Security and reliability are crucial for distributed coordination of heterogeneous multi-agent systems (HMASs). Therefore, this article focuses on the prescribed-time cluster consensus control problem for a class of HMASs composed of unmanned surface vehicles and autonomous aerial vehicles in the presence of actuator faults and false data injection (FDI) attacks. Firstly, to meet the prescribed-time requirement, a practical prescribed-time stability criterion related to time base generator is established for the cluster consensus design and analysis of HMASs. Secondly, based on the hierarchical control framework, a prescribed-time cluster state estimator is designed for HMASs with time-varying communication topology to achieve reliable estimation of the output trajectory of the cluster leader under FDI attacks in the cyber layer, and a prescribed-time synchronization tracking controller is designed for each agent with the help of neural networks to maintain the desired formation configuration under actuator faults in the physical layer. Finally, the simulation results verify the effectiveness of the proposed scheme.

This paper proposes an adaptive resilient fault-tolerant cooperative control (RFTCC) scheme for multiple fixed-wing unmanned aerial vehicles (UAVs) subject to cyberattacks and actuator faults. A control-oriented dynamic modeling framework is established to characterize fixed-wing UAV formation tracking under cyberattack and actuator fault threats. A fixed-time composite cyberattack observer is designed to simultaneously estimate and compensate for persistent spoofing attacks and intermittent DoS attacks on position measurements, ensuring rapid convergence under attacked conditions. To enhance the resilience of the multiple fixed-wing UAV system, an adaptive RFTCC scheme is investigated, integrating adaptive laws to dynamically adjust control gains against actuator faults and attack-induced uncertainties. Stability analysis proves the boundedness of tracking errors under the proposed control framework. Numerical simulations involving four UAVs demonstrate the effectiveness of the proposed control scheme in maintaining formation tracking despite simultaneous cyberattacks and actuator faults. The simulation results highlight the control effectiveness in attack mitigation, fault tolerance, and trajectory recovery.

Unmanned Aerial Vehicles (UAVs) equipped with onboard processors enable edge intelligence for smart-grid Internet of Things (IoT) inspection, yet insulator defect detection from aerial imagery remains challenging due to cluttered backgrounds, small targets, and strict latency constraints. Compared with handcrafted methods that rely on manual feature design and parameter tuning, this paper presents an end-to-end improved Faster R-CNN detector whose feature extractor is redesigned via convolutional feature reuse and a two-layer multi-scale fusion scheme with a deeper extraction path, preserving fine-grained cues while limiting model complexity. Experiments on the Chinese Power Line Insulator Dataset (CPLID) and a newly constructed Glass Insulator Dataset (GID) achieve [email protected] values of 97.75% and 96.79%, respectively, which is 7.1% higher than a standard Faster R-CNN (VGG16) baseline on CPLID while reducing the model size to 58 MB. On a UAV system (DJI M300 + H20T + Jetson Xavier NX), TensorRT acceleration runs at 8.5 FPS, outperforming Faster R-CNN (3.2 FPS) and YOLOv11 (5.3 FPS) under the same edge setting. Outdoor flight tests on 330 kV transmission lines further validate field readiness, supporting low-latency and bandwidth-efficient UAV inspection for smart-grid IoT maintenance.

This article addresses the pursuit-evasion problem among differential drive robots in an obstacle environment with perception uncertainty. To calculate probabilistic collision-free trajectories during the pursuit process, this article introduces the chance-constraint pursuit Voronoi cell (CCPVC), which consists of separation hyperplanes between robots and separation hyperplanes between robots and obstacles. The optimization problems are formulated to compute the separation hyperplanes, and the solution methods are provided. By incorporating two buffer terms, CCPVC exhibits favorable probabilistic collision avoidance properties. Furthermore, a nearest point finding algorithm specifically designed for pursuit scenarios, along with a distributed pursuit control policy tailored for differential drive robots are proposed based on CCPVC. Rigorous proofs for the probabilistic collision avoidance guarantees of CCPVC and the control law during the pursuit process are provided, respectively. Finally, the effectiveness of the proposed methods is validated through simulations and experiments.

With the increasing complexity of industrial systems and process data, deep learning has achieved superior performance in soft sensing but remains constrained by limited interpretability. Most existing interpretability techniques are correlation based, capturing statistical dependencies but providing little insight into underlying mechanisms. Causal modeling, by contrast, offers stronger interpretability by revealing directional and temporal influences, thereby improving both reliability and understanding. Although some recent methods consider time delays, their treatment of lags remains coarse and limited, and cannot adequately capture heterogeneous cross variable delay patterns in industrial time series. To address these limitations, we propose spatio-temporal causal learning with delay annotation (STCLD), which introduces a spatio-temporal delay attention (STDA) module to explicitly learn delay annotated spatio temporal causal graphs for soft sensing. STDA minimizes a maximum mean discrepancy objective to discover causal relations with edge specific delays, while attention path strength and multidimensional dynamic complexity are used to infer causal directions in a model-based way. The learned causal graph and delay information then guide a delay aware prediction module to build a self-interpretable soft sensor. Experiments on two real-world industrial datasets show that STCLD consistently outperforms strong baselines in both predictive accuracy and causal interpretability, providing a robust and general framework for interpretable soft sensor modeling in complex process industries.

A novel depth-homography model for Infrared (IR) and Visible (RGB) images registration and YOLOv8n-CoordAttn detection model for wildfire detection are presented. In low-light and smoke-occluded conditions, fire detection using IR images performs better than RGB images, while RGB images may still complement IR information as heat radiation around the fire makes the fire boundary blurry in the thermal imagery. Hence, fire detection based on image fusion between IR and RGB images is a more reliable approach. For image alignment between these two modalities, camera calibration is widely used, while in this work, an innovative depth-homography model as a simpler and yet precise alternative is presented, which estimates the homography matrix for an arbitrary depth with which the image alignment is conducted. Moreover, YOLOv8n-CoordAttn is presented, where YOLOv8n is augmented with Coordinate Attention modules. This detection model predicts bounding boxes of fire spots based on multispectral IR-RGB images, aiming to improve accuracy while still conducting inference in real-time. Also, outdoor flight tests using a DJI M300 UAV equipped with an H20T camera system in daytime and nighttime are carried out to gather IR-RGB datasets for training and evaluating the depth-homography and YOLOv8n-CoordAttn detection models, whose video demonstration is available at https://www.youtube.com/watch?v=Hq6X-FcUVss.

