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This article studies the distributed formation control problem for multiple unmanned surface vehicles (USVs) considering uncertain coefficient matrixes, unmeasurable velocities, and time-varying disturbances. The main contributions are as follows: First, a global coordinate translation is proposed to partially linearize the nonlinear dynamic model equipped with the unmeasurable velocity. Second, based on the global coordinate translation, a novel type of fixed-time extended two-state observer (FTETSO) is developed to estimate unmeasurable velocities and total disturbances for each vehicle. Wherein, the estimation errors will converge to zero within a fixed time. Meanwhile, considering estimation accuracy, a two-state extension is proposed to replace a single-state extension. Third, using a sliding model-based control technique, an FTETSO-based distributed global output-feedback fixed-time formation controller (GOFFC) is elaborately developed. Based on the proposed controller, the fixed-time convergence of the closed-loop system is ensured. Finally, the validity and stability of the proposed control approach are verified by simulations. • A newly introduced decoupling technique can guarantee global stability. • A novel fixed-time extended two-state observer is proposed. • A distributed global output-feedback fixed-time formation control scheme is proposed. • The observation errors and tracking errors converge to zero in a fixed time. [ABSTRACT FROM AUTHOR]

This article researches the problem of trajectory tracking for unmanned marine vehicles (UMVs) under disturbances and denial‐of‐services (DoS) attacks in the wireless channel. An equivalent data‐driven model of UMVs with ocean disturbances is established by using partial form dynamic linearization algorithm. The disturbances and the input of UMVs have different pseudo partitioned Jacobean matrix, and the disturbances are estimated by using extended state observer, which improves the immunity of UMVs to disturbances in the environment. It is the first time that the DoS attacks are considered under the data‐driven model for UMVs, and a novel data‐driven adaptive trajectory tracking control framework is constructed. The article proposes an attack predictive compensation mechanism to mitigate their effects of DoS attacks, which follows the Bernoulli distribution. Based on it, the data‐driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data‐driven control scheme and the predictive compensation mechanism is validated through the simulations. [ABSTRACT FROM AUTHOR]

In the paper, a backstepping control strategy based on a fractional-order finite-time command filter and a fractional-order finite-time disturbance observer is proposed for the trajectory tracking control of an unmanned surface vehicle. A fractional-order finite-time command filter is presented to estimate the derivatives of the intermediate control, which cannot be directly calculated, thereby reducing the chattering generated by the integer-order command filter. The fractional-order finite-time disturbance observer is presented to approximate and compensate for the model uncertainty and unknown external disturbances in the system. Subsequently, the globally asymptotically stable nature of the closed-loop system is proved based on the Lyapunov method. The effectiveness of the method is proven by simulation experiments on unmanned surface vehicles. [ABSTRACT FROM AUTHOR]

This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack of control in the sway channel. Secondly, nonlinear behavior stemming from saturation constraints is not always differentiable. Regarding this, a smooth dead-zone-based model was conducted to fit the behavior, leaving a relatively simple actuator model. Then, an improved prescribed time–prescribed performance function (PTPPF) and error transformation method were utilized to propose a model-parameter-free control algorithm that guarantees user-defined constrained boundaries while ensuring all tracking errors converge within small domains before a preassigned settling time. The theoretical analysis was conducted by the initial value theorem, Lyapunov's second method, and proof by contradiction, followed by comparative simulation results that verified the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the trajectory tracking control of unmanned surface vehicles (USVs) subject to input quantization, actuator faults and dead zones. In scenarios with dense marine facilities, there are constraints on the tracking performance and convergence time of USVs. First, the designed control signal is quantized by a hysteresis quantizer to reduce the transmission rate. Second, to guarantee the transient and steady-state tracking performance of the USV, a prescribed performance control technology with a predefined settling time is employed. Third, a predefined-time adaptive sliding mode control (SMC) method is designed by integrating the auxiliary function and the barrier function. Moreover, the lumped uncertainties caused by quantization, actuator faults, and dead zones are simultaneously processed using control gain based on barrier function. The proposed control method guarantees that the tracking error and sliding variable converge to the corresponding predefined bounds within a predefined time. The predefined bounds are independent of the upper bound on the lumped uncertainty. The stability of the controlled system is proven via the Lyapunov theorem. Finally, the effectiveness of the designed controller is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Aiming at the situation that small unmanned surface vehicle (USV) encounters unknown disturbance during low speed sailing, a course controller with finite time stability is designed. To solve this problem, we construct an undisturbed ideal navigation model which simply meets the stability requirements, and constructs an adaptive sliding mode surface. The control under finite time approach law is also introduced. The model under perturbation can land on the sliding mode surface in finite time and then synchronize with the ideal navigation model. The adaptive control was applied in the implementation of power control for the thruster structure, so as to ensure the tracking of the desired course within the finite time, and satisfy the needs for the stable system performance. Lyapunov direct method is used to strictly prove that the designed controller can ensure the system which converges to the steady state value in a given time period. Simulation results show that the designed adaptive finite-time controller can ensure the stable course tracking of the USV with thruster structure at low speed, and meet the requirements of the course robustness of the USV under dynamic conditions. [ABSTRACT FROM AUTHOR]

