. The lack of virtual control variables in the proposed protocol reduces its complexity. The theoretical results are then validated by numerical simulations.

When cooperating through an intensive formation, the safe distancing of unmanned aerial vehicles (UAVs) is a delicate issue, especially if UAVs are subjected to actuator faults that cause rapid maneuvers. This article investigates the fixed-time fault-tolerant formation control of multiple quadrotor UAVs under actuator faults, which considers the collision avoidance among UAVs when faults occur, and the convenience of engineering application. First, an augmented fixed-time observer with measurement noise oppression is adopted to estimate and compensate actuator faults and disturbance in rotational and translational dynamics. Then, a baseline attitude controller, a command filter, and a velocity controller are proposed for each quadrotor UAV to track the desired velocity within a fixed time. Next, a distributed fixed-time sliding-mode controller that integrates the gradient of repulsive potential function into the sliding manifold is designed to achieve leader-follower formation control and collision avoidance simultaneously. The control scheme is proven to be fixed-time convergent via Lyapunov stability analysis and is normalized in accordance with the compatibility of hardware implementation. Finally, the designed algorithm is embedded into PX4 architecture to illustrate the effectiveness and practicality of the control strategy.

This article presents a new perspective from control theory to interpret and solve the instability and mode collapse problems of generative adversarial networks (GANs). The dynamics of GANs are parameterized in the function space and control directed methods are applied to investigate GANs. First, the linear control theory is utilized to analyze and understand GANs. It is proved that the stability depends only on control parameters. Second, a proportional-integral-derivative (PID) controller is designed to improve its stability. GANs can be controlled to adaptively generate images by an overshoot rate that is only related to the PID control parameters. Third, a new PIDGAN is derived with a theoretical guarantee of stability. Fourth, to exploit the nonlinear characteristics of GANs, the nonlinear control theory is applied to further analyze GANs and develop a feedback linearization control-based PIDGAN named NPIDGAN. Both PIDGAN and NPIDGAN not only improve stability but also prevent mode collapse. With five datasets covering a wide variety of image domains, the proposed models achieve superior performance with 1024×1024 resolution compared with the state-of-the-art GANs, even when data are limited.

Few-shot fault diagnosis is a challenging problem for complex engineering systems due to the shortage of enough annotated failure samples. This problem is increased by varying working conditions that are commonly encountered in real-world systems. Meta-learning is a promising strategy to solve this point, open issues remain unresolved in practical applications, such as domain adaptation, domain generalization, etc. This article attempts to improve domain adaptation and generalization by focusing on the distribution-shift robustness of meta-learning from the task generation perspective. In fact, few-shot fault diagnosis under varying working conditions allows to address the distribution shift problem in a natural way. An unsupervised across-tasks meta-learning strategy with distributional similarity preference is proposed, where the core is the distribution-distance-weighting mechanism. Differently from the naive random meta-train task generation strategy used in existing meta-learning methods, the source instances that present a more similar distribution with respect to the target instances gain larger weightings in the task generation. This strategy leads to a meta-task training set that is enough diverse, and at the same time can be easily learned due to the distribution similarity features of the source tasks. The proposed method introduces the concept of maximum mean discrepancy that is applied to derive the distribution distance of the measurements. Moreover, a model-agnostic meta-learning is applied to realize few-shot fault diagnosis under varying working conditions. The proposed solutions are verified and compared by considering two public datasets used for bearing fault diagnosis. The results show that the proposed strategy outperforms different related few-shot fault diagnosis methods under varying working conditions. Moreover, it is thus proved that, meta-learning with distribution similarity feature represents an effective approach for domain adaptation and generalization.

This article proposes a zero-sum differential game-based control scheme for a class of affine nonlinear control systems with actuator faults, including loss of control, loss of effectiveness, and bias faults. In the control design, the bias faults and the control signal are chosen as the two opposite sides. The Nash equilibrium is achieved while the optimal control signal and the upper bound of bias faults can be derived from the Hamilton-Jacobi-Isaacs (HJI) equation. An adaptive dynamic programming (ADP) method is employed to estimate the weight of the critic network. The developed zero-sum differential game-based control scheme can ensure the system stability and optimal performance. Two simulation results on a numerical system and a rigid spacecraft model illustrate the effectiveness of the proposed control scheme.

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

This article investigates the fault-tolerant coordinated tracking control problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults and communication delays. By supplementing the commonly used Gaussian functions in the fuzzy neural networks (FNNs) with sine-cosine functions and constructing two kinds of recurrent loops within the FNN architecture, double recurrent perturbation FNNs are cleverly designed to learn the unknown terms containing faults and uncertainties. Then, adaptive laws are designed for double recurrent perturbation FNNs. Moreover, by assimilating fractional-order calculus into the sliding-mode surfaces and the control signals, refined transient-state and steady-state adjustment performances can be obtained. It is shown by Lyapunov stability analysis that all fixed-wing UAVs can coordinately track their desired trajectories and the tracking errors are uniformly ultimately bounded. Comparative simulation results are provided to show the effectiveness of the proposed control strategy.

This article presents a distributed fault-tolerant control (FTC) scheme for nonlinear fractional-order (FO) multiagent systems (MASs) with the order lying in (0, 1], such that the proposed control architecture can be directly applied to both FO and integer-order (IO) systems without any modifications. To handle the unexpected actuator faults encountered by the FO MASs, a hierarchical FTC mechanism is developed for each system by constructing an event-triggered distributed FO estimator at the upper layer to estimate the leader system's output via conditionally triggered neighboring information, and an FTC unit at the lower layer to counteract the loss-of-effectiveness faults via Nussbaum function with FO criteria. To further address the unknown nonlinear functions involving bias faults and periodic disturbances, the Fourier series expansion technique is used to construct the input variables of fuzzy neural networks (FNNs), such that the FNNs with dynamically adjusted weight matrices, centers, and widths can be developed for each FO system to act as the learning module. It is shown by FO Lyapunov stability analysis that all follower systems can track the leader system against faults and periodic disturbances. Simulation results on FO systems and hardware-in-the-loop experiment results on IO fixed-wing unmanned aerial vehicles show the extensive feasibility of the developed scheme.

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.

Heterogeneous Multi-Agents System (MAS) has been attracting increasing attention in many application areas, but the safety and reliability of MAS are still challenging issues. Fault diagnosis is a necessary technology to ensure the safety and reliability of heterogeneous MAS. According to the characteristics of high dispersion in MAS, strong local perception ability and weak global perception ability, this paper proposes a distributed hybrid knowledge-based and data-driven fault diagnosis, which realizes dynamic re-construction of data and knowledge through reinforcement learning and fuzzy broad learning. In the meantime, we also consider communication network topology to realize distributed collaborative diagnosis, which can effectively improve the diagnostic performance. Then, we develop a high-fidelity heterogeneous MAS software-in-the-loop and hardware-in-the-loop fault simulators to simulate different types of failures (i.e., actuator failure, sensor and communication failure). Finally, through the cross-validation on the above developed simulators, this work verifies the effectiveness of the proposed distributed intelligent fault diagnosis.

The demand for bearing skidding diagnosis is widely present in aeroengines operating at high-speed and light-load conditions. However, the weak and time-varying characteristics of skidding signal raise challenges for accurate diagnosis. To address these issues, we propose a synergistic TransGCN strategy to extract rich feature information from time-varying weak bearing skidding signals. Unlike existing methods, the prior knowledge obtained from bearing skidding analysis and the alternate integration and synergistic optimization of various advantages are used to enhance algorithm performance. First, an adaptive chirplet transform is designed to measure the time-varying cage slip rate. Second, the skidding sensitive characteristics are determined, and the variation ranges of slip rate sensitivity are employed as prior knowledge to calculate the fusion weights of multisource information. Then, an unsupervised deep feature representation network is constructed to analyze the complex correlation of bearing skidding signals. Finally, a synergistic TransGCN is developed by alternately integrating and synergistic optimizing Bayesformer and graph convolutional network. The superiority of the proposed strategy has been verified.