Wildfires pose escalating threats to ecosystems, infrastructure, and human safety. This paper presents an integrated autonomous UAV-based wildfire suppression system designed to execute multiple fire-spot extinguishing missions efficiently. The proposed framework assumes prior detection of wildfire locations and comprises two main modules: a trajectory planning algorithm using dynamic programming and a Linear Quadratic Regulator (LQR) controller for optimal trajectory tracking. The dynamic programming algorithm minimizes the flight distance of the multiple fire-spots firefighting trajectory. The LQR controller is developed based on a linearized model of the quadrotor UAV and ensures accurate trajectory tracking. The effectiveness of the proposed system is demonstrated through MATLAB simulations and validated via outdoor experiments using the DJI M300 UAV platform equipped with a custom-designed water-dropping mechanism. Results confirm the feasibility of the system in suppressing multiple wildfire spots autonomously.

Wildfires threaten ecosystems and communities and require rapid detection and effective suppression. This paper presents an autonomous leader-follower UAV system for real-time wildfire detection and suppression. A leader UAV employs deep learning and thermal imaging to detect fires accurately, even under low-visibility conditions, achieving 95.2% detection accuracy. It integrates visual and thermal data to refine fire coordinates, which are relayed to a ground station. The ground station directs a follower UAV, equipped with a dual-tank water payload, to execute targeted water drops with 0.2 m precision along optimized suppression paths. By separating detection and suppression roles, the system enhances mission endurance and payload capacity. This coordinated approach enables rapid and accurate wildfire containment, offering a scalable solution for early-stage fire management.

In this paper, a novel early forest fire detection and localization system is proposed based on an unmanned aerial vehicle (UAV) equipped with RGB and thermal cameras. The forest fire detection is achieved by the proposed bimodal detection network. Fire localization, i.e., fire distance estimation, is realized by the proposed monocular depth estimation network and scale recovery strategy. To validate the effectiveness of the proposed method, the performance of the detection network is tested using the bimodal dataset UAVFire presented in this paper and the public dataset FLAME2. The proposed depth estimation network is validated on the public dataset WildUAV. The experimental results show that the proposed bimodal detection network has a AP of 97.9% and 98.2% on the UAVFire and FLAME2 datasets, respectively, with a model parameter of 6.5 MB. Compared with other unimodal detection methods as well as bimodal detection methods, the detection method in this paper performs well in terms of both the detection accuracy and the model lightweighting. The model parameter of the proposed monocular depth estimation network is only 0.69 MB, and the comparison results with other methods on theWildUAV dataset prove that the depth estimation method in this paper achieves a good balance between accuracy and model lightweighting. Finally, the detection and distance estimation of early small fires are validated on the dataset UAVFire, demonstrating the effectiveness of the proposed method.

This paper presents a lightweight method for detecting forest fires using dual-modal remote sensing images captured by an unmanned aerial vehicle (UAV). The aim is to achieve efficient fire monitoring on a computationally resource-constrained UAV platform. The proposed detection network is based on the improved YOLOv8, which uses RGB image and thermal image as network input at the same time. A lightweight dual-modal feature fusion module named DFM is designed to effectively combine RGB and thermal features. The existing C2f module in YOLOv8 was replaced by the lightweight module C2f-F, along with the addition of the parameter-free attention module SimAM. This improvement improves the detection performance of the model while minimizing the model parameters. The evaluation experimental results on the FLAME 2 dataset show that the accuracy of the proposed dual-modal forest fire detection method reaches 98.4%, and the model size is only 2.9MB, which achieves a good balance between accuracy and number of parameters compared with other mainstream methods. Additionally, on the iCrest 2-s edge computing device, the detection speed reaches 20.67FPS, further confirming that this lightweight approach satisfies the real-time detection requirements for forest fires.

This paper proposes a trajectory prediction-based target localization and circumnavigation pattern for cluttered three-dimensional environments, which is more realistic and suitable for more complex environments than traditional patterns. The main work of the paper consists of two parts: tracking based on trajectory prediction and circumnavigation based on broadcast information. On the one hand, the tracking Autonomous Aerial vehicle (AAV) obtains target trajectory prediction based on the B-spline curve, and then achieves target localization and tracking through front-end search and back-end optimization. On the other hand, without communicating with each other, a distributed control strategy is presented so that the multiple circumnavigation AAVs can achieve target circumnavigation and reciprocal avoidance by only observing the status of adjacent AAVs. In the simulation, obstacle avoidance vehicles moving freely at different speeds are selected as targets in two scenarios and the simulation results are given to verify the effectiveness of the proposed approach. Furthermore, a hardware-in-the-loop experiment and a overall system validation experiment are designed to verify the feasibility of the algorithm.

This article develops a safety-driven distributed cooperative framework (SDDCF) for multiple unmanned aerial vehicles (UAVs) subject to actuator faults in the application of emergency search-and-rescue mission. A capability-triggered decision mechanism is proposed to conquer the challenging situation that the system redundancy cannot satisfy the requirement of fault-tolerant control. By quantitatively analyzing the capability of UAV, a safety threshold is provided, which can be updated adaptively in the light of performance requirement and real-time system capability estimated by a fixed-time fault observer. When the safety threshold is violated, the active performance degradation of the faulty UAVs and communication topology reconfiguration of the multiple UAVs are performed. By virtue of the SDDCF with capability-triggered mechanism, the safety of multiple UAVs system suffering from severe actuator faults is ensured for mission completion. The efficacy of the presented framework is demonstrated by a proof-of-concept emergency search-and-rescue mission in real-world flight experiments. Note to Practitioners—The proposed SDDCF is devoted to reduce the safety risk of multiple UAVs with severe actuator faults in emergency missions, where the mobility and reliability must be balanced carefully. Compared with the existing fault-tolerant control schemes, the SDDCF can ensure the safety even if the actuator faults exceed the system redundancy in a specific mission. Moreover, the practicability of the SDDCF, which can be extended to diverse task scenarios, has been verified in real-world flight experiments. In the future, the abilities of cooperative perception and risk avoidance should be improved to further enhance the safety of multiple UAVs in uncertain environments.

This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling. It then presents the augmented estimator-based decentralized fault estimation (FE) and leader-following consensus-based distributed fault-tolerant tracking control (FTTC) protocols. These are incorporated into an integrated structure that ensures the robust asymptotic convergence of estimation errors and excellent tracking performance of multi-USVs. Finally, the study derives criteria for an exponential tracking of composite faulty multi-USVs under cyber-attacks using dual-constraint restriction (attack frequency and excitation rate). Comparative simulations substantiate the advantage of the developed integrated FE and FTTC scheme.

In this article, a resilient consensus control method is proposed for nonlinear multiple unmanned aerial vehicles (UAVs) with input saturation and Denial of Service (DoS) attacks. First, an input saturation constraint based on the UAV dynamic model is investigated in this article, and an adaptive compensating term is developed to handle the input saturation. The DoS attacks considered in this article can interrupt all the communication transmissions of the attacked UAV from neighbors so that the victim is not able to receive any information from neighboring UAVs during DoS attacks. To deal with such a difficult problem, a fixed-time security constraint estimator (FTSCE) is proposed to ensure the stability and security of UAVs during the DoS attacks. Moreover, the unknown state is estimated to reduce the amount of the transferred information. Based on the proposed FTSCE, the relative position and velocity of UAV states are used to design the resilient consensus controller against the DoS attacks. By using the proposed controller, the system stability can be guaranteed according to the Lyapunov stability analysis. Finally, the numerical simulation is conducted to verify the effectiveness of the proposed resilient consensus control method.