The trajectory tracking method for an unmanned surface vehicle (USV) using integral terminal sliding mode integral backstepping adaptive control (ITSMIBAC) scheme is presented in this paper, which takes into account external disturbances and uncertain model parameters. Furthermore, finite-time convergence and excellent track tracking effect are guaranteed by the differential equations that is made up of the integral terminal sliding mode function. In particular, a backstepping method adding integral term is used to ensure the globally asymptotic stability of system and make the differential equations to converge to zero. The upper bound of the disturbances including external disturbances and uncertain model parameters is estimated by an adaptive control law. Finally, the effectiveness and the feasibility of the proposed controller are demonstrated by numerical example and comparison result through tracking circular orbit. [ABSTRACT FROM AUTHOR]

This article presents an adaptive method for ArduPilot-based autopilots of fixed-wing unmanned aerial vehicles (UAVs). ArduPilot is a popular open-source unmanned vehicle software suite. We explore how to augment the PID loops embedded inside ArduPilot with a model-free adaptive control method. The adaptive augmentation, adopted for both attitude and total energy control, uses input/output data without requiring an explicit model of the UAV. The augmented architecture is tested in a software-in-the-loop UAV platform in the presence of several uncertainties (unmodeled low-level dynamics, different payloads, time-varying wind, and changing mass). The performance is measured in terms of tracking errors and control efforts of the attitude and total energy control loops. Extensive experiments with the original ArduPilot, the proposed augmentation, and alternative autopilot strategies show that the augmentation can significantly improve the performance for all payloads and wind conditions: the UAV is less affected by wind and exhibits more than 70% improved tracking, with more than 7% reduced control effort. [ABSTRACT FROM AUTHOR]

The adaptive containment control problem with collision avoidance is investigated for unmanned surface vehicles (USVs). At first, the finite time disturbance observer is developed to present the estimation and compensation of external disturbance. Then, incorporating the artificial potential and radial basis function into the new containment strategy, all followers can enter the convex hull spanned by leaders while the collision is avoided. Furthermore, it is demonstrated that the error signals in the closed-loop system are boundedness by employing Lyapunov stability. Simulation studies finally manifest the effectiveness of proposed method. • The paper achieves the containment control of unmanned surface vehicles with collision avoidance. • To approximate the model uncertainties, the containment control law with neural network is developed. • The finite-time disturbance observer provides an estimation and compensation of the disturbance. [ABSTRACT FROM AUTHOR]

In this article, a composite nonlinear feedback (CNF) controller with anti-windup extended state observer (ESO) is proposed for path-following control of unmanned ground vehicles (UGVs). To describe steering dynamics accurately, a saturation model with bounded disturbances is developed subject to path-following, lateral dynamic, and steering capability. The anti-windup ESO is investigated for real-time disturbance estimation under saturated input. The CNF controller is designed for path-following. In terms of asymptotically null controllability, a practical semiglobal stabilization result is derived for the closed-loop system with saturated input and bounded disturbances. Experimental results show that the proposed strategy is effective for UGVs. [ABSTRACT FROM AUTHOR]

Surface-vehicle berthing is a complex and challenging task. It involves complicated ship dynamics owing to a large drift angle and the time-varying environmental disturbances induced by wind, wave, and current. Therefore, berthing requires state processing at each step and a controller that can respond to the non-linear behavior of the unmanned surface vehicle (USV). Herein, a systematic approach for the autonomous berthing of a USV is proposed. A state-machine approach is proposed to solve state transitions in the berthing step. A model reference adaptive controller was adopted to cope with the uncertainty of USV dynamics during berthing. Herein, the theoretical background and design of the adaptive controller are described. Simulation for the validation of the proposed method was conducted using the robot operating system Gazebo-based simulator. Finally, the respective performances of the proposed method and conventional controller were compared. Using the proposed berthing approach, an accurate and stable docking of small USVs became achievable. [ABSTRACT FROM AUTHOR]