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.

The absence of fault data in certain categories presents a significant challenge in data-driven fault diagnosis, as obtaining a complete fault dataset is often unfeasible. Zero-shot learning has emerged as a viable solution to this problem. Nonetheless, it often encounters problem of unreliable diagnosis results due to domain shift. In this article, a feature generating network with attribute-consistency is developed for zero-shot fault diagnosis, which introduces the attribute consistency constraint and feature transformation with attribute information. The implementation process comprises two parts, unseen fault class generation and discriminative feature transformation. The attribute consistency constraint adopted in data generation can make the generated data represent their attribute well. For feature transformation, a concatenation operation is used to transforming the generated samples into more discriminative representations. The effectiveness of the proposed method is verified using a public dataset for fault diagnosis purpose. Results indicate that the proposed method outperforms the state-of-art zero-shot diagnosis method.

Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two subclasses are developed based on knowledge calibration and knowledge compromise, is carried out in this survey article. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize previous knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope that this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.

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 presents a model predictive control (MPC) strategy to find the optimal switching time sequences of networked switched systems with uncertainties. First, based on predicted trajectories under exact discretization, a large-scale MPC problem is formulated; second, a two-level hierarchical optimization structure coupled with a local compensation mechanism is established to solve the formulated MPC problem, where the proposed hierarchical optimization structure is actually a recurrent neural network consisting of a coordination unit (CU) at the upper level and a series of local optimization units (LOUs) related to each subsystem at the lower level. Finally, a real-time switching time optimization algorithm is designed to calculate the optimal switching time sequences.

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

This study investigates the distributed secure control problem of multiple unmanned surface vehicles (USVs) in the presence of wave-induced disturbances and unified abrupt and incipient rudder angle faults in physical layer, and aperiodic Denial-of-Service (DoS) attacks in cyber layer. Multi-USVs with rudder angle fault and DoS attack modeling are first established. Then, the decentralized unknown input observer (UIO)-based fault estimation and distributed secure control approach is developed in a co-designed framework for multi-USVs with cyber–physical threats. Advantages of the proposed secure scheme are: 1) actuator faults, DoS attacks, and event-triggering strategies with varying action instants, durations, and locations are synchronously addressed and 2) the criteria of exponential consensus are derived by virtue of attack frequency and average dwelling time technique without prior knowledge of unknown wave-induced perturbation bounds and elimination of Zeno behavior in an event-based mechanism. Comparative simulations outline the performance and advantage of the proposed distributed secure control algorithm.

To enhance the reliability of digital automation systems in the Industry 4.0 era, this paper investigates a robust fault-tolerant control scheme in the discrete-time domain subject to periodic disturbances, consisting of a fault estimator, dynamic disturbance compensation loop, and fault-tolerant controller. The fault estimator simultaneously estimates both the system states and actuator/sensor faults. The existence and stability conditions of the proposed estimator are given, and a robust design method is proposed to make the state estimates robust to disturbances. To further reduce the estimation errors caused by periodic disturbances, a novel disturbance compensation loop is introduced and is optimized by a joint zero-assignment and pole-optimization method to delicately compensate for the adverse impacts of periodic input disturbances. The proposed robust fault-tolerant controller uses fault estimation to ensure fast recovery in the event of bounded actuator/sensor faults. The proposed scheme is evaluated through simulations of a two-wheeled mobile robot subject to periodic disturbances and simultaneous abrupt inclination angular sensor and ramp actuator faults, where its performance is shown to exceed that of existing methods.

In this article, the distributed adaptive fixed-time output time-varying formation tracking issue of heterogeneous multiagent systems (MASs) with actuator faults is addressed, in which the followers suffer from loss-of-effectiveness actuator faults, and the leader has unknown bounded input. To solve the above issue, a distributed fixed-time observer is constructed with the leader's unknown input, by which each follower can obtain the leader's states in a predesigned time. Then, based on the observer and the desired formation vector, a local adaptive fixed-time fault-tolerant formation control algorithm is proposed for each follower with the help of time-varying gains to make up for the influence of actuator faults. Furthermore, it is proven that the designed controller can satisfactorily accomplish the considered task of the heterogeneous MASs by using the Lyapunov stability theory. Specifically, the obtained upper bound of the convergence time only depends on a few controller parameters. Finally, a simulation example is implemented to validate the efficiency of the analytical results.

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.

This article addresses the distributed multiple fault isolation, modeling, and the closed-loop fault estimation under asynchronous switching for high speed train (HST) with switched dynamics, which is composed of traction, coasting, and braking. First, directed-graph-quantum-learning-based multiple-agent system (MAS) classifiers are introduced to characterize the joints effects of multiple faults. Some sufficient conditions are derived under the condition that the multiple fault topology contains a directed spanning tree and cycle edge, and these conditions guarantee that the multiple fault isolation problem can be solved under randomized learning techniques. Then, single-integrator agents are employed to capture the time-varying topology of multiple fault modeling, in which edge agreement and persistence condition are used to guarantee asymptotic consensus. After that, a novel robust fault estimation design along with the switched Lyapunov function and average dwell time is proposed for the possible power actuator faults subject to asynchronous switching and electromagnetic interferences. In addition, switched estimators are designed such that the closed-loop system is asymptotically stable. A multiple fault isolation and estimation case is investigated to validate the application of this methodology.

To ensure the safety of an automation system, fault detection (FD) has become an active research topic. With the development of artificial intelligence, model-free FD strategies have been widely investigated over the past 20 years. In this work, a hybrid FD design approach that combines data-driven and model-based is developed for nonlinear dynamic systems whose information is not known beforehand. With the aid of a Takagi-Sugeno (T-S) fuzzy model, the nonlinear system can be identified through a group of least-squares-based optimization. The associated modeling errors are taken into account when designing residual generators. In addition, statistical learning is adopted to obtain an upper bound of modeling errors, based on which an optimization problem is formulated to determine a reliable FD threshold. In the online FD decision, an event-triggered strategy is also involved in saving computational costs and network resources. The effectiveness and feasibility of the proposed hybrid FD method are illustrated through two simulation studies on nonlinear systems.

This article studies the issue of distributed hierarchical fixed-time optimal fault-tolerant output formation for heterogeneous multiagent systems (MASs). A two-layer formation control framework is proposed by using distributed optimization and cooperative fault-tolerant output regulation approaches. The upper layer includes a virtual system and a distributed fixed-time optimization control algorithm to produce a global optimal reference signal, which minimizes the global objective function in a fixed time. The lower layer includes an actual agent system and an adaptive fixed-time fault-tolerant tracking control protocol to compensate for the actuator faults and ensure the fixed-time tracking of the optimal formation trajectory composed of the optimal reference signal and the expected time-varying formation vector. Different from the relevant works, this framework can avoid the phenomenon of fault propagation between neighboring agents by the interaction network. Furthermore, the convergence time of the formation error is not relied on the initial conditions of MASs. Finally, two simulation experiments are designed to prove the effectiveness of the developed theoretics.

This article investigates the fixed-time fault-tolerant formation control problem of a leader–follower heterogeneous multiagent system (HMAS), including multiple unmanned aerial vehicles (UAVs) and multiple unmanned ground vehicles (UGVs) under loss of effectiveness actuator faults and disturbances. Different from the existing fixed-time formation results, to realize the special application, a finite-time performance function (FTPF) is considered, which can guarantee that formation error converges to a prescribed arbitrarily small region within a known time. Then, based on sliding mode control and bi-limit homogeneity, distributed and decentralized fixed-time formation control algorithms are constructed for the HMAS in the$x$–$y$axis and$z$axis, respectively, which can steer the whole system achieving target formation configuration within a scheduled time. In addition, based on local state information, adaptive online updating strategies for unknown actuator efficiency factors and lumped uncertainties are proposed. Then, under the online updating parameters, two novel distributed and decentralized adaptive fault-tolerant formation control laws are presented using practical fixed-time stability theory, which not only achieves stable formation tracking with finite-time prescribed behavioral metrics but also ensures that the formation errors are uniformly bounded within a settling time. Finally, the effectiveness of the developed control schemes is verified by simulation examples.

The disturbance decoupling-based fixed-time and predefined-time fault estimation issues are addressed for complex networks. A distributed fixed-time fault estimation observer subject to two power functions is first constructed, which can not only eliminate the effect of external disturbances but also identify unknown faults in a fixed time. Then, a nonlinear fixed-time fault estimator with two power functions, including state residuals, is presented to enhance the convergence speed of fault estimation. Furthermore, the predefined-time design strategy is investigated to assure the convergence of fault estimation errors in the specified time in advance. Finally, simulation results of complex networks of chaotic systems are shown to verify the advantage of the presented fixed-time and predefined-time schemes.

Short open reading frames (sORFs) refer to the small nucleic fragments no longer than 303 nt in length that probably encode small peptides. To date, translatable sORFs have been found in both untranslated regions of messenger ribonucleic acids (RNAs; mRNAs) and long non-coding RNAs (lncRNAs), playing vital roles in a myriad of biological processes. As not all sORFs are translated or essentially translatable, it is important to develop a highly accurate computational tool for characterizing the coding potential of sORFs, thereby facilitating discovery of novel functional peptides. In light of this, we designed a series of ensemble models by integrating Efficient-CapsNet and LightGBM, collectively termed csORF-finder, to differentiate the coding sORFs (csORFs) from non-coding sORFs in Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. To improve the performance of csORF-finder, we introduced a novel feature encoding scheme named trinucleotide deviation from expected mean (TDE) and computed all types of in-frame sequence-based features, such as i-framed-3mer, i-framed-CKSNAP and i-framed-TDE. Benchmarking results showed that these features could significantly boost the performance compared to the original 3-mer, CKSNAP and TDE features. Our performance comparisons showed that csORF-finder achieved a superior performance than the state-of-the-art methods for csORF prediction on multi-species and non-ATG initiation independent test datasets. Furthermore, we applied csORF-finder to screen the lncRNA datasets for identifying potential csORFs. The resulting data serve as an important computational repository for further experimental validation. We hope that csORF-finder can be exploited as a powerful platform for high-throughput identification of csORFs and functional characterization of these csORFs encoded peptides.

BACKGROUND: Protein histidine phosphorylation (pHis) plays critical roles in prokaryotic signal transduction pathways and various eukaryotic cellular processes. It is estimated to account for 6-10% of the phosphoproteome, however only hundreds of pHis sites have been discovered to date. Due to the inherent disadvantages of experimental methods, it is an urgent task for developing efficient computational approaches to identify pHis sites. RESULTS: Here, we present a novel tool, pHisPred, for accurately identifying pHis sites from protein sequences. We manually collected the largest number of experimental validated pHis sites to build benchmark datasets. Using randomized tenfold CV, the weighted SVM-RBF model shows the best performance than other four commonly used classification models (LR, KNN, RF, and MLP). From ten thousands of features, 140 and 150 most informative features were individually selected out for eukaryotic and prokaryotic models. The average AUC and F1-score values of pHisPred were (0.81, 0.40) and (0.78, 0.46) for tenfold CV on the eukaryotic and prokaryotic training datasets, respectively. In addition, pHisPred significantly outperforms other tools on testing datasets, in particular on the eukaryotic one. CONCLUSION: We implemented a python program of pHisPred, which is freely available for non-commercial use at https://github.com/xiaofengsong/pHisPred . Moreover, users can use it to train new models with their own data.

Wind turbines play an increasingly important role in renewable power generation. To ensure the efficient production and financial viability of wind power, it is crucial to maintain wind turbines’ reliability and availability (uptime) through advanced real-time condition monitoring technologies. Given their plurality and evolution, this article provides an updated comprehensive review of the state-of-the-art condition monitoring technologies used for fault diagnosis and lifetime prognosis in wind turbines. Specifically, this article presents the major fault and failure modes observed in wind turbines along with their root causes, and thoroughly reviews the techniques and strategies available for wind turbine condition monitoring from signal-based to model-based perspectives. In total, more than 390 references, mostly selected from recent journal articles, theses, and reports in the open literature, are compiled to assess as exhaustively as possible the past, current, and future research and development trends in this substantial and active investigation area.

Self-triggered pseudo terminal ingredients are proposed in this study for the classic dual-mode robust model predictive control (RMPC) of discrete-time linear systems. By resorting to the off-line design of pseudo terminal ingredients, viz. pseudo terminal set and pseudo terminal cost, the optimization problem to be solved online is transformed into several subproblems with short prediction horizons. Furthermore, the controller design is based on the nominal system state, which enables a self-triggered mechanism during the implementation. The proposed approach is able to steer the system state into a predetermined terminal constraint set via intermittent samplings. The resultant computational and communicational burden for the online part is significantly reduced, which brings great convenience for band-limited practical systems. Simulations demonstrate the effectiveness of the proposed approach.

Intending to address the issue of sensor fault-tolerant control, a model-free adaptive fault-tolerant constrained control strategy is proposed with virtual sensor technology. The novel introduction of observer-based model-free adaptive control to fault-tolerant control brings benefits to the nonlinear systems with unknown dynamics in the presence of sensor faults. In the design procedures, the dynamic linearization technique is utilized first to establish the equivalent linear model of nonlinear systems. Then, the virtual sensor technology is introduced by designing the novel state-observer-based estimation algorithm to fulfill the data-driven system dynamic and sensor fault modeling. Besides, the broad learning technique is employed with offline training to provide output information for observer-based data-driven modeling under the fault case. Based on the data-driven model, the fault-tolerant constrained control scheme is explored with the anti-windup compensator against input constraints caused by actuator saturation, and the stability analysis is supplied. Finally, the simulations are carried out for both affine and non-affine nonlinear systems to manifest the effectiveness and superiority of the proposed control strategy.

Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships among variables. These assumptions may be improper in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal. With the aid of neural networks, this work proposes a new nonlinear counterpart called a single-side CCA (SsCCA) to enhance FD performance. The contributions of this work are four-fold: 1) an objective function for the nonlinear CCA is first reformulated, based on which a generalized solution is presented; 2) for the practical implementation, a particular solution of SsCCA is developed; 3) an SsCCA-based FD algorithm is designed for nonlinear systems, whose optimal FD ability is illustrated via theoretical analysis; and 4) based on the difference in FD results between two test statistics, fault diagnosis can be directly achieved. The studies on a nonlinear three-tank system are carried out to verify the effectiveness of the proposed SsCCA method.

Data-driven fault detection and isolation (FDI) depends on complete, comprehensive, and accurate fault information. Optimal test selection can substantially improve information achievement for FDI and reduce the detecting cost and the maintenance cost of the engineering systems. Considerable efforts have been worked to model the test selection problem (TSP), but few of them considered the impact of the measurement uncertainty and the fault occurrence. In this article, a conditional joint distribution (CJD)-based test selection method is proposed to construct an accurate TSP model. In addition, we propose a deep copula function which can describe the dependency among the tests. Afterward, an improved discrete binary particle swarm optimization (IBPSO) algorithm is proposed to deal with TSP. Then, application to an electrical circuit is used to illustrate the efficiency of the proposed method over two available methods: 1) joint distribution-based IBPSO and 2) Bernoulli distribution-based IBPSO.

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%.

Remaining useful life (RUL) prediction is a reliable tool for the health management of components. The main concern of RUL prediction is how to accurately predict the RUL under uncertainties. In order to enhance the prediction accuracy under uncertain conditions, the relevance vector machine (RVM) is extended into the probability manifold to compensate for the weakness caused by evidence approximation of the RVM. First, tendency features are selected based on the batch samples. Then, a dynamic multistep regression model is built for well describing the influence of uncertainties. Furthermore, the degradation tendency is estimated to monitor degradation status continuously. As poorly estimated hyperparameters of RVM may result in low prediction accuracy, the established RVM model is extended to the probabilistic manifold for estimating the degradation tendency exactly. The RUL is then prognosticated by the first hitting time (FHT) method based on the estimated degradation tendency. The proposed schemes are illustrated by a case study, which investigated the capacitors' performance degradation in traction systems of high-speed trains.

In this article, the fault-tolerant synchronization and time-varying tracking control problem is investigated for nonlinear multivehicle systems (MVSs) in the presence of partial loss-of-control-effectiveness (LoCE) faults. Based on the graph theory, a two-level fault-tolerant cooperative control framework is proposed, namely, the low-level distributed nominal control scheme and the high-level topology reconfiguration protocols. The low-level scheme is developed to guarantee system performances in the fault-free scenario. With the low-level scheme, the high-level topology reconfiguration protocols, each of which corresponds to one partial LoCE fault scenario, are then proposed to mitigate the fault impact by adjusting the underlying topology. Accordingly, without modifying the structure or the design parameter of the low-level control scheme, the proposed framework can guarantee the synchronization and tracking errors of the MVS asymptotically convergent to zero in both fault-free and fault scenarios. Finally, the effectiveness of the proposed control method is verified via a simulation study of three degree-of-freedom helicopters.

This article investigates a distributed fractional-order fault-tolerant formation-containment control (FOFTFCC) scheme for networked unmanned airships (UAs) to achieve safe observation of a smart city. In the proposed control method, an interval type-2 fuzzy neural network (IT2FNN) is first developed for each UA to approximate the unknown term associated with the loss-of-effectiveness faults in the distributed error dynamics, and then a disturbance observer (DO) is proposed to compensate for the approximation error and bias fault encountered by each UA, such that the composite learning strategy composed of the IT2FNN and the DO is obtained for each UA. Moreover, fractional-order (FO) calculus is incorporated into the control scheme to provide an extra degree of freedom for the parameter adjustments. The salient feature of the proposed control scheme is that the composite learning algorithm and FO calculus are integrated to achieve a satisfactory fault-tolerant formation-containment control performance even when a portion of leader/follower UAs is subjected to the actuator faults in a distributed communication network. Furthermore, it is shown by Lyapunov stability analysis that all leader UAs can track the virtual leader UA with time-varying offset vectors, and all follower UAs can converge into the convex hull spanned by the leader UAs. Finally, comparative hardware-in-the-loop (HIL) experimental results are presented to show the effectiveness and superiority of the proposed method.

In this article, a robust asymptotic fault estimation (RAFE) design is proposed for discrete-time interconnected systems with sensor faults. By constructing a singular augmented system, an equivalent description of the considered interconnected systems is presented. Then, a novel RAFE observer is proposed for the singular augmented system. Furthermore, gain matrices of the RAFE observer are calculated based on multiconstrained design. Simulation results are illustrated to show the feasibility of the presented approaches.

This discusses the fault estimation (FE) and fault accommodation (FA) methods for fractional-order systems. First, two Lyapunov theorems of input-to-state practical stability are presented for fractional-order systems, based on which an adaptive FE/FA scheme is provided. Such a scheme ensures the faulty system is input-to-state practically stable (ISpS) with respect to estimation errors. Furthermore, new results are extended to fractional-order switched and interconnected systems: for the former, an FE/FA method is proposed which fully reveals the tradeoff between the value of the fractional order, each mode's dynamics, and switching law; for the latter, a cyclic-small-gain theorem of the fractional-order system is given under which both decentralized and distributed FE/FA methods are proposed.

This article proposes a fractional-order intelligent fault-tolerant synchronization tracking control (FO-I-FTSTC) scheme for multiple unmanned airships (UAs) against actuator faults. Within the developed control architecture, fixed-time prescribed performance functions (PPFs) are first designed to transform the synchronization tracking errors into a new set of error variables, such that the original errors are strictly confined within the prescribed bounds. Then, fractional calculus and sliding mode surface are sequentially introduced to construct the FO errors. Moreover, to handle the unknown terms and bias faults in the FO sliding-mode error dynamics, fuzzy neural networks with recurrent loops are artfully constructed to act as the intelligent learning units. Furthermore, the norm of the loss-of-effectiveness fault factors is introduced for each UA to reduce the number of adaptive parameters. The distinct feature of the proposed method is that the FO-I-FTSTC performance is significantly enhanced by integrating recurrent fuzzy neural networks, fractional calculus, and fixed-time PPFs into a unified framework, leading to a high-precision control scheme. It is shown by Lyapunov analysis that all UAs can track their desired references in a synchronized manner, and the synchronization tracking errors are bounded and strictly confined within the prescribed error bounds. Comparative hardware-in-the-loop experiments are presented to show the effectiveness of the proposed FO-I-FTSTC scheme.

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems, great challenges which may prevent successful FDD implementations on practical high-speed trains are then summarized in detail. Benefiting from theoretical developments of data-driven FDD strategies, instructive perspectives on this topic are further elaborately conceived by the integration of model-based FDD issues, system identification techniques, and new machine learning tools, which provide several promising solutions to FDD strategies for traction systems in high-speed trains.

With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.

This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme.

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

This article considers the decentralized fractional-order fault-tolerant control problem for unmanned aerial vehicles (UAVs) against wind disturbances and actuator faults in a directed communication network. A new composite adaptive disturbance observer-based decentralized fractional-order fault-tolerant control (CADOB-DFO-FTC) scheme, which incorporates fractional-order (FO) sliding-mode surfaces, nonlinear disturbance observers (NDOs), fuzzy wavelet neural networks (FWNNs), and robust controllers, is developed to achieve the attitude tracking control of networked UAVs in a decentralized way. Based on the FO sliding-mode surfaces, the NDOs are first developed to estimate the lumped uncertainties due to the aerodynamic parameter perturbations, wind disturbances, and actuator faults. Then, adaptive FWNNs with updating weighting matrices, mean vectors, and deviation vectors are constructed to effectively attenuate the adverse effects induced by the NDO estimation errors. Furthermore, to compensate the FWNN approximation errors, robust controllers are integrated into the developed control scheme to enhance the approximation abilities. It is shown that by using Lyapunov methods, all UAVs can track their attitude references. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed method.

This paper proposes a novel distributed fault-tolerant consensus tracking control design for multi-agent systems with abrupt and incipient actuator faults under fixed and switching topologies. The fault and state information of each individual agent is estimated by merging unknown input observer in the decentralized fault estimation hierarchy. Then, two kinds of distributed fault-tolerant consensus tracking control schemes with average dwelling time technique are developed to guarantee the mean-square exponential consensus convergence of multi-agent systems, respectively, on the basis of the relative neighboring output information as well as the estimated information in fault estimation. Simulation results demonstrate the effectiveness of the proposed fault-tolerant consensus tracking control algorithm.