It is generally accepted that the impact of weather variation is gradually increasing in modern distribution networks with the integration of high-proportion photovoltaic (PV) power generation and weather-sensitive loads. This article analyzes power flow using a novel stochastic weather generator (SWG) based on statistical machine learning (SML). The proposed SML model, which incorporates generative adversarial networks (GANs), probability theory, and information theory, enables the generation and evaluation of simulated hourly weather data throughout the year. The GAN model captures various weather variation characteristics, including weather uncertainties, diurnal variations, and seasonal patterns. Compared to shallow learning models, the proposed deep learning model exhibits significant advantages in stochastic weather simulation. The simulated data generated by the proposed model closely resemble real data in terms of time-series regularity, integrity, and stochasticity. The SWG is applied to model PV power generation and weather-sensitive loads. Then, we actively conduct a power flow analysis (PFA) on a real distribution network in Guangdong, China, using simulated data for an entire year. The results provide evidence that the GAN-based SWG surpasses the shallow machine learning approach in terms of accuracy. The proposed model ensures accurate analysis of weather-related power flow and provides valuable insights for the analysis, planning, and design of distribution networks.

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.

This paper presents a novel, precise, and fast framework for autonomous aerial forest fire fighting using unmanned aerial vehicles (UAVs) to extinguish a line of fires by most efficiently utilizing the on-board camera. Autonomous aerial firefighting algorithms using UAVs have been proven to be promising in early wildfire suppression. However, UAVs/drones have limited payloads, which do not allow them to carry as much retardant as fixed-wing aircraft do. Hence, firefighting drones should release the retardant in a way that accurately extinguishes wildfire and efficiently suppresses the line of fires. In this work, a DJI M300 RTK drone mounted on which a RGB camera and a 3D-printed water tanker are mounted and utilized to extinguish a line of fires in an outdoor experimental environment. A line of fires has been set up using firepits, whose GPS locations are known. The optimal path along which a drone should approach and release the retardant to extinguish the fire line is calculated using the RANSAC algorithm, as well as the in-motion dropping mission starting point. Nonetheless, due to errors in the drone's onboard GPS sensor, the drone does not exactly position itself at the starting point. In fact, in the proposed method, the drone uses on-board camera images to adjust itself and get aligned with the retardant-releasing line as it is supposed based on prediction, followed by approaching the fire line and releasing the retardant. The testing results show efficient and accurate suppression of fire spots whose video verification is provided at https://www.youtube.com/watch?v=ZP2KoxtwAsg.

In this paper, rethinking the application of deep neural network models in aerial image-based wildfire perception, a gradient-weighted class activation mapping (Grad-CAM) scheme is studied and tested on the model of you only look once (YOLO) for the drone-image-based wildfire detection. Grad-CAM helps the model to be more explainable and understandable. It also helps engineers and fire-fighters to view the production of target layer(s) of the wildfire detection model. Such a benefit is similar as higher-weighted segmentation models. Therefore, Grad-CAM could be a tool to help designing less-weighted wildfire detection models. It could be also used to support fire-fighters labeling the suspect wildfire area and make decision-making process more efficiently.

This article presents a bio-inspired safety control scheme for unmanned aerial vehicles (UAVs) in confined environments with disturbance. Although there has been some existing research on the effect of disturbance for a single UAV, multi-UAV formation under external wind disturbances remains challenging, especially in a tight and confined environment. Inspired by nature, this study concentrates on an anti-disturbance mechanism for safe multi-UAV formation in a tight environment. The presented safety control system combines disturbance observer-based control (DOBC), bionic formation switching (BFS) strategy, and safety evaluation. Two safety issues are considered in this article. For a single UAV, the estimated disturbance is compensated in the inner-loop controller. While for multi-UAV formation, the BFS strategy attenuates the effect of external wind disturbance leveraging the formation configuration. The so-called group perturbation immune factor (GPIF) is designed to analyze and evaluate the safety of the overall formation. The experimental results validate the comprehensiveness and anti-disturbance capability of the system.

The next-generation power grid evolves from the development of fundamental cyber-physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid's real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

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.

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.

Optimization-based methods are commonly applied in autonomous driving trajectory planners, which transform the continuous-time trajectory planning problem into a finite nonlinear program with constraints imposed at finite collocation points. However, potential violations between adjacent collocation points can occur. To address this issue thoroughly, we propose a safety-guaranteed collision-avoidance model to mitigate collision risks within optimization-based trajectory planners. This model introduces an “embodied footprint”, an enlarged representation of the vehicle’s nominal footprint. If the embodied footprints do not collide with obstacles at finite collocation points, then the ego vehicle’s nominal footprint is guaranteed to be collision-free at any of the infinite moments between adjacent collocation points. According to our theoretical analysis, we define the geometric size of an embodied footprint as a simple function of vehicle velocity and curvature. Particularly, we propose a trajectory optimizer with the embodied footprints that can theoretically set an appropriate number of collocation points prior to the optimization process. We conduct this research to enhance the foundation of optimization-based planners in robotics. Comparative simulations and field tests validate the completeness, solution speed, and solution quality of our proposal.

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 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.

Achieving accurate control performance of the end-effector is critical for practical applications of aerial manipulator. However, due to the presence of floating-base disturbance from the unmanned aerial vehicle (UAV) platform and the kinematic error amplification effect from multilink structure of the manipulator, it is extremely challenging to ensure the high-precision performance of aerial manipulator. Building upon the philosophy of disturbance rejection, we propose a predictive optimization scheme that allows aerial manipulator to successfully execute millimeter-level flying pick and peg-in-hole task. First, the error amplification effect of the floating base is quantitatively analyzed by virtue of the aerial manipulator kinematics. Intuitively, it is found that if the further motion of the UAV platform is well predicted, the manipulator can directly counteract the floating disturbance by following a modified reference trajectory. Hence, a learning-based prediction approach is leveraged to rapidly forecast the UAV platform motion online. Subsequently, an optimization controller is formulated to follow the reference trajectory by incorporating multiple practical constraints of aerial manipulator. Flight tests demonstrate that this study goes a step further to achieve higher accuracy of the end-effector than the existing results (centimeter-level).