This article presents a new adaptive method for formation control of unmanned aerial vehicles (UAVs) with limited leader information and communication. We study a formation control protocol in the framework of vector-field guidance where the leader can communicate its position and orientation but not its velocity. A practical motivation for this scenario is the so-called congestion-aware control, in which tradeoffs between the density of unmanned vehicles and communication interference caused by many communicating vehicles arise: these tradeoffs may require to reduce the communication load to avoid interference. To compensate for the lack of knowledge of the leader velocity, each UAV makes use of a local estimation mechanism. The resulting method is an adaptive control method, whose stability can be established using Lyapunov stability. We show that the method can be extended to a distributed communication setting with a few neighboring UAVs in place of the leader. Extensive simulations with different formation shapes (Y, V, and T formation) show that the proposed adaptation mechanism effectively achieves the formation despite the unknown leader velocity. The proposed mechanism has a very similar performance to the ideal case when the leader velocity is perfectly known, and outperforms all the nonadaptive cases in which the followers have an incorrect knowledge of the leader velocity. [ABSTRACT FROM AUTHOR]

Under directed communication topology, this article investigates the distributed fault-tolerant cluster synchronization problem for multiple unmanned surface vehicles. Nonlinear uncertainties, external disturbances, and actuator failures are considered. The negative effect caused by thruster faults can be attenuated by utilizing the adaptive input compensation strategy. The neural network scheme is utilized to estimate nonlinear uncertainties and external disturbances. By choosing some unmanned surface vehicles as pinning nodes, a distributed fault-tolerant pinning control algorithm is presented. The cluster synchronization for multiple unmanned surface vehicles can be guaranteed under the proposed control method. The Lyapunov technique is utilized to demonstrate the stability of the multiple USV system. The feasibility of the novel strategy is demonstrated by numerical simulation. • A novel pinning control method is proposed, which only needs to choose several fixed unmanned surface vehicles as control nodes. • An adaptive input compensation is designed to attenuate the negative effect caused by thruster faults. • A distributed fault-tolerant pinning control strategy is proposed to guarantee cluster synchronization for multiple unmanned surface vehicles. • The position errors and velocity errors of unmanned surface vehicles in different subgroups converge to a small neighborhood of zero. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of formation control and obstacle avoidance for a swarm of unmanned surface vehicles operating in complex environments with unknown disturbances and obstacles. We propose a novel model-free sliding mode predictive adaptive control scheme (MFSPAC) that combines model predictive control and backstepping structure. The proposed MFSPAC only requires input and output data to achieve effective performance. The value of this paper lies in the design of a novel model-free sliding mode predictive control law, which enables formation control without relying on the system model and requiring no prior knowledge. By combining model predictive control with backstepping, this paper not only facilitates the application of model-free control schemes to rotating robotic systems but also enables obstacle avoidance for such systems using MFSPAC schemes. Subsequently, the model-free extended state observer method is employed to observe unknown lumped disturbances and pseudo-Jacobian matrix estimation errors without requiring a system model. Application of the Gerschgorin disk theory demonstrates that the proposed method is bounded-input, bounded-output stable upon completion of obstacle-avoidance actions. Numerical simulations confirm the superiority of the designed controller. • The method and design comprehensively consider all disturbance, noise, and limitations. • A model-free control method is obtained that relies only on input and output data. • Model-free extended observer methods estimate system state without a priori knowledge. • The control method is superior to legacy methods in complex marine environments. • Control is bounded-input bounded-output stable after obstacle-avoidance activity. [ABSTRACT FROM AUTHOR]

In this paper, a novel fixed-time fault-tolerant control (FTC) scheme is proposed for unmanned marine vehicles (UMVs) with dynamics uncertainties and ocean disturbances. By introducing a high-order integral term, a fixed-time integral terminal sliding mode (ITSM) with faster convergence rate is proposed, which mitigates the effect of the initial system state on the convergence time successfully. Furthermore, based on the online estimation information from the adaptive mechanism, the effects of thruster faults, dynamics uncertainties, and ocean disturbances can be compensated by designing an adaptive fixed-time fault-tolerant controller. Compared to the existing results, the proposed FTC strategy ensures stability in a fixed-time framework. Finally, the comparison simulations of a floating production ship have demonstrated the efficacy of the proposed scheme. • A new fixed-time integral terminal sliding mode (ITSM) control strategy is designed for UMVs in this paper. Unlike our previous work in asymptotic convergence over an AISMC (Yu et al., 2022), the proposed FTC method can ensure fixed-time convergence. • A novel adaptive FTC strategy in the framework of fixed-time ITSMC is presented. Compared with existing result that neglected actuator faults (Qiao et al., 2018), the fault effect are taken into account. [ABSTRACT FROM AUTHOR]