This paper investigates the fixed-time time-varying formation control problems for heterogeneous multi-agent systems (MASs) composed of multiple Unmanned Ground Vehicles (UGVs) and multiple Unmanned Aerial Vehicles (UAVs) in the presence of actuator faults, parameter uncertainties, matched and mismatched disturbances. Besides achieving the desired formation configurations, each follower can also track the position trajectory produced by the virtual leader within fixed time simultaneously. The difference dynamic characteristics between the heterogeneous agents leads to unbalanced interaction of lumped uncertainties in the communication network, which increases the difficulty of collaborative control. To estimate the mismatched disturbances and lumped uncertainties, a fixed-time observer for each follower is designed, which can guarantee the estimation errors converge to the origin in fixed settling time. Subsequently, by utilizing the backstepping technique and the fixed-time stability theory, an observer-based distributed fixed-time formation controller for each follower in the X- Y axes and the observer-based decentralized fixed-time tracking controllers for follower-UAVs in the Z axes are presented, which are shown to be fixed-time stable even under the influence of actuator faults and mismatched disturbances. Moreover, the fixed-time results can ensure the convergence time is independent of initial conditions. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithms.

Degradation prognostics of aero-engine are a well-recognized challenging issue. Data-driven prognostic techniques have been receiving attention because they rely on neither expert knowledge nor mathematic model of the system. But they are highly dependent on the quantity and quality of degradation data. To solve the problems caused by unlabeled, unbalanced condition monitoring (CM) data and uncertainties of the prognostics process, a novel data-driven aero-engine degradation prognostic strategy is proposed in this article. First, two indicators are defined to remove redundant degradation features. Then, the number of discrete states of health is determined by a fuzzy c -means algorithm, and the health state labels can be automatically assigned for health state estimation, where the uncertain initial condition and the uncertainty of health state's transition are fully considered. Finally, a multivariate health estimation model and a multivariate multistep-ahead long-term degradation prediction model are proposed for remaining useful life estimation for aero-engines. Verification results using the aero-engine data from NASA can show that the proposed data-driven degradation prognostic strategy is effective and feasible.

This article studies the distributed fault estimation (DFE) and fault-tolerant control for continuous-time interconnected systems. Using associated information among subsystems to design the DFE observer can improve the accuracy of fault estimation of the interconnected systems. Based on the static output feedback (SOF), the global outputs of the interconnected systems are used to construct a distributed fault-tolerant control (DFTC). The multiconstrained methods are proposed to enhance the transient performance and ability to suppress the external disturbances simultaneously. The conditions of the presented design methods are expressed in terms of linear matrix inequalities. The simulation results are illustrated to show the feasibility of the presented approaches.

Crack is the earlier indication of concrete structural severe damage; it plays an important role in structure health monitoring (SHM) of industrial civil infrastructures (such as buildings, bridges, roads, dams, etc.). Crack damage classification is the first and critical stage for concrete SHM. However, commonly used human visual classification is costly, labor-intensive, and unreliable, other machine learning based classification methods also have some drawbacks. To address these problems, this article proposes a cascade broad neural network architecture for concrete surface structural crack damage automated classification, which generates an effective and efficient framework with much less hyper-parameters than deep neural networks, and sufficiently explores the advantages of multilevel cascades of classifier ensemble. Experimental results on four challenging datasets demonstrate that its performance is quite more excellent than current mainstream classification methods (both in testing accuracy and training time).

Nowadays vehicle type classification is a fundamental part of intelligent transportation systems (ITSs) and is widely used in various applications like traffic flow monitoring, security enforcement, and autonomous driving, etc. However, vehicle classification is usually used in supervised learning, which greatly limits the applicability for real ITS. This article proposes a semisupervised vehicle type classification scheme via ensemble broad learning for ITS. This presented method contains two main parts. In the first part, a collection of base broad learning system (BLS) classifiers is trained by semisupervised learning to avoid time-consuming training process and alleviate the increasingly unlabeled samples burden. In the second part, a dynamic ensemble structure constructed by trained classifier groups with different characteristics obtains the highest type probability and determine which the vehicle belongs, so as to achieve superior generalization performance than a single base classifier. Several experiments conducted on the pubic BIT-Vehicle dataset and MIO-TCD dataset demonstrate that the proposed method outperforms single BLS classifier and some mainstream methods on effectiveness and efficiency.

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme.

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles.

Persistent actuator failures can cause uncertain system dynamics mutation including minimum and nonminimum phase switching, which motivates us to develop a robust adaptive control scheme using a control-separation-based linear quadratic (LQ) design. It is shown that such a system can be expressed by a parameterized time-varying input-output model plus an actuator failure dependent signal and a persistent signal which captures the effect of the system dynamics mutation. A control-separation LQ design has the specific desired functions for output regulation and failure compensation as well, and is applied and analyzed for solving the persistent actuator failure compensation problem. Both nominal control design for system known and adaptive control design for system unknown are developed. The desired control performance is ensured by a robust adaptive control technique and is fully characterized in terms of a small in the mean output regulation, that is, the mean value of the system output is bounded by the average number of actuator failures over time and the average actuator failure value. Such desired system performance is shown by a complete analytical study and demonstrated by an illustrative simulation study.

This paper addresses the fault-tolerant control (FTC) issue for a class of multilayer interconnected nonlinear systems by using the inclusion principle. First, an overlapping decomposition method is proposed that expands the original interconnected system into a new one where each layer is not overlapped with each other. Second, for such an expanded system, the coupling effects among multiple layers are analyzed by using cyclic-small-gain theorem, and FTC schemes based on layer cooperation are further developed. Finally, the control designed for the expanded system is contracted back to the original interconnected system to achieve its FTC goal. An example of multiple pendulums is taken to illustrate the efficiency and applicability of the obtained theoretical results.

This paper proposes a novel decentralized output sliding-mode fault-tolerant control (FTC) design for heterogeneous multiagent systems (MASs) with matched disturbances, unmatched nonlinear interactions, and actuator faults. The respective iteration and iteration-free algorithms in the sliding-mode FTC scheme are designed with adaptive upper bounding laws to automatically compensate the matched and unmatched components. Then, a continuous fault-tolerant protocol in the observer-based integral sliding-mode design is developed to guarantee the asymptotic stability of MASs and the ultimate boundedness of the estimation errors. Simulation results validate the efficiency of the proposed FTC algorithm.

In order to deal with the speed cooperative control problem in the multiple linear induction traction systems consists of multiple linear induction motors, a model-free cooperative adaptive sliding-mode-constrained-control strategy is proposed considering the input magnitude and rate constraints which may cause the problem of actuator and integral saturation. First, the equivalent circuit topology of the single motor in the system is investigated. Besides, the system is considered as the multiagent system with fixed communication topology due to the interaction between adjacent motors. Then, the output observer is presented to estimate the output and the estimation algorithm of pseudo-partial derivative parameter and uncertainties is proposed. Based on the above, the proposed control scheme is presented by designing an integral sliding-mode surface containing the systematic error and an anti-windup compensator is added to eliminate the saturation. Finally, the simulations of the proposed control strategy for multiagent systems are carried out to demonstrate the effectiveness and superiority of the proposed control strategy.