In order to realize the operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this article investigates an adaptive anti-disturbance performance guaranteed fuzzy formation control problem for quadrotor UAVs by using aperiodic signal updating. The unknown dynamics are approximated by using fuzzy logic systems. A disturbance observer is constructed for each UAV, including position subsystem (outer-loop) and attitude subsystem (inner-loop), to reduce the negative effects of UAVs with disturbances in complex flight environments. To avoid the potential internal collision among the multiple UAVs, a prescribed performance function that widens the initial value range of the consistency error is designed to keep the formation error within the specified range. Intermittent output signals generated by event-triggered control strategy of attitude subsystem are used to reduce sensors data transmission on each UAV, thereby saving energy and communication resources. Via the Lyapunov stability theory, the formation error can converge to a prescribed boundary range. Finally, the validity of the proposed control strategy is illustrated by simulation results.

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.

This study proposes a novel control scheme for simultaneously tracking and stabilizing nonholonomic wheeled mobile robots (NWMRs) subject to actuator and sensor faults, measurement errors, uncertain dynamics, and time-varying slippage/skid disturbances. To this end, a nonlinear model based on a type-3 (T3) fuzzy logic system (FLS) is developed for NWMR tracking and stabilization. Furthermore, a nonlinear model predictive controller (NMPC) is designed analytically without employing iterative computations, thus achieving fast performance. A new approach of type-3 nonsingleton fuzzification is introduced to handle measurement errors. Additionally, faults in the actuators and sensors are detected by a supervisory scheme and eliminated by a devised compensator. Finally, extensive simulations and experimental validations are conducted to further verify the effectiveness of the proposed scheme, along with a comparative analysis of several benchmarking methods.

At present, distributed renewable energy has made smart grids a hot spot of research in the Cyber-Physical System (CPS) field. This paper mainly discusses the problem of the control system of insulated-gate bipolar transistors (IGBTs) in the distributed three-phase inverter. The second-order sliding mode observer based on a super-twisting algorithm (STA) is used to observe the control system. The three-phase current of the smart grid is observed by the device, to improve the efficiency of the control system in the smart grids under different conditions.

Over the past few years, the use of Cyber-Physical Systems (CPS) has become more and more prevalent in various industries. With this increase in usage, there has been a surge of interest in exploring frequent network attacks and creating reliable techniques for detecting false data injection attacks (FDIA). Considering that the fake data injection attack can keep the residual unchanged before and after the attack, bypassing the traditional insufficient data monitoring technology. Based on the existing power grid model, the attack characteristics of FDIA are analyzed, and an improved algorithm is proposed based on the adaptive Kalman filter algorithm. The algorithm's effectiveness is tested by combining the existing Euclidean and Bayonet similarity detection methods.

Under false data-injection (FDI) attacks, the data of some agents are tampered with by the FDI attackers, which causes that the distributed algorithm cannot estimate the ideal unknown parameter. Due to the concealment of the malicious data tampered with by the FDI attacks, many detection algorithms against FDI attacks often have poor detection results or low detection efficiencies. To solve these problems, a conveniently distributed diffusion least-mean-square (DLMS) algorithm with cross-verification (CV) is proposed against FDI attacks. The proposed DLMS with CV (DLMS-CV) algorithm is comprised of two subsystems: one subsystem provides a detection test of agents based on the CV mechanism, while the other provides a secure distribution estimation. In the CV mechanism, a smoothness strategy is introduced, which can improve the detection performance. The convergence performance of the proposed algorithm is analyzed, and then the design method of the adaptive threshold is also formulated. In particular, the probabilities of missing alarm and false alarm are examined, and they decay exponentially to zero under sufficiently small step size. Finally, simulation experiments are provided to illustrate the effectiveness and simplicity of the proposed DLMS-CV algorithm in comparison to other algorithms against FDI attacks.

This article investigates the distributed active fault-tolerant cooperative control problem for leader-follower multiagent systems (MASs) in the presence of multiple faults, communication delays, and external disturbances. A new distributed consensus protocol is put forward to ensure the state consensus of MASs, which can be served as a nominal controller in fault-free cases with communication delays and external disturbances. A novel distributed time-delay intermediate observer, which can estimate system states and multiple faults simultaneously, is derived based on the time-delay closed-loop system equation. By integrating a fault compensation mechanism into the nominal controller, a distributed active fault-tolerant consensus controller is constructed for the follower agents to eliminate the adverse effects of multiple faults. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

This article considers the safety control problem of a quadrotor unmanned aerial vehicle (UAV) subject to actuator faults and external disturbances, based on the quantization of system capability and safety margin. First, a trajectory function is constructed online with backpropagation of system dynamics. Therefore, a degraded trajectory is gracefully regenerated, via the tradeoff between the remaining system capability and the expected derivatives (velocity, jerk, and snap) of the trajectory. Second, a control-oriented model is established into a form of strict feedback, integrating actuator malfunctions and disturbances. Therefore, a retrofit dynamic surface control (DSC) scheme based on the control-oriented model is developed to improve the tracking performance. When comparing to the existing control methods, the compensation ability is analyzed to determine whether the faults and disturbances can be handled or not. Finally, simulation and experimental studies are conducted to highlight the efficiency of the proposed safety control scheme.

This article investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler–Lagrange systems with actuator faults and external disturbances. First, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems are used to approximate unknown parameters of the system. What is more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

With the rise of wind energy production in global power generation, wind farm facilities are becoming increasingly attractive targets for malicious attacks, in particular those affecting wind farm network operators’ cybersubsystems and functionalities. Given the significance of this problem, this article proposes a novel anomaly-based intrusion detection and diagnosis system to carry out in-line monitoring as with firewalls. Also, an innovative cyberattack-resilient active power control is designed to responsively mitigate the impacts of cyberattacks on the safe regulation of active power from wind farms. An offshore wind farm benchmark is used to implement and demonstrate the effectiveness of the proposed solutions in the presence of wind turbulences, measurement noises and realistic smart cyberattack scenarios.

For deep learning-based soft sensors, the lack of interpretability and the consequent unreliability has become one of the most important problems. In this article, a neural network scheme called the deep multiple attention soft sensor (DMASS), which consists solely of attention mechanisms, is proposed to develop a self-interpretable soft sensor. DMASS was established to ensure the self-interpretability of data selection and sensor modeling and try to integrate these originally independent phases into the single scheme. First, the existing attention mechanisms’ core implementation steps are summarized as a unified form, and then the variable attention mechanism and time lag attention mechanism are proposed. When DMASS's training is completed, the obtained attention weights provide the self-interpretable data selection results. Then, a self-attention activation structure (SAAS) is proposed to extract the nonlinear spatio-temporal features of data. The mathematical expression for the extracted feature, the SAAS's attention matrix, the information path diagram for DMASS's training, and the uncertainty-aware interval prediction show the self-interpretability of sensor modeling. Finally, DMASS was applied to predict the thermal deformation of the air preheater rotor, and the validity of DMASS's self-interpretability is verified by the known mechanism analysis and information bottleneck theory. Meanwhile, DMASS's great sensing performance was confirmed through comparison with other novel soft sensors.

