Underactuated Unmanned Surface Vessels Research Articles

A hierarchical LFFC control scheme for underactuated unmanned surface vessels based on unreliable communication interaction

A hierarchical LFFC control scheme for underactuated unmanned surface vessels based on unreliable communication interaction

Event-triggered simultaneous tracking control and stabilization of underactuated USVs

This study addresses the control problem of simultaneous tracking control and stabilization for underactuated unmanned surface vessels (USVs) with only two available inputs in the surge and yaw directions. Firstly, two velocity modifications are introduced to transform the error dynamics into two decoupled subsystems. Then, a novel error transforming scheme is presented to reduce the complexity of controller expressions and overcome the difficulties caused by underactuated dynamics. Based on the above transforming scheme and the event-triggered control (ETC) mechanism, a unified control method is designed to guarantee that the error states converge to a bounded neighborhood of the origin while excluding the Zeno behavior of the controllers. In addition, two radial basis function neural networks (RBFNNs) are applied to approximate the unknown dynamics and additional control errors caused by the ETC mechanism. Finally, comparative simulation experiments are performed to validate the effectiveness of the proposed controller.

Predictive trajectory tracking control for the USV in networked environments with communication constraints

The trajectory tracking control problem of under-actuated unmanned surface vessels (USVs) suffering from communication constraints (network delay, packet loss, packet disorder) as well as external environment disturbance is investigated in this paper. To solve the aforementioned trajectory control problem, a nonlinear networked predictive control strategy based on the discrete sliding mode framework is proposed. Initially, using the forward Euler discretization method, a novel discrete virtual velocity control law is designed to transform trajectory tracking control into virtual velocity tracking control. Then, a sliding mode controller is suggested to suppress external disturbance of the USV. In addition, a networked sliding mode predictive control (NSMPC) scheme is proffered to guarantee active compensation of the communication constraints and stability of the trajectory tracking system simultaneously. Afterward, a theoretical analysis is given to show that the closed-loop system is uniformly bounded stable after the introduction of the digital network. Finally, extensive comparative simulation examples are provided to demonstrate the effectiveness of the proposed control strategy.

The Non-Singular Terminal Sliding Mode Control of Underactuated Unmanned Surface Vessels Using Biologically Inspired Neural Network

Underactuated Unmanned Surface Vessels (USVs) are widely used in civil and military fields due to their small size and high flexibility, and trajectory tracking control is a critical research area for underactuated USVs. This paper proposes a trajectory tracking control strