As a classical unknown input filtering (UIF) method, the Kitanidis filter is capable of providing the minimum-variance-unbiased state estimation for stochastic systems with unknown inputs. However, it inevitably suffers from the performance degradation in non-Gaussian noise situations due to the limitation of variance criterion. In this letter, a novel UIF algorithm is developed for a class of nonlinear systems subject to non-Gaussian noises and unknown inputs. By constructing a correntropy-metric-based joint optimization problem and introducing a dual-iteration mechanism, the robust estimation of system state and unknown input can be obtained simultaneously, thereby providing enhanced filtering accuracy in non-Gaussian scenarios. Finally, a numerical simulation example is presented to illustrate the effectiveness and superiority of the proposed algorithm.

The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural ordinary differential equations (neural ODEs), leading to a prominent generalization improvement. The feedback neural network is a novel two-DOF neural network, which possesses robust performance in unseen scenarios with no loss of accuracy performance on previous tasks. A linear feedback form is presented to correct the learned latent dynamics firstly, with a convergence guarantee. Then, domain randomization is utilized to learn a nonlinear neural feedback form. Finally, extensive tests including trajectory prediction of a real irregular object and model predictive control of a quadrotor with various uncertainties, are implemented, indicating significant improvements over state-of-the-art model-based and learning-based methods.

This paper is concerned with the study of robust distributed average tracking (DAT) algorithms for networked control systems in the presence of external disturbances or false data injection attacks (FDIAs). To eliminate the impacts of external disturbances and FDIAs, the technologies of disturbance-observer-based control (DOBC) and active disturbance rejection control (ADRC) are introduced into the context of DAT problems. First, for a class of external disturbances with known dynamics, we propose an anti-disturbance DAT (AD-DAT) algorithm, where a stand-alone disturbance observer based on the idea of DOBC is employed to estimate the disturbance and then to compensate it in the design of control inputs. The proposed AD-DAT algorithm can track the average of multiple time-varying reference signals with zero steady-state error and the accurate tracking is robust with respect to initialization constraints. Furthermore, for another class of FDIAs with unknown dynamics, we design an anti-attack DAT (AA-DAT) algorithm where the control input is based on the estimates of states instead of original states, and construct an extended state observer including a state observer and an FDIAs observer based on the idea of ADRC. The extended state observer plays a key role in estimating and eliminating the impact of FDIAs without compromising the accurate tracking performance. In addition, sufficient conditions are derived for the proposed two algorithms from a theoretical point of view to guarantee accurate average tracking. Finally, some numerical examples are given to illustrate the validity and effectiveness of the proposed algorithms.Note to Practitioners—This paper is motivated by the problem of robust distributed average tracking (DAT) for the time-varying centroid of the formation of a group of autonomous vehicles. The problem arises in the scenario where two groups of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) perform a combined surveillance-reconnaissance mission (where the UAVs aim to provide aerial coverage and early warning against threats for the UGVs, as shown in Fig. 1) in an uncertain environment where the external disturbances might exist or the FDIAs might be launched by adversaries. Obviously, the tracking accuracy of the target signal will be compromised in the presence of external disturbances or FDIAs. However, most existing works for disturbance rejection mainly focused on the static average consensus rather than the dynamic one even though a few works mentioned the DAT problem with only considering the case of external disturbances. Based on this, we propose an AD-DAT algorithm and an AA-DAT algorithm for the DAT problem based on the ideas of DOBC and ADRC, respectively. Numerical examples show that the proposed algorithms are able to estimate and eliminate the impacts of the external disturbances and the FDIAs, which implies that the algorithms can be implemented in practical scenarios. In future research, we will extend the results to more general scenarios in the presence of external disturbances and FDIAs.