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action–critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

This paper is focused on the cooperative trajectory planning problem for multiple car-like robots in a cluttered and unstructured environment narrowed by static obstacles. The concerned multi-vehicle trajectory planning (MVTP) problem is challenging because i) the scenario is nonconvex and tiny; ii) the vehicle kinematics is nonconvex; and iii) a feasible homotopy class is unavailable a priori. We propose a two-stage MVTP method: Stage 1 identifies a feasible homotopy class, and Stage 2 quickly finds a local optimum based on the identified homotopy class. Numerical optimal control, adaptive scaling, grouping, and trust region construction strategies are integrated into the proposed planner. Our planner is extensively compared in 100 benchmark cases with the state-of-the-art MVTP methods such as incremental sequential convex programming, numerical optimal control, conflict-based search, priority-based trajectory optimizer, and optimal reciprocal collision avoidance. The simulation results demonstrate our method's superiority in runtime and optimality. Experiments with three car-like robots demonstrate the efficiency of our proposed planner. Source codes are in https://github.com/libai1943/MVTP_benchmark.

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.

The bionic flapping-wing robotic aircraft is inspired by the flight of birds or insects. This article focuses on the flexible wings of the aircraft, which has great advantages, such as being lightweight, having high flexibility, and offering low energy consumption. However, flexible wings might generate the unexpected deformation and vibration during the flying process. The vibration will degrade the flight performance, even shorten the lifespan of the aircraft. Therefore, designing an effective control method for suppressing vibrations of the flexible wings is significant in practice. The main purpose of this article is to develop an adaptive fault-tolerant control scheme for the flexible wings of the aircraft. Dynamic modeling, control design, and stability verification for the aircraft system are conducted. First, the dynamic model of the flexible flapping-wing aircraft is established by an improved rigid finite element (IRFE) method. Second, a novel adaptive fault-tolerant controller based on the fuzzy neural network (FNN) and nonsingular fast terminal sliding-mode (NFTSM) control scheme are proposed for tracking control and vibration suppression of the flexible wings, while successfully addressing the issues of system uncertainties and actuator failures. Third, the stability of the closed-loop system is analyzed through Lyapunov's direct method. Finally, co-simulations through MapleSim and MATLAB/Simulink are carried out to verify the performance of the proposed controller.

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.

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.

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.

Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on the trajectory curvature, thus easily making the resultant trajectories beyond the ego vehicle’s kinematic capability. Regarding planning in the Cartesian frame, selection-based methods suffer from the curse of dimensionality. By contrast, optimization-based methods in the Cartesian frame are more flexible to find optima in the continuous solution space, but the new challenges are how to tackle the intractable collision-avoidance constraints and nonconvex kinematic constraints. An iterative computation framework is proposed to accumulatively handle the complex constraints. Concretely, an intermediate problem is solved in each iteration, which contains linear and tractably scaled collision-avoidance constraints and softened kinematic constraints. Compared with the existing optimization-based planners, our proposal is less sensitive to the initial guess especially when it is not kinematically feasible. The efficiency of the proposed planner is validated by both simulations and real-world experiments. Source codes of this work are available athttps://github.com/libai1943/CartesianPlanner.

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 article details the development and evaluation of a practical solution for path-constrained proximity maneuvers of spacecraft. Whereas no velocity measurement is utilized within the feedback structure, the controller rigorously enforces actuator magnitude constraints. Specifically, the control algorithm is constructed from a potential function method repelling the spacecraft from possible collisions. The proposed controller can guarantee potential functions to be navigated to the origin and thus overcome the stubborn local minima problem. Moreover, the control capability under any given control limit can be estimated and adjusted by changing the feedback gains. The specific performance with guaranteed safety can be also explicitly calculated by designers. The results are obtained through a Lyapunov-based stability analysis to prove uniformly ultimate boundedness. Numerical simulation results illustrate the performance and features of the developed control method.

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.

Autonomous intersection management (AIM) refers to planning the trajectories for multiple connected and automated vehicles (CAVs) when they traverse an unsignalized intersection cooperatively. As an extension of the conventional AIM, lane-free AIM allows the CAVs to adjust their velocities and paths flexibly within the intersection. Nominally, one needs to formulate a centralized optimal control problem (OCP) to describe the concerned lane-free AIM scheme, but solving such an intractably scaled problem is challenging. This work proposes a batch-processing framework, which divides the traffic flow into batches. The cooperative trajectories within one batch are planned by numerically solving a small-scale OCP; all the batches are managed via a reservation-based method following the first-come-first-serve policy. The proposed batch-processing framework aims to run as fast as a reservation-based method at the macro level while taking care of the cooperative driving quality at the micro level. The proposed method is validated via simulation and preliminary experiments.

This paper focuses on physical faults and cyberattacks analysis and intelligent detection of faults/attacks with integrated fault-tolerant and cyber-resilient controllers for a PV system at microgrid level. The possibility of detecting and diagnosing faults/attacks rapidly enables the controllers to accommodate/mitigate the effects of faults/attacks in the microgrid system. This allows the microgrid to continue operation without any serious problems or interruptions. In this regard, the present paper considers a hybrid AC/DC microgrid composed of different renewable distributed generation resources. To monitor real-time data from the PV system at microgrid level, a hybrid intelligent diagnosis system with two parallel diagnosis units based on rule-based and model-based approaches, is presented. Lastly, the online information obtained from the diagnosis system is used by the controllers to guarantee safe operation of the microgrid during faults/attacks. The high effectiveness of the proposed strategy under different faults and cyber-attacks is demonstrated in an advanced microgrid benchmark model with wide variations in operating conditions and electrical loads.

A solution for a tractor-trailer vehicle's generic maneuver planning task can be introduced by an optimal control problem (OCP). However, the curse of dimensionality is excited along with the OCP solution due to the collision-avoidance constraints in a large scale. The collision-avoidance conditions are weakened by simply constructing a corridor along a homotopically guiding route such that the vehicle's maneuvers are safely separated from obstacles. This approach is motivated by the safe flight corridor (SFC) applied for path planning of unmanned aerial vehicle (UAV). But SFC cannot be applied directly to the ground vehicle cases because a tractor-trailer vehicle cannot be modeled as a mass point in a narrow environment. An extension of the SFC is proposed, which requires different bodies of a multi-body vehicle to stay in different safe travel corridors. In this way, a reduced-scale OCP is formulated, and the problem scale becomes irrelevant to the environmental complexity. Simulation results illustrate that near-optimal maneuvers can be derived within less CPU time.