This paper presents a finite-time path following control approach with an adaptive error constraint line-of-sight guidance for marine unmanned surface vehicles (USVs). The proposed guidance and control strategy is capable of keeping the cross-tracking error within the constraint range while the USV follows a desired curved path, and it can guarantee the tracking errors converge in finite time for an underactuated USV with unknown disturbances. Specifically, an adaptive error constraint line-of-sight guidance law is originally proposed to calculate the desired course angle and realize cross-tracking error constraint by combining the control Lyapunov function and adaptive sideslip estimator. Furthermore, a finite-time backstepping control method by using a new virtual control signal and modified tracking error compensation signals is applied to control an underactuated USV convergence to the desired course angle. Then, a finite time surge control law with a modified tracking error compensation dynamic is designed to achieve surge velocity tracking. The proposed finite-time control laws have better transient and steady-state performance, and simplify the design of the controller by not relying on interference observers. Case studies using a physics-based model are made to support the theoretical results. • for underactuated An adaptive error constraint guidance law for underactuated USVs is proposed. • The proposed guidance law can limit the cross-tracking error to the constraint range. • Finite-time control laws with tracking error compensation dynamics are designed. • The proposed control strategy is independent from disturbance observer. [ABSTRACT FROM AUTHOR]

The surface round-up and towing operations are essential for surface garbage removal, oil spill recovery and shipwrecked ship rescue. All of which are related to the national economy and the people's livelihood, and it is urgent to improve operation efficiency. This work considers connecting double unmanned surface vehicles (DUSVs) with floating ropes to achieve efficiently autonomous round-up and towing. The core of this research is how to realize autonomous cooperative towing control of DUSV under the interference of floating rope hydrodynamic moment and wind, wave and current. Therefore, a vertical formation control algorithm was proposed to improve the control performance of the DUSV. The virtual leader–follower was treated as the guidance target, and the relative distance between the DUSV and the guidance target should be maintained properly. Next, to reduce the probability of drag separation of DUSV, on the basis of the sensors equipped on the floating rope, an adaptive moment control algorithm was designed via the control of the heading angle error and the adjustment of the moment correction. The results of the simulation and water experiments verified the excellent performance of our algorithms. • A system consisting of double USVs (DUSVs) connected to a floating rope is employed. • Vertical formation control algorithm is proposed to improve DUSVs' performance. • An adaptive moment control algorithm is designed to low the drag separation. • Cooperative towing tasks can be fulfilled excellently with our DUSVs and algorithm. [ABSTRACT FROM AUTHOR]

• An agricultural vehicle dynamic-based model is developed by taking into account the internal forces, inertia and road bank angle. • Sliding mode observer (SMO) is designed for estimating the system state so as to adaptively adjust the uncertain bounds of cornering stiffness. • The proposed adaptive sliding mode control(ASMC) method not only guarantees strong robustness with respect to parameter uncertainties, disturbance and varying road conditions, but also requires no prior knowledge of the road information. In this paper, a novel adaptive sliding mode control (ASMC) for path tracking of unmanned agricultural vehicles is proposed. It is shown that the designed ASMC is capable of driving the closed-loop lateral position error dynamics to converge to zero asymptotically, where the sliding mode observer (SMO) is designed to estimate the system state so as to adaptively adjust the uncertain bounds of cornering stiffness. Compared with the classical sliding mode control technique, the proposed ASMC not only guarantees strong robustness with respect to parameter uncertainties, disturbance and varying road conditions, but also requires no prior knowledge of the road information. Simulation and experimental results from a typical agricultural wheeled vehicle are provided to verify the excellent performance of the proposed scheme. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the path-following control of the unmanned surface vehicle (USV) with the consideration of cyber and physical attacks. The actuator of USV has fully unknown dynamics under deception attack, and the USV system consists of complex nonlinear perturbations caused by physical attack. Therefore, a Nussbaum-type function based robust neural event-triggered path-following control scheme is proposed by combining event-triggered technique with Nussbaum approach. Together with hybrid disturbances and model uncertainties, the lumped nonlinearity is online approximated by employing the robust neural damping technique. Within the backstepping framework, a Nussbaum gain function is further designed to tackle unknown control directions in the actuator system. To facilitate the implementation of the proposed algorithm, an input-based event-triggered mechanism is introduced to reduce the channels clogging and the resources wasting between controllers and actuators. Theoretical analysis reveals that all signals of the closed-loop tracking system are bounded and tracking errors can converge to an arbitrarily small neighborhood of zero. Numerical simulation is performed by employing a class of underactuated surface cable-laying vehicle. The corresponding results demonstrate the effectiveness and superiority of the proposed approach. • A novel control algorithm is proposed for the cyber–physical attacks of USV. • The robust damping technique can avoid the frequent updating of NNs weight. • Only two Nussbaum parameters are derived to compensate the undesired perturbation. • The input-based event-triggered design can effectively lower the transmission burden. [ABSTRACT FROM AUTHOR]