Due to its small size and light weight, the tiny unmanned aerial vehicle (UAV) is especially suitable for tasks that involve confined indoor space exploration. However, autonomous localization of the tiny UAV has become a great challenge in the global navigation satellite system (GNSS)-denied, unstructured, and resource-constrained environments. In this paper, an onboard ultra-wideband (UWB)-based relative localization scheme of the tiny UAV is presented, where an anchor-UAV equipped with the UWB anchors is employed to provide position reference for the tiny UAV. As the key in the proposed scheme, the state estimation algorithm should be able to overcome the joint effect of the inertial sensor bias, the UWB measurement noises, and the limited computational resources. To this end, a composite disturbance filtering (CDF) method is proposed which consists of the disturbance observer for real-time bias compensation, an improved particle filter to deal with the non-Gaussian noises, and a particle size adaptation procedure to release the computation burden. The proposed CDF method represents a refined treatment of the multi-source heterogeneous uncertainties in the relative localization system and strikes a balance between localization accuracy and computational efficiency. The effectiveness of the proposed localization method is validated via both simulation tests and flight experiments.Note to Practitioners—The tiny UAVs are capable of dexterous flight in confined indoor spaces, which makes them especially suitable for the indoor operation tasks such as environment exploration, disaster relief, gas seeking, and meteorological measurement. In these tasks, accurate localization of the tiny UAV is of vital importance. To achieve autonomous localization of the tiny UAV in the GNSS-denied, infrastructure-free, and unstructured indoor environment, an onboard inertial measurement unit (IMU)/UWB fusion-based relative localization scheme is presented. In the presented scheme, a large UAV equipped with UWB anchors is employed to provide position reference for the tiny UAV. To address the multi-source heterogeneous disturbances (the dynamic IMU biases and the non-Gaussian UWB noises) in the localization model and the limited computational resource onboard the tiny UAV, a CDF method with an adaptive sample size is proposed. As demonstrated by the simulation and experiments, the positioning accuracy and computation efficiency of the tiny UAV have been effectively enhanced with the proposed scheme.

Due to the presence of strong inner dynamic coupling and changes in center of mass (CoM) during tasks execution, the precise tracking control problem of aerial manipulator systems becomes challenging. When the mounted manipulator is performing a task, the movements of the manipulator will cause the inherent dynamic coupling force/torque disturbances. Such disturbances are quickly acted on the position loop and the orientation loop of the UAV body, to the detriment of the control accuracy. On the other hand, the fluctuation of the UAV body leads to the base floating, resulting in a significantly adverse influence on the precision of the manipulator end-effector. Since different disturbances have distinct mathematical properties, i.e., norm bounded and rate bounded, currently, there is no control framework that is able to tackle the dynamic coupling, model uncertainty and base floating simultaneously. In this paper, a refined anti-disturbance control architecture is proposed for the decentralized aerial manipulator model, where various disturbances with different mathematical properties are well explored and tackled according to their positions and effects acting on the system. The stability of the proposed control framework is ensured by using Lyapunov-like analysis. Experimental results are presented to illustrate the performance of the proposed control framework.Note to Practitioners—One of the key challenges that hinder the aerial manipulator potential applications is its stability and accuracy due to the existence of various disturbances. Most of disturbance rejection control methods in the literature for aerial manipulator always deal with the various disturbances as the lumped one. Their distinct mathematical properties and different impacts on the system are not well explored, which may result in the performance degradation and limit its practical implementation. In this article, a refine anti-disturbance architecture is proposed, which is able to handle various disturbances in a more systematical way according to their mathematical properties and effects acting on the system. Physical experiments suggest that finely tackling the different disturbances enjoys a better performance in aerial manipulator trajectory tracking control problem and thus can promote the aerial manipulator to be deployed in the tasks demanding on the accuracy. In addition, such control strategy can also be extended to other robotic systems suffering from various disturbances.

One of inescapable challenges in facilitating the application of aerial manipulators is to achieve the high precision control performance of the end-effector. The manipulator motions beneath the UAV platform constantly contend with composite disturbances, such as floating base, strong inner coupling effects, and model uncertainties. These factors collectively contribute to an inadequate control performance. In this paper, a composite control scheme is presented to tackle this issue. Specifically, a joint velocity planner is proposed to handle the base-floating disturbance in kinematic loop. By virtue of the generated joint reference signal, the base-floating disturbance can be effectively alleviated. The tracking error of the end-effector can be ensured within a small set. Moreover, in a complementary manner, neural network (NN) approximation and nonlinear disturbance observer (NDO) compensation are combined to track the joint references. The NN is adopted to estimate composite dynamic model including inner coupling effects and model uncertainties, while the NDO is designed to handle the remaining uncompensated part. The stability of the closed-loop system including the manipulator kinematics and dynamics is guaranteed using the Lyapunov-like method. Experimental results are reported to manifest the effectiveness of the proposed composite control scheme.Note to Practitioners—This work is driven by the precise end-effector control problem of an aerial manipulator subject to base-floating, strong dynamic coupling, and model uncertainties. Most of existing approaches implicitly address this issue by improving the flight performance of the aerial platform. However, the composite disturbances acted on the manipulator, which would deteriorate the operation accuracy of the end-effector, are not systematically addressed. In this work, a composite control scheme is constructed, which consists of the manipulator joint velocity planner and the dynamic controller. The idea is intuitive. The joint velocity is generated to counteract the fluctuation of the aerial platform. Furthermore, the dynamic controller is developed to accurately track the planned joint velocity in the presence of strong coupling and model uncertainties. The proposed scheme guarantees the stability of the close loop system, making our approach especially promising solution for aerial manipulation under composite disturbances.

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 article addresses issues of covert attacks detection and resilient control of quadcopters. In the presence of generalized covert attacks characterized by strong stealth, passive detection techniques prove to be insufficient. Active detection methods are common solutions to this problem, but the balance between the detection capability and the stability of quadcopters still remains as a challenge. Different from the existing active attack detection methods, a coding and channel switching approach and its matched resilient controller for quadcopters are proposed. First, the design and detectability analysis of the coding-based attack detection method are given. Once covert attacks revealed, a secure channel is activated to replace the attacked channel. Second, to enhance quadcopter resilience against covert attacks, a resilient control framework is proposed. It incorporates attack effect estimation and compensation via a fixed-time observer. This framework maintains the stability of the quadcopter's control system under covert attacks. Finally, the numerical simulation and real-world experiments are conducted to evaluate the effectiveness and feasibility of the proposed scheme.

In this article, the deception attack detection problem is studied in scenarios involving multisecurity level transmission channels. Powerful attackers can construct stealthy deception attacks by exploiting data from reliable and unreliable channels. From the perspective of data correlation, we develop three detection schemes with different resource consumption. First, a fully security channel is utilized to establish innovation-based time-varying data correlation, which triggers residual covariance variation under attacks. Second, a noise-encryption mechanism is introduced without requiring the fully security channel. For the initial two methods, we propose a targeted optimization method to improve the detection performance by exploiting the quantified residual covariance variation. Third, we propose a time-shift coding method from the perspective of dynamic system stability, which is rigorously proved to be sensitive to attack behavior. For these proposed methods, we quantify the residual covariance variation induced by attacks and achieve detection by the$\chi ^{2}$test and generalized likelihood ratio test. Finally, the efficiency and reliability of these detection schemes are validated by examples.

The avoidance of dynamic obstacles is a challenging issue for uncrewed aerial vehicle (UAV) due to its limited perception range.This article presents a framework including cooperative warning and risk-averse safety control for multiple UAVs. When external obstacles are discovered, the evaluation of conditional value-at-risk for the obstacles is performed by the discoverers. Whenever the risk value violates the designed safety threshold, the warning information will be transmitted to the threatened neighbors immediately. Moreover, by incorporating the event-triggered mechanism, the risk-averse safety control scheme is constructed. Consequently, multiple UAVs are able to evade dynamic obstacles from various directions with enhanced safety and reduced conservatism. The effectiveness and superiority of the proposed scheme are substantiated by comparative simulations and real-world flight experiments.