Abstract Underwater image processing has always been a very challenging problem. Under the influence of environmental factors, underwater images are prone to some problems, such as colour cast, low visibility, and few edge details. Here, an image enhancement algorithm is proposed to improve image degradation mainly caused by the absorption of light. First, colour compensation and white balance algorithm are used to restore the natural appearance of the image. Then the improved dark channel prior (DCP) is used to improve the visibility and avoid blocking artifacts which appear in traditional DCP. Unsharp masking (USM) is applied to enhance the texture features of the DCP image. Finally, wavelet fusion is used to fuse the DCP image and DCP+USM image. The fusion algorithm not only further improves the visibility and texture features, but also reduces the noise of DCP+USM. Compared with other methods, quantitative analysis results show that the enhanced images have higher visibility, more details and edge information.

This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm for use over a wireless sensor network (WSN). This algorithm is based on information freshness, which is calculated as the age of information (AoI) of the sampled data. The proposed algorithm integrates the traditional event-triggered mechanism, information freshness calculation method, and Kalman consensus filter (KCF) algorithm to estimate the concentrations of pollutants in the aircraft more efficiently. The proposed method also considers the influence of data packet loss and the aircraft’s loss of communication path over the WSN, and presents an AoI-freshness-based threshold selection method for the ET-KCF algorithm, which compares the packet AoI to the minimum average AoI of the system. This method can obviously reduce the energy consumption because the transmission of expired information is reduced. Finally, the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory. Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.

A fault-tolerant formation control (FTFC) strategy is proposed against severe actuator faults, applied to a team of wheeled mobile robots (WMRs). In the beginning, a team of WMRs is operating in a prescribed formation topology. As long as the robot(s) cannot complete the required mission due to severe actuator faults, the formation is reconfigured for the healthy WMRs to eliminate the fault effects. The new reconfiguration is determined by means of an optimal assignment scheme so that each healthy robot can be assigned to a unique position. Subsequently, each robot starts planning its trajectory to reach its new position in the new formation configuration by virtue of a hybrid genetic algorithm and particle swarm optimization (GA–PSO). As metaheuristic optimization techniques, such as GA and PSO, are unable to solve the optimization problem with continuous control inputs, control parameterization and time discretization (CPTD) method is, therefore, adopted to offer an approximate piecewise linearization of the control inputs. Thus, an approach with the integration of CPTD and GA–PSO is developed. This integrated approach enables that the time of achieving the configuration is minimized, while the physical constraints of WMRs and collision avoidance are explicitly considered. Finally, real-time experiments are conducted to validate the effectiveness of the proposed algorithm compared with other optimization techniques, such as GA and PSO.Note to Practitioners—Cooperative unmanned systems have drawn significant interests in military and civilian applications. During missions’ execution, it is of great importance for cooperative unmanned systems to have fault-tolerance capabilities for achieving the desired mission when faults occur in one or more team members. A challenging problem is how to detect and isolate the fault and how to mitigate the fault effects on the whole mission. This article presents a fault-tolerant formation control strategy in the case of severe actuator fault occurrence in a team of wheeled mobile robots.

This article presents a fixed-time accommodation strategy of actuator faults for hypersonic gliding vehicles (HGVs). The approach against actuator faults is incorporated by sliding mode control (SMC), bilimit homogeneity, and adaptive techniques, with consideration of practical constraints and model uncertainties. The resulting fault-tolerant attitude control law allows the fault compensation to be completed in a fixed time, in view of the limited time available for recovery of a faulty HGV. The effectiveness of the presented scheme is validated by comparing it to the finite-time fault accommodation scheme.Note to Practitioners—Hypersonic vehicles have drawn significant interests due to the features of super-fast and flexible maneuverability. The use of advanced fault-tolerant control (FTC) techniques is expected to guarantee safety during hypersonic vehicle operation. A challenging problem is how to counteract actuator faults promptly and effectively in the presence of practical constraints and model uncertainties. This article presents a fixed-time FTC algorithm for a hypersonic gliding vehicle.

In a dynamic environment, moving to the destination safely and effectively is of paramount importance for an unmanned ground vehicle (UGV). This article presents a strategy of trajectory planning and tracking that aims to ensure the UGV’s safety in an uncertain environment. Specifically, based on the initial environment information, a global optimal trajectory connecting the start and the destination is predefined by an artificial fish swarm algorithm (AFSA). In the presence of unforeseen obstacles, a trial-based forward search (TFS) algorithm based on the Markov chain is proposed in the local trajectory planning module, while collision prediction is integrated as heuristic information. The vehicle’s current state is updated accordingly for the sake of avoiding entire state spaces involved in the computation. Therefore, the storage efficiency and convergence rate in local path planning are sufficiently enhanced in comparison to dynamic programming. Moreover, command signals can be calculated with the proposed multiconstrained model predictive controller (MMPC), ensuring the vehicle to track the reference trajectory and smoothen the motion. Finally, the results in both simulations and experiments reveal the effectiveness of the proposed algorithm in the presence of both static and dynamic obstacles.Note to Practitioners—This article is motivated by the unmanned ground vehicle (UGV) collision avoidance problem in practical missions, such as farming and emergency response. In recent years, various trajectory planning and tracking algorithms have been widely developed. However, the environmental complexity and the intruders’ unexpected movement pose difficulties in trajectory planning, especially in ensuring the computation time under the allowable threshold. Moreover, the UGV practical trajectory tracking is a challenging task which demands a desired response within various physical constraints. In this article, a two-stage conflict resolution system is proposed. First, a trial-based forward search (TFS) is used to generate a new trajectory deviating the UGV from the initially generated trajectory by the artificial fish swarm algorithm (AFSA), aiming to avoid the unforeseen intruders (unknown in prior) in real-time. Using these two trajectory planning algorithms alternatively, both global trajectory optimality in a cluttered environment and appropriate maneuvers with respect to unexpected intruders can be achieved. Subsequently, the UGV is modeled according to its kinematic characteristics, and thus a multiconstrained model predictive controller (MMPC) is designed to follow the reference trajectory. The physical constraints are respected by integrating them into the controller. Simulations and experimental results demonstrate that the proposed strategy can guide and control a UGV from the start to the destination safely and smoothly, even in the case of multiple obstacles with constant or varying velocities. Furthermore, the proposed collision avoidance strategy can be extended to other unmanned systems, including unmanned aerial vehicles and unmanned surface vehicles.

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.