To achieve the accurate control of the variable-mass unmanned surface vehicle (USV), this paper proposes an adaptive surge control method with uncertain variation in mass and draft. Aiming at the time-varying characteristics of the USV, the analytical expressions of mass and each hydrodynamic derivative are presented based on the draft and its higher-order terms as independent variables. The model uncertainty problem caused by quantities of nonlinear coupling terms related to the mass and draft is solved. For the high correlation between the motion state and the draft of the variable-mass USV, a sliding mode observer is designed to obtain the estimated varying mass and drat in real-time. The adaptive surge control algorithm of variable-mass USV is proposed, which can update its adaptive laws in real time based on the sliding mode observations. The overall stability of the control system is analyzed via the Lyapunov algorithm. Simulation experiments of several payload delivery missions are carried out. Results show that the proposed controller can achieve high precision surge control under typical missions for variable-mass USV, such as step variation and continuous payload change, and the control effect is remarkably better than the traditional control method. • The coupling relationships in the variable-mass USV's model are decoupled and linearized via different orders of the draft. • A sliding mode observer is designed to estimate the unknown dynamic mass and draft via the USV's motion states in 3 DOF. • The adaptive surge controller of variable-mass USV is proposed and the stability is verified through the Lyapunov algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses distributed formation control of multiple under-actuated unmanned surface vehicles (USVs) subject to input quantization, in addition to the unknown dynamics caused by external sea disturbances and internal model uncertainties. A two-level distributed guidance and neuro-adaptive quantized control architecture is presented to achieve a time-varying formation regardless of the input quantization. Specifically, at the kinematic level, an extended state observer (ESO)-based distributed guidance law is developed to track a time-varying trajectory where the ESO is adopted to estimate the unavailable linear velocity and rate of turn (ROT) of neighboring USVs. At the dynamic level, by using a linear time-varying model to deal with the difficulty caused by quantization and the radial basis function neural networks (RBFNNs) to identify the unknown dynamics, a neuro-adaptive quantized control law is developed where no information on the parameters of quantizers is required. The stability of the proposed two-level formation control architecture is proven on the basis of input-to-state stability, and all signals in the closed-loop system are uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the proposed neuro-adaptive quantized control method for USVs. • The formation control of USVs with input quantization is considered and addressed. • A distributed formation control method is proposed for under-actuated USVs herein. • A linear time-varying model is employed to depict the quantizer. [ABSTRACT FROM AUTHOR]

This paper investigates a novel robust adaptive fault-tolerant control algorithm for the path-following activity of the unmanned surface vehicle (USV) via the multiplied event-triggered mechanism. An improved multiplied event-triggered condition is designed by employing the estimate model with a concise form. Thus, the burden-some problem is released, especially for the channel from sensors to the controller. Besides, two discrete adaptive parameters are constructed to stabilize the perturbation caused by the gain constraint and actuator faults. The model uncertainties and the practical disturbance are compensated by utilizing neural networks (NNs) approximation, in which the weight updating is reduced with the robust neural damping technique. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system. Finally, both the simulation and the physical vehicle-based experiments are illustrated to verify the effectiveness of the algorithm. • A robust fault-tolerant control algorithm is proposed for unmanned surface vehicle. • The adaptive parameters are updated online to compensate for unknown actuator faults. • An improved multiplied event-triggered condition is designed with a concise form. • It can evade the unnecessary information transmission of the onboard control network. [ABSTRACT FROM AUTHOR]