The polarization-based attitude and heading reference system (PAHRS) consisting of inertial navigation system (INS) and polarization sensor (PS) offers an effective solution for attitude and heading determination in the case of global navigation satellite system (GNSS) signal degradation. Its performance depends largely on the state estimation accuracy. In the existing work, the Kalman filtering (KF), as a low complexity scheme, is employed to achieve the state estimation of PAHRS. However, in practice, the performance of PAHRS could be affected by weather conditions and the maneuvering state of the vehicle. The accurate noise statistics of INS and PS is often encountered, which leads to a degradation of PAHRS. To improve the adaptability and accuracy of the system, in this article, we conduct a KF flow-based deep neural networks (KFDNNs), a real-time state estimator that learns from PS and INS data to carry out Kalman filter. In the constructed KFDNNs, deep neural networks (DNNs) are inserted into the flow of the KF to learn the optimal Kalman gain from PAHRS data, which we can retain data efficiency and interpretability of the classic algorithm while circumvents the dependency of the KF on knowledge of the noise statistics. Moreover, a two-stage training strategy consisting of warm-up training stage and task-oriented training stage is presented for the KFDNNs, which mitigate the gradient explosion caused by the unstable random initialization of KFDNNs while improve the flexibility of sequence length selection. Finally, the simulation and vehicle test are carried to verify the performance of PAHRS. The experimental results confirm the KFDNNs outperforms KF-based INS/PS method especially in complex weather and maneuvering scenarios.

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods.Note to Practitioners—Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

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.

Accurate and reliable polarization information is extremely important as heading cues in underwater low-light environments, especially in the presence of hybrid refraction/scattering dynamic effects. In this article, a bio-inspired antagonistic differential polarization algorithm (ADPA) is proposed for polarization perception, enhancing the resolution and contrast of polarization signals in underwater low-light environments. Starting from the analysis of disturbances in underwater polarization measurements, the wavelet denoising algorithm is introduced to extract the effective light intensity. Subsequently, the antagonistic differential measurement equation is derived by mimicking biological antagonistic differential mechanisms, improving the polarization contrast from orthogonal photoreceptors. The proposed ADPA exhibits the capability to effectively handle consistent interference while dampening time-varying nonlinear disturbances. On this basis, a bio-inspired navigation strategy using ADPA is presented for heading determination. The mapping relationship between underwater polarization information and spatial motion information is revealed. Underwater navigation experiments in real oceans are carried out to manifest the effectiveness of the investigated ADPA-based method. In comparison with existing polarization methods, the proposed method can significantly improve the reliability, adaptability, and robustness of underwater polarization-based navigation.

In nature, when encountering unexpected uncertainty, animals tend to react quickly to ensure safety as the top priority, and gradually adapt to it based on recent valuable experience. We present a framework, namely EVOLutionarymodel-baseduncertainty obserVER (EVOLVER), to mimic the bio-behavior for robotics to achieve rapid transient reaction ability and high-precision steady-state performance simultaneously. In particular, the Koopman operator is leveraged to explore the latent structure of internal and external disturbances, which is subsequently utilized in anevolutionarymodel-based disturbance observer to estimate the eventual disturbance. The resulting observer can guarantee a provable convergence in optimal conditions. Several practical considerations, including construction of a training dataset, data noise handling, and lifting functions selection, are elaborated in pursuit of the theoretical optimality in real applications. The lightweight feature of our framework enables online computation, even on a microprocessor (STM32F7 with 100 Hz control frequency). The framework is thoroughly evaluated by one simulation and three experiments. The experimental scenarios include: 1) Trajectory prediction of an irregular free-flying object subject to aerodynamic drag, 2) indoor and outdoor agile flights of a quadrotor subject to wind gust, and 3) high-precision end-effector control of a manipulator subject to base moving disturbance. Comparison results show that the performance of our proposed EVOLVER is superior to several state-of-the-art model-based and learning-based schemes.