The current maritime applications have yielded strong demands for the development of advanced unmanned surface vehicles (USVs) with more reliable path following capabilities to greatly extend mission durations and enhance accommodative capabilities of USVs to more hazardous and dynamic environments. This paper presents an adaptive path following control method using a retrofit adaptive tracking control technique with application to a USV with consideration of environmental disturbances (like winds, waves, and currents), while taking into account of the system constraints of USVs, including both turning features (turning rate limit and turning dynamics) and rudder operation constraints (rudder deflection and rate saturation, and its dynamics). In order to guarantee the satisfactory performance of the USV operating in a calm environment, a baseline state feedback tracking controller considering the characteristics of yaw rate and rudder operations, and USV steering and actuator dynamics is first designed. In the presence of time-varying environmental disturbances, a retrofit adaptive disturbance compensating control mechanism is then developed based on the disturbance amplitude estimated from an indirect adaptive disturbance estimator. Finally, a reconfigurable adaptive path following controller is synthesized by combining the baseline controller and the adaptive disturbance compensating mechanism for the proper operation of the USV in the presence of environmental disturbances, while the desired path is successfully followed by the USV within an acceptable deviation boundary and without violating constraints of turning rates as well as amplitude and rate of rudder deflections. To evaluate the effectiveness of the proposed path following control methodology, both numerical simulations on a nonlinear USV model and field experiments on a real-size USV are conducted.

This paper develops a constrained Model Predictive Control (MPC) formulation for longitudinal control of a fixed wing aircraft. In order to account for the inherent coupling between the inputs and outputs, which is a characteristic of multivariable systems, multiple prediction horizons are used, i.e. one for each output. Furthermore, a novel Quadratic Programming (QP) problem is derived to solve this MPC problem, via the Primal-Dual procedure. Numerical simulations using two QP algorithms demonstrate successful tracking performance of the MPC based controller.

This paper investigates the distributed finite-time fault-tolerant containment control problem for multiple unmanned aerial vehicles (multi-UAVs) in the presence of actuator faults and input saturation. The distributed finite-time sliding-mode observer (SMO) is first developed to estimate the reference for each follower UAV. Then, based on the estimated knowledge, the distributed finite-time fault-tolerant controller is recursively designed to guide all follower UAVs into the convex hull spanned by the trajectories of leader UAVs with the help of a new set of error variables. Moreover, the unknown nonlinearities inherent in the multi-UAVs system, computational burden, and input saturation are simultaneously handled by utilizing neural network (NN), minimum parameter learning of NN (MPLNN), first-order sliding-mode differentiator (FOSMD) techniques, and a group of auxiliary systems. Furthermore, the graph theory and Lyapunov stability analysis methods are adopted to guarantee that all follower UAVs can converge to the convex hull spanned by the leader UAVs even in the event of actuator faults. Finally, extensive comparative simulations have been conducted to demonstrate the effectiveness of the proposed control scheme.

In this paper, a quasi-optimal higher order sliding mode control (HOSMC) scheme is designed using disturbance observer (DOB) and adaptive techniques. The overall HOSMC scheme is constituted by three portions: 1) a switch-based quasi-optimal control law that ensures rapid finite-time stabilization of the origin, producing a nominally uncertainty-free system, based on a perturbed chain of integrators; 2) a DOB which estimates the lumped uncertainties with lower frequencies; and 3) an adaptive controller designed to deal with the fast-varying terms of these lumped uncertainties over the pass-band of the DOB. Since the adaptive controller and the DOB work in a cooperative manner, the only requirement is that the switching gain be greater than the bound of the DOB estimation error, instead of the bound of the lumped uncertainties. As a consequence, overestimation in the adaptive controller can be avoided by resorting to the equivalent control. Simulation results confirm the capabilities of the proposed control strategy.

Trajectory planning for a tractor-trailer vehicle is challenging because the vehicle kinematics consists of underactuated and nonholonomic constraints that are highly coupled. Prevalent sampling-based or search-based planners suitable for rigid-body vehicles are not capable of handling the tractor-trailer vehicle cases. This work aims to deal with generic n-trailer cases in the tiny environments. To this end, an optimal control problem is formulated, which is beneficial in being accurate, straightforward, and unified. An adaptively homotopic warm-starting approach is proposed to facilitate the numerical solution process of the formulated optimal control problem. Compared with the existing sequential warm starting strategies, our proposal can adaptively define the subproblems with the purpose of making the gaps between adjacent subproblems “pleasant” for the solver. Unification and efficiency of the proposed adaptively homotopic warm-starting approach have been investigated in several extremely tiny scenarios. Our planner finds solutions that other existing planners cannot. Online planning opportunities are briefly discussed as well.

In this paper, a decentralized fault-tolerant cooperative control scheme is developed for multiple unmanned aerial vehicles (UAVs) in the presence of actuator faults and a directed communication network. To counteract in-flight actuator faults and enhance formation flight safety, neural networks (NNs) are used to approximate unknown nonlinear terms due to the inherent nonlinearities in UAV models and the actuator loss of control effectiveness faults. To further compensate for NN approximation errors and actuator bias faults, the disturbance observer (DO) technique is incorporated into the control scheme to increase the composite approximation capability. Moreover, the prediction errors, which represent the approximation qualities of the states induced by NNs and DOs to the measured states, are integrated into the developed fault-tolerant cooperative control scheme. Furthermore, prescribed performance functions are imposed on the attitude synchronization tracking errors, to guarantee the prescribed synchronization tracking performance. One of the key features of the proposed strategy is that unknown terms due to the inherent nonlinearities in UAVs and actuator faults are compensated for by the composite approximators constructed by NNs, DOs, and prediction errors. Another key feature is that the attitude synchronization tracking errors are strictly constrained within the prescribed bounds. Finally, simulation results are provided and have demonstrated the effectiveness of the proposed control scheme.

Making rational decisions for sequential decision problems in complex environments has been challenging researchers in various fields for decades. Such problems consist of state transition dynamics, stochastic uncertainties, long-term utilities, and other factors that assemble high barriers including the curse of dimensionality. Recently, the state-of-the-art algorithms in reinforcement learning studies have been developed, providing a strong potential to efficiently break the barriers and make it possible to deal with complex and practical decision problems with decent performance. We propose a formulation of a velocity varying one-on-one quadrotor robot game problem in the three-dimensional space and an approximate dynamic programming approach using a projected policy iteration method for learning the utilities of game states and improving motion policies. In addition, a simulation-based iterative scheme is employed to overcome the curse of dimensionality. Simulation results demonstrate that the proposed decision strategy can generate effective and efficient motion policies that can contend with the opponent quadrotor and gather advantaged status during the game. Flight experiments, which are conducted in the Networked Autonomous Vehicles (NAV) Lab at the Concordia University, have further validated the performance of the proposed decision strategy in the real-time environment.