With the aim to solve the problems related to the power distribution and current chattering in a distributed energy storage system (DESS), which can be considered as a multiagent system in dc microgrid, a model-free cooperative sliding mode control scheme with a directed-graph-based observer is proposed in this article. First, a state-of-charge (SoC)-based droop control strategy is combined with a directed-graph-based voltage regulation method to balance the SoCs of each energy storage unit (ESU), allocate the power, and maintain the bus voltage stability. Besides, a directed-graph-based observer is designed to estimate the battery current considering the communication between the ESUs, and based on this observer, an estimation algorithm for adaptive coefficients is developed. Then, the multiagent sliding mode control strategy with an antiwindup compensator of input constraint is developed to track the reference current with little chattering in the DESS, and the stability proof is given. Finally, simulations are conducted to validate the effectiveness and superiority of the newly designed control strategy for the DESS.

Fault-tolerant cooperative control of multiagent systems has attracted ever-increasing attention in recent years due to the fact that multiple agents can provide much more redundancy than a single agent system, thereby making the fault tolerant cooperative control design more flexible. However, multiagent systems may bring severe challenges that do not exist in single-agent systems. This article aims at presenting a survey of trends and methodologies of fault tolerant cooperative control in multiagent systems. Depending on the countermeasure against the faults, the existing fault-tolerant cooperative control methodologies are first classified into four categories: Individual methodologies, cooperative methodologies, topology reconfiguration-based methodologies, and composition reconfiguration-based methodologies. Then the characteristics and implementation schemes of four categories of methodologies are discussed in detail. Furthermore, the applicability of fault tolerant cooperative control in smart grids is outlined. Finally, several challenging issues are envisioned for future research.

High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. The first objective of this paper is to present a comprehensive review on the fault detection and diagnosis (FDD) techniques for high-speed trains. The second purpose of this work is, motivated by the pros and cons of the FDD methods for high-speed trains, to provide researchers and practitioners with informative guidance. Then, the application of FDD for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years. Finally, the challenges and promising issues are speculated for the future investigation.

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 article addresses the distributed optimal fault-tolerant control (FTC) issue by using the two-level game approach for a class of nonlinear interconnected systems, in which each subsystem couples with its neighbors through not only the states but also the inputs. At the first level, the FTC problem for each subsystem is formulated as a zero-sum differential game, in which the controller and the fault are regarded as two players with opposite interests. At the second level, the whole interconnected system is formulated as a graphical game, in which each subsystem is a player to achieve the global Nash equilibrium for the overall system. The rigorous proof of the stability of the interconnected system is given by means of the cyclic-small-gain theorem, and the relationship between the local optimality and the global optimality is analyzed. Moreover, based on the adaptive dynamic programming (ADP) technology, a distributed optimal FTC learning scheme is proposed, in which a group of critic neural networks (NNs) are established to approximate the cost functions. Finally, an example is taken to illustrate the efficiency and applicability of the obtained theoretical results.

This paper investigates the attitude and position tracking control problem of the Lead–Wing close formation system with unknown multiplicative actuator faults. In close formation flight, the movement of the Wing unmanned aerial vehicle (UAV) is influenced by the vortex effects of the neighboring Lead UAV. This situation requires a modeling of the aerodynamic coupling vortex effects and linearization on the basis of optimal close formation geometry. Adaptive actuator fault parameters are identified by merging the improved unknown input observers, and adaptive laws with projection functions are presented for actuator fault tracking. The sufficient condition corresponding to the existence of unknown input observers is given. Then, an adaptive fault-tolerant H-infinity output feedback control scheme is developed to guarantee the asymptotic stability, H-infinity closed-loop system performance, and attitude and position tracking properties while the Lead UAV is being maneuvered. Simulation results of the Lead–Wing close formation flight validate the efficiency of the proposed fault-tolerant control algorithm.

An effective strategy for analyzing and diagnosing multiple faults is developed, based on the concise causality structure obtained by multisignal modeling and the fault source signals extracted by blind source separation (BSS). The key idea is enlightened by the need to handle the redundant test signals and the multiple fault ambiguity groups when applying multisignal modeling for multiple fault diagnosis. Considering that BSS is inherently suitable to extract the independent source information, it is integrated into the multisignal model to reconstruct the causality structure that will have superior diagnosticability. Preliminary study on test point design is also presented in the proposed strategy. The proposed multiple fault diagnosis strategy has been verified on a hydraulic automatic gauge control simulation system in a cold rolling mill. Results show that it can use less test information to effectively diagnose all simulated single and multiple faults.

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/.

This paper addresses the fault estimation (FE) and accommodation issues of interconnected systems by using two new concepts namely interconnected separation principle and constrained interconnected separation principle that allow for the separate design not only between diagnostic observer and fault tolerant controller for each subsystem, but also between observer/controller of each subsystem and those of other ones. Sufficient fault recoverability conditions are established, under which both distributed and decentralized FE and accommodation schemes are provided. The new results help to provide a framework for observer-based fault diagnosis and fault tolerant control of interconnected systems, and are further applied to the meta aircraft configuration that consists of multiple aircraft joined together to illustrate their efficiency.

In this paper, a new distributed fault estimation observer with adjustable parameters is designed for a class of nonlinear interconnected systems. The presented fault estimator consists of proportional and integral terms to improve the accuracy of fault estimation. The observer gain matrices of the proposed fault estimation scheme for the underlying systems are calculated based on robust${\mathcal {L}_{2}-\mathcal {L}_{2}}$and${\mathcal {L}_{2}-\mathcal {L}_\infty }$performance. The proposed method achieves a lower performance level in the aspect of quantitative analysis compared with existing fault estimation approaches. A simulation example is provided to demonstrate the effectiveness of the new design method.

Incipient faults in high-speed trains are usually masked by noises and disturbances from process and sensors, which severely increases the difficulty of incipient fault detection and diagnosis. By introducing Hellinger distance into multivariate statistical analysis framework, this paper develops a robust detection and diagnosis method for incipient faults under the principal component analysis. The proposed method can detect all incipient sensor faults in traction systems of high-speed trains in real time by comparing reference probability density functions (PDFs) with the online estimated PDFs. According to the fault detection information, an accurate fault diagnosis can be achieved online through Bayesian inference. Key advantages of the proposed method are its salient robustness to unknown noises and disturbances, as well as the high sensitivity to incipient faults. In addition, the proposed method does not require any information on system models of high-speed trains or any human intervention. The effectiveness of the proposed method has been firstly proven by mathematical derivations and then been verified by numerical simulations. Finally, the proposed method has been applied to the practical experiment platform of the high-speed trains.

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.

Hot spots are common abnormalities in photovoltaic (PV) energy systems. Their presence can potentially cause damage to PV modules, such as performance degradation or even unexpected fire to PV energy systems. By sufficiently mining the information hidden in the test data collected from PV modules, this paper develops a space-to-space projection method, which at its core is a linear approach via preserving the locally geometrical structure with respect to time series. Based on the nonlinear model of PV modules established via the proposed projection, data-driven detection of hot spots in PV energy systems can be directly achieved with three key advantages: 1) its implementation does not depend on any mathematical model or physical knowledge of PV energy systems; 2) it is of high-computational efficiency especially in the online detection phase; and 3) it can capture the dynamic characteristic because the local structure of samplings regarding time is given sufficient consideration. The effectiveness and feasibility of the proposed approach are first presented by theoretical analysis and, then, convictively demonstrated via 15 sets of hot spot experiments on practical PV modules.

In this paper, a novel descriptor estimator-based incipient fault estimation scheme is designed for Lipschitz nonlinear descriptor systems with process disturbances and measurement output noises. By using the proposed estimator, incipient sensor faults, abrupt actuator faults, and measurement noises can be estimated asymptotically. Application results conducted on a three-phase inverter system of China railway high-speed trains are given to illustrate the effectiveness of the developed approach. The main contributions are summarized in two aspects: 1) the unified framework designed for actuator and sensor fault reconstruction/estimation problem is capable of dealing with actuator and sensor faults at the same time and 2) a linear matrix inequality optimization method is formulated to achieve optimal performance of signal estimation.