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).

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.

Malware, exposed on the ground control station, can tamper with the unmanned aerial vehicles (UAVs) interaction information. It makes UAVs vulnerable to the desired trajectory attacks, which deteriorates the flight safety if no timely action is taken. Therefore, this article focus on fast reactive mechanism for desired trajectory attacks on UAVs. A fixed-time detection scheme is proposed based on fixed-time unknown inputs observer and trajectory tracking errors. Subsequently, a fixed-time sliding mode attack observer is developed to compensate the effect caused by attacks. Meanwhile, the estimation errors of attacks can be stabilized within fixed time. Finally, experiment tests are presented to demonstrate the effectiveness of the proposed methods.

Singularly perturbed systems (SPSs) wildly exist in practical engineering practice; however, the disturbance rejection in SPSs is still a problem. As well-known disturbance rejection methods, composite hierarchical anti-disturbance control (CHADC) can effectively compensate and suppress disturbance, which improves the control performances of complex systems. To enhance the capability of anti-disturbance and control precision, this article proposes a novel composite hierarchical anti-disturbance (CHAD) dynamic surface control (DSC) for the fast subsystem considering highly dynamic disturbance, and CHAD proportion-integration (PI) control for the slow subsystem with slow-varying harmonic disturbance. The disturbances are supposed to be generated by an exogenous system based on partially known information with model uncertainty, and the stabilities of both fast and slow closed-loop subsystems are analyzed. Meanwhile, the compensability of disturbances is discussed. Finally, simulations for an F-8 aircraft are given to demonstrate the effectiveness of the proposed methods.

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.

Precise attitude control of space manipulators plays an important role in advanced on-orbit assembly tasks. The vibration of flexible appendage and inertial uncertainties encountered in the operating process, however, may cause attitude error or even safety threats to the space manipulator system. In this article, a high-precision attitude control scheme of a space manipulator system is designed via a combination of disturbance observer (DO), prescribed performance-based H∞control, and iterative learning control (ILC) techniques. The proposed control scheme consists of three portions: 1) a DO that estimates the vibration disturbance caused by flexible appendage of base satellite; 2) a robust H∞controller with prescribed performance to attenuate the inertial uncertainties resulting from capture of an unknown object; and 3) an ILC for improving the transient and steady-state process in the presence of a repetitive on-orbit assembly task. This novel control scheme can not only handle the flexible vibration and inertial uncertainty of the space manipulator but also achieve satisfactory tracking performance. Both simulation and experimental results confirm the superiority of the proposed control strategy.

In this paper, a hybrid anti-disturbance composite control scheme is presented to address the problem subject to systems remarkably affected by external disturbances and discrete dynamics of actuators. By explicitly considering the hybrid feature resulting from discrete actuation signals acted on a continuous system, the hybrid disturbance observer is designed to estimate the effect of external disturbance. Meanwhile, H∞technique is used to mitigate the effect of continuous command executing error caused by discontinuous operation, which cannot be modeled in priori. Within the proposed composite control scheme, the combination of the disturbance estimation information and the H∞technique enables the entire hybrid system precisely performing. The stability of the whole continuous-discrete control system is analyzed. Finally, based on the Mars lander dynamics, comparative simulation studies illustrate that the developed control scheme can preserve a satisfactory level of performance, even in the presence of external disturbances and actuator quantization errors.

In this article, a novel spacecraft composite attitude stabilization scheme based on dual disturbances observers (DDOs) and high-precision nonsingular terminal sliding mode control is presented, with explicit consideration of reaction wheel dynamics and multiple disturbances. First, spacecraft attitude coupling dynamics model is constructed covering reaction wheel dynamics and multiple disturbances. These disturbances include matched disturbances caused by motor counter electromotive force and equivalent mismatched disturbance caused by reaction wheel friction and environment disturbances. DDOs are designed to estimate both equivalent matched and mismatched disturbances, respectively. Subsequently, the high-precision control scheme is designed to compensate the estimated disturbances and attenuate the influence of estimated errors via composite nonsingular terminal sliding mode attitude controller. The closed-loop stability and convergence are proved based on the Lyapunov stability theory. Finally, hardware-in-the-loop experiments are conducted to verify the effectiveness of proposed spacecraft attitude stabilization 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.