This paper presents a reliable active fault-tolerant tracking control system (AFTTCS) for actuator faults in a quadrotor unmanned aerial vehicle (QUAV). The proposed AFTTCS is designed based on a well-known model reference adaptive control (MRAC) framework that guarantees the global asymptotic stability of a QUAV system. To mitigate the negative impacts of model uncertainties and enhance system robustness, a radial basis function neural network is incorporated into the MRAC scheme for adaptively identifying the model uncertainties online and modifying the reference model. Meanwhile, actuator dynamics are considered to avoid undesirable performance degradation. Furthermore, a fault detection and diagnosis estimator is constructed to diagnose lossof- control-effectiveness faults in actuators. Based on the fault information, a fault compensation term is added to the control law to compensate for the adverse effects of actuator faults. Simulation results show that the proposed AFTTCS enables the QUAV to track the desired reference commands in the absence/presence of actuator faults with satisfactory performance.

As you open this January issue of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), I hope everyone enjoyed a great holiday season and is excited for the new year of 2019. I am very delighted and honored to report several key metrics of IEEE TNNLS to the community.

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.

In this paper, we propose a cooperative motion planning method for a group of connected and automated vehicles (CAVs) crossing a lane-free intersection without using explicit traffic signaling. This multi-vehicle motion planning task is formulated as a centralized optimal control problem. However, the solution to this optimal control problem is numerically intractable due to the high dimensionality of the collision-avoidance constraints and the nonlinearity of the vehicle kinematics. A two-stage strategy is proposed for generating online solutions: at Stage 1, the CAVs are requested to reach a standard formation before entering the intersection; at Stage 2, the vehicles cross the intersection. As the motion planning sub-problem at Stage 2 begins with a standard configuration, the optimal solution to this standard sub-problem can be computed offline in advance and applied online directly. On the other hand, the formation reconfiguration sub-problem at Stage 1 is easy to solve online. Through dividing the entire dynamic process into two periods, the difficulties in the original optimal control problem are significantly reduced so that the real-time performance is achieved.

This paper focuses on the scheme of cooperative lane change motion planning of multiple connected and automated vehicles, so as to minimize the time for lane change while penalizing large steering angles subject to hard collision avoidance constraints. Nominally this scheme should be formulated in a centralized way with the constraints of all the vehicles considered simultaneously. In order to facilitate the numerical solving process of this centralized optimization problem, we propose a stepwise computation framework. Starting with a sub-problem with all of the collision avoidance constraints removed, a sequence of sub-problems are defined by adding back the removed collision avoidance constraints gradually until the original problem takes shape in the end. The optimum of one sub-problem is always used as the initial guess when solving the next sub-problem. This iterative process continues until the optimum of the original problem is obtained. In this way, the difficulties in the original centralized problem are divided into multiple parts, and every progress made to address the partial difficulties is “solidified” by the initial guess.

This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults. First, the upper bounds of the norms of the unknown functions are introduced, which contain actuator faults and model uncertainties. Subsequently, self-constructing fuzzy neural networks (SCFNNs) with adaptive laws are capable of obtaining the bounds. The bound estimation can reduce the computational burden with a lower amount of rules and weights, rather than the dynamic matrix approximation. Moreover, with the aid of SCFNNs, a multivariable sliding mode control (SMC) is developed to guarantee the finite-time stability of the handicapped aircraft. As compared to the existing intelligent FTAC techniques, the proposed method has twofold merits: fault accommodation can be promptly accomplished and decoupled difficulties can be overcome. Finally, simulation results from the nonlinear longitudinal Boeing 747 aircraft model illustrate the capability of the presented FTAC scheme.

This work formulates the multi-vehicle lane change motion planning task as a centralized optimal control problem, which is beneficial in being generic and complete. However, a direct solution to this optimal control problem is numerically intractable due to the dimensionality of the collision-avoidance constraints and nonlinearity of the vehicle kinematics. A progressively constrained dynamic optimization (PCDO) method is proposed to facilitate the numerical solving process of this complicated problem. PCDO guarantees to efficiently obtain an optimum to the original optimal control problem via solving a sequence of simplified problems which gradually judge and reserve only the active collision-avoidance constraints. A first-regularization-then-action strategy, together with the look-up table technique, is developed for online solutions. At the regularization stage, the vehicles form a standard formation by linear acceleration/deceleration only. At the action stage, the vehicles execute lane change motions computed offline and recorded in the look-up table. This makes online motion planning feasible because 1) the computational complexity at the regularization stage scales linearly rather than exponentially with the vehicle number; and 2) online computation at the action stage is fully avoided through data extraction from the look-up table.

The paper analyzes the common-source digital upsets process characterized by using multi-dimensional hidden Markov model (MDHMM) which is used to simulate digital upsets. The digital upsets are generated from the artificial environments operating on the distributed redundant processing controllers. The main contribution of this paper is to characterize the correlated digital upsets relationship generated by the common-source environment by MDHMM. Furthermore, the paper applies them to performance degradation of a control system analysis. In this paper, different methods are used to model the correlated digital upset processes under the same condition. The MDHMM model can better characterize the correlated digital upset processes than hidden Markov model (HMM) through comparing the distributions of the correlated digital upset processes. Finally, the performance degradation simulations of a certain control system are conducted when subject to common-source digital upsets, and the corresponding results are analyzed in details. These analyses also demonstrate the importance of the optimal redundancy design for control systems, which can provide a good analysis result on the best balance among performance, reliability, and costs in practice.

This paper presents a methodology on the design of an optimal predictive control scheme applied to an islanded microgrid. The controller manages the batteries energy and performs a centralized load shedding strategy to balance the load and generation within the microgrid, and to keep the stability of the voltage magnitude. A nonlinear model predictive control (NMPC) algorithm is used for processing a data set composed of the batteries state of charge, the distributed energy resources (DERs) active power generation, and the forecasted load. The NMPC identifies upcoming active power unbalances and initiates automated load shedding over noncritical loads. The control strategy is tested in a medium voltage distribution system with DERs. This control strategy is assisted by a distribution monitoring system, which performs real-time monitoring of the active power generated by the DERs and the current load demand at each node of the microgrid. Significant performance improvement is achieved with the use of this control strategy over tested cases without its use. The balance between the power generated by the DERs and the load demand is maintained, while the voltage magnitude is kept within the maximum variation margin of ±5% recommended by the standard ANSI C84.1-1989.

A fast learning algorithm for training multilayer feedforward neural networks (FNN's) by using a fading memory extended Kalman filter (FMEKF) is presented first, along with a technique using a self-adjusting time-varying forgetting factor. Then a U-D factorization-based FMEKF is proposed to further improve the learning rate and accuracy of the FNN. In comparison with the backpropagation (BP) and existing EKF-based learning algorithms, the proposed U-D factorization-based FMEKF algorithm provides much more accurate learning results, using fewer hidden nodes. It has improved convergence rate and numerical stability (robustness). In addition, it is less sensitive to start-up parameters (e.g., initial weights and covariance matrix) and the randomness in the observed data. It also has good generalization ability and needs less training time to achieve a specified learning accuracy. Simulation results in modeling and identification of nonlinear dynamic systems are given to show the effectiveness and efficiency of the proposed algorithm.