This paper proposes a decentralized fault tolerant methodology for a class of interconnected nonlinear systems. The key novelty of our proposed method is that fault tolerant control can be achieved without necessarily exchanging the state information between the subsystems and the couplings' effect can be dealt with utilizing the cyclic-small-gain methodology. Simulation results demonstrate effectively the validity of our proposed approach.

This short paper develops an adaptive compensation control scheme for two physically linked two-wheel-drive mobile robots with multiple actuator faults. Kinematic and dynamic models are first proposed. Then, an adaptive control scheme is designed, which ensures system stability and asymptotic tracking properties. Simulation results verify its effectiveness.

This paper studies the fault tolerant safe control of nonlinear systems. The proposed theory ensures that the state of the system is away from the unsafe set, and asymptotically stable as well in the presence of faults. The theoretical results are applied to an attitude dynamics of a hypersonic vehicle with actuator faults.

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

In this paper, a novel adjustable parameter (AP)-based distributed fault estimation observer (DFEO) is proposed for multiagent systems (MASs) with the directed communication topology. First, a relative output estimation error is defined based on the communication topology of MASs. Then a DFEO with AP is constructed with the purpose of improving the accuracy of fault estimation. Based on${H} _{{\infty }}$and${H} _{{2}}$with pole placement, multiconstrained design is given to calculate the gain of DFEO. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed DFEO design with AP.

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.

An immune optimization algorithm, based on the model of biological immune system, is proposed to solve multiobjective optimization problems with multimodal nonlinear constraints. First, the initial population is divided into feasible nondominated population and infeasible/dominated population. The feasible nondominated individuals focus on exploring the nondominated front through clone and hypermutation based on a proposed affinity design approach, while the infeasible/dominated individuals are exploited and improved via the simulated binary crossover and polynomial mutation operations. And then, to accelerate the convergence of the proposed algorithm, a transformation technique is applied to the combined population of the above two offspring populations. Finally, a crowded-comparison strategy is used to create the next generation population. In numerical experiments, a series of benchmark constrained multiobjective optimization problems are considered to evaluate the performance of the proposed algorithm and it is also compared to several state-of-art algorithms in terms of the inverted generational distance and hypervolume indicators. The results indicate that the new method achieves competitive performance and even statistically significant better results than previous algorithms do on most of the benchmark suite.

This paper addresses the problem of fault estimation observer design with finite-frequency specifications for discrete-time Takagi-Sugeno (T-S) fuzzy systems. First, for such T-S fuzzy models, an H∞ fault estimation observer with pole-placement constraint is proposed to achieve fault estimation. Based on the generalized Kalman-Yakubovich-Popov lemma, the given finite-frequency observer possesses less conservatism compared with the design of the entire-frequency domain. Furthermore, the performance of the presented fault estimation observer is further enhanced by adding the degree of freedom. Finally, two examples are presented to illustrate the effectiveness of the proposed strategy.

In this paper, a dynamic surface control (DSC) scheme is proposed for a class of uncertain strict-feedback nonlinear systems in the presence of input saturation and unknown external disturbance. The radial basis function neural network (RBFNN) is employed to approximate the unknown system function. To efficiently tackle the unknown external disturbance, a nonlinear disturbance observer (NDO) is developed. The developed NDO can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set. Using NDO and RBFNN, the DSC scheme is developed for uncertain nonlinear systems based on a backstepping method. Using a DSC technique, the problem of explosion of complexity inherent in the conventional backstepping method is avoided, which is specially important for designs using neural network approximations. Under the proposed DSC scheme, the ultimately bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results are given to show the effectiveness of the proposed DSC design using NDO and RBFNN.

In this paper, a stable multivariable model reference adaptive control (MRAC) scheme is proposed for a four-rotor helicopter with unknown external disturbance. Firstly, the disturbance observer is designed to well monitor the unknown disturbance. And then, the adaptive controller is developed based on the disturbance observer to compensate the external disturbance and track the desired system states. Finally, the simulation results illustrate the effectiveness of the proposed adaptive control scheme.

In this paper, two adaptive observer-based strategies are proposed for control of nonlinear processes using input/output (I/O) data. In the two strategies, pseudo-partial derivative (PPD) parameter of compact form dynamic linearization and PPD vector of partial form dynamic linearization are all estimated by the adaptive observer, which are used to dynamically linearize a nonlinear system. The two proposed control algorithms are only based on the PPD parameter estimation derived online from the I/O data of the controlled system, and Lyapunov-based stability analysis is used to prove all signals of close-loop control system are bounded. A numerical example, a steam-water heat exchanger example and an experimental test show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.

In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

The problem of H∞ state feedback control of affine nonlinear discrete-time systems with unknown dynamics is investigated in this paper. An online adaptive policy learning algorithm (APLA) based on adaptive dynamic programming (ADP) is proposed for learning in real-time the solution to the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in the H∞ control problem. In the proposed algorithm, three neural networks (NNs) are utilized to find suitable approximations of the optimal value function and the saddle point feedback control and disturbance policies. Novel weight updating laws are given to tune the critic, actor, and disturbance NNs simultaneously by using data generated in real-time along the system trajectories. Considering NN approximation errors, we provide the stability analysis of the proposed algorithm with Lyapunov approach. Moreover, the need of the system input dynamics for the proposed algorithm is relaxed by using a NN identification scheme. Finally, simulation examples show the effectiveness of the proposed algorithm.

The problem of fault-tolerant dynamic surface control (DSC) for a class of uncertain nonlinear systems with actuator faults is discussed and an active fault-tolerant control (FTC) scheme is proposed. Using the DSC technique, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, an accommodation scheme is proposed to compensate for both actuator time-varying gain and bias faults, and avoids the controller singularity. In addition, the proposed controller guarantees that all signals of the closed-loop system are semiglobally uniformly ultimately bounded, and converge to a small neighborhood of the origin. Finally, the effectiveness of the proposed FTC approach is demonstrated on a simulated aircraft longitudinal dynamics example.

It is well known that there always exist some level of time delay between fault occurrence and fault accommodation, which is called as the time delay due to fault diagnosis (TDDTFD) in this paper. TDDTFD may cause severe loss of system performance and stability. This paper investigates the TDDTFD's adverse effect on the system performance. First, a fault diagnosis (FD) model is constructed to diagnose sensor faults which integrate time-varying gain and bias faults, where a novel FD algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Meanwhile, the time spent at each step in FD and its analytical expression are derived strictly. Further, the analysis of the system performance degraded by TDDTFD is developed, and the conditions under which the magnitudes of sensor faults should be satisfied such that the state of the faulty system controlled by the normal controller remains bounded during TDDTFD are derived. In addition, the corresponding solutions are proposed to minimize the adverse effect of the time delay. Finally, simulation results of near-space vehicle attitude dynamics are presented to demonstrate the efficiency of the proposed approach.