In this paper, suffering from both complex uncertainties and underactuations, accurate trajectory tracking control problem of an asymmetric underactuated surface vehicle (AUSV) is first addressed by guiding yaw dynamics which are free of persistent excitation (PE). Using nested coordinate transformations, the AUSV is formulated in a cascade structure consisting of translation and rotation subsystems with complex uncertainties. Finite-time uncertainty observers (FUOs) are devised to exactly estimate transformed uncertainties, and enable separation principle in controller and observer syntheses. By virtue of creating yaw-guided dynamics, rotation tracking is shaped to stabilize yaw and sway tracking discrepancies, simultaneously, in collaboration with yaw controller. Nominal dynamics of translation tracking errors are globally asymptotically stabilized by surge-control synthesis using cascade analysis and Lyapunov approach, and thereby contributing to global asymptotic stability of the entire translation-rotation tracking system. Eventually, an FUO-based yaw-guided tracking control (FUO-YTC) scheme of an AUSV with complex uncertainties is established. Simulation studies demonstrate remarkable performance.

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.

Failure to counteract in-flight actuator stuck failures within a limited amount of time can result in catastrophic consequences. Aircraft safety is also undermined when fault-tolerant control (FTC) systems are designed based on inaccurate diagnostic results. This paper presents an FTC scheme for transport aircraft subject to actuator stuck failures, which is integrated with fuzzy logic system (FLS), real finite-time sliding mode control (RFTSMC), and least squares control allocation (LSCA) techniques. First, the term, including the stuck information, is obtained by resorting to an FLS. The norm of the weight matrix is adopted in the FLS estimation, such that the amount of the adaptive parameters is independent of the number of FLS rules. Following this, a virtual fuzzy logic aided FTC strategy is developed using RFTSMC, by which the actuator stuck failure is counteracted within finite time. Furthermore, an LSCA unit is capable of distributing the commands to the fault-free actuators, with explicit consideration of incorrectly identified values of actuator effectiveness indicators. Finally, simulation studies of a nonlinear Boeing 747 longitudinal model are performed to validate the effectiveness of the proposed scheme.

This paper presents an adaptive quasi-optimal higher order sliding-mode control (HOSMC) scheme, which is able to avoid gain overestimation. The overall scheme consists of two elements: 1) a quasi-optimal control law that provides fast finite-time stabilization for a chain of integrators; and 2) an adaptive HOSMC with integration of the quasi-optimal control and the integral sliding-mode concept. The adaptation strategy solves the problem of gain tuning without overestimation and has the advantage of chattering reduction. Moreover, the bounds of the uncertainties are no longer needed in the controller design. Simulation results are provided to demonstrate the effectiveness of the proposed HOSMC algorithm.

This paper studies the reliable linear quadratic control for a helicopter with actuator failures. The helicopter model is introduced. The actuator fault models are established as well. A reliable linear quadratic control (RLQQ design algorithm is developed based on the iterative linear matrix inequality (ILMI) approach. Simulation results are given to demonstrate the effectiveness of the designed reliable controllers

In this paper, a new sensitive microwave life-detection system which can be carried by special rescuing robots has been constructed. The system can be used for rescuing, anti-terrorist and law-enforcement purposes. The schematic diagram of microwave transmitting/receiving (T/R) system with automatic cancellation subsystem and signal processing system are presented. The design is recited in detail. Experiments have been conducted to verify the effectiveness of this system. The recorded signal frequency spectrums for heartbeat and respiration of a man behind an obstacle (wall) are presented