In this paper, the cooperative adaptive fault tolerant fuzzy tracking control (CAFTFTC) problem of networked high-order multiagent with time-varying actuator faults is studied, and a novel CAFTFTC scheme is proposed to guarantee that all follower nodes asymptotically synchronize a leader node with tracking errors converging to a small adjustable neighborhood of the origin in spite of actuator faults. The leader node is modeled as a higher order nonautonomous nonlinear system. It acts as a command generator giving commands only to a small portion of the networked group. Each follower is assumed to have nonidentical unknown nonlinear dynamics, and the communication network is also assumed to be a weighted directed graph with a fixed topology. A distributed robust adaptive fuzzy controller is designed for each follower node such that the tracking errors are cooperative uniform ultimate boundedness (CUUB). Moreover, these controllers are distributed in the sense that the controller designed for each follower node only requires relative state information between itself and its neighbors. The adaptive compensation term of the optimal approximation errors and external disturbances is adopted to reduce the effects of the errors and disturbances, which removes the assumption that the upper bounds of unknown function approximation errors and disturbances should be known. Analysis of stability and parameter convergence of the proposed algorithm are conducted that are based on algebraic graph theory and Lyapunov theory. Comparing with results in the literature, the CAFTFTC scheme can minimize the time delay between fault occurrence and accommodation and reduce its adverse effect on system performance. In addition, the FTC scheme requires no additional fault isolation model, which is necessary in the traditional active FTC scheme. Finally, an example is provided to validate the theoretical results.

This paper addresses the problem of fault-tolerant control (FTC) for near-space vehicle (NSV) attitude dynamics with actuator faults, which is described by a Takagi-Sugeno (T-S) fuzzy model. First, a general actuator fault model that integrated varying bias and gain faults, which are assumed to be dependent on the system state, is proposed. Then, sliding mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, for the two cases where the state is available or not, two accommodation schemes are proposed to compensate for the effect of the faults. These schemes do not need the condition that the bounds of the time derivative of the faults should be known. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of NSV are presented to demonstrate the efficiency of the proposed FTC approach.

This paper addresses the issues of robust H∞reliable control for a class of uncertain switched nonlinear systems with actuator failures. A new method of settling a class of uncertain matrices multiply phenomena is proposed. A sufficient condition of the existence of the state feedback controller, guaranteeing the closed-loop systems is of a specified H∞performance, is proposed. Meanwhile, the design method of the reliable controller is given. At the end of this paper, an illustrating example of the proposed method is provided.

This paper develops a new solution framework for adaptive output feedback fuzzy control systems aimed at effectively dealing with nonlinear systems with multiple input-multiple output (MIMO) delays and in the presence of dynamics and actuator failure uncertainties. Takagi-Sugeno (T-S) fuzzy systems are employed to represent nonlinear systems, which have desired capacity for dynamic system approximation with parametric and structural properties suitable for using rigorous feedback control techniques. A multiple-delay fuzzy system prediction model is derived and its system properties are clarified. Such a prediction model enables the use of a model-based approach for fuzzy control. The design and analysis are presented for an adaptive control scheme for multiple-delay T-S fuzzy systems, as well as for an adaptive actuator failure compensation scheme for systems with redundant actuators subject to uncertain failures, for which new system parametrizations and controller structures are developed. Simulation results are presented to demonstrate the studied new concepts and to verify the desired performance of the new types of adaptive fuzzy control systems.

This paper addresses the problem of fault-tolerant control for Takagi-Sugeno (T-S) fuzzy systems with actuator faults. First, a general actuator fault model is proposed, which integrates time-varying bias faults and time-varying gain faults. Then, sliding-mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault-diagnostic algorithm is proposed to estimate the actuator fault, which removes the classical assumption that the time derivative of the output errors should be known as in some existing work. Further, a novel fault-estimation observer is designed. Utilizing the estimated actuator fault, an accommodation scheme is proposed to compensate for the effect of the fault. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of a near-space hypersonic vehicle are presented to demonstrate the efficiency of the proposed approach.

This paper studies the problem of robust fault estimation (FE) observer design for discrete-time Takagi–Sugeno (T–S) fuzzy systems via piecewise Lyapunov functions. Both the full-order FE observer (FFEO) and the reduced-order FE observer (RFEO) are presented. The objective of this paper is to establish a novel framework of the FE observer with less conservatism. First, under the multiconstrained design, an FFEO is proposed to achieve FE for discrete-time T–S fuzzy models. Then, using a specific coordinate transformation, an RFEO is constructed, which results in a new fault estimator to realize FE using current output information. Furthermore, by the piecewise Lyapunov function approach, less conservative results on both FFEO and RFEO are derived by introducing slack variables. Simulation results are presented to illustrate the advantages of the theoretic results that are obtained in this paper.

This paper addresses the problem of integrated robust fault estimation (FE) and accommodation for discrete-time Takagi–Sugeno (T–S) fuzzy systems. First, a multiconstrained reduced-order FE observer (RFEO) is proposed to achieve FE for discrete-time T–S fuzzy models with actuator faults. Based on the RFEO, a new fault estimator is constructed. Then, using the information of online FE, a new approach for fault accommodation based on fuzzy-dynamic output feedback is designed to compensate for the effect of faults by stabilizing the closed-loop systems. Moreover, the RFEO and the dynamic output feedback fault-tolerant controller are designed separately, such that their design parameters can be calculated readily. Simulation results are presented to illustrate our contributions.

Global asymptotic stability problem is studied for a class of recurrent neural networks with distributed delays satisfying Lebesgue-Stieljies measures on the basis of linear matrix inequality. The concerned network model includes many neural network models with various delays and structures as its special cases, such as the delays covering the discrete delays and distributed delays, and the network structures containing the neutral-type networks and high-order networks. Therefore, many new stability criteria for the above neural network models have also been derived from the present stability analysis method. All the obtained stability results have similar matrix inequality structures and can be easily checked. Three numerical examples are used to show the effectiveness of the obtained results.

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.

A fault detection and diagnosis (FDD) method is proposed by combining transfer entropy (TE) and signed digraph (SDG). Given process historical data, transfer entropy is used to construct a SDG model to represent causal relationship between process variables. A fault severity evaluation method is then proposed based on the modified SDG model, where the nodes can take values of (0), (±1), (±3) and (±6). Then, an index named DoF is developed to measure fault severity. The application results can verify the effectiveness and feasibility of the proposed method.

Based on the adaptive-control technique, this paper deals with the problem of fault-tolerant tracking control for near-space-vehicle (NSV) attitude dynamics. First, Takagi–Sugeno (T–S) fuzzy models are used to describe the NSV attitude dynamics; then, an actuator-fault model is developed. Next, an adaptive fault-tolerant tracking-control scheme is proposed based on the online estimation of actuator faults, in which a compensation control term is introduced in order to reduce the effect of actuator faults. Compared with some existing results of fault-tolerant control (FTC) in nonlinear systems, the technique presented in this paper is not dependent on fault detection and isolation (FDI) mechanism and is easy to implement in aerospace-engineering applications. Finally, simulation results are given to illustrate the effectiveness and potential of the proposed FTC scheme.

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.

This paper addresses the problem of robust fault estimation and fault tolerant control (FTC) for Takagi–Sugeno (T–S) fuzzy systems. A fuzzy-augmented fault estimation observer (AFEO) design is proposed to achieve fault estimation of T–S models with actuator faults. Furthermore, based on the information of online fault estimation, an observer-based dynamic output feedback-fault tolerant controller (DOFFTC) is designed to compensate for the effect of faults by stabilizing the closed-loop system. Sufficient conditions for the existence of both AFEO and DOFFTC are given in terms of linear matrix inequalities. Simulation results of an inverted pendulum system are presented to illustrate the effectiveness of the proposed method.

This paper investigates the path-tracking problem for four-wheel-steering and four-wheel-driving electric vehicles with input constraints, actuator faults, and external resistance. A hybrid fault-tolerant control approach, which combines the linear-quadratic control method and the control Lyapunov function technique, is proposed. It not only maintains the vehicle's tracking performance in spite of faults, input constraints, and external resistance but also reduces the cost of the fault-tolerant process. A prototype vehicle from the Laboratoire d'Automatique, Ge¿nie Informatique et Signal (LAGIS), is particularly focused on illustrating the applicability of our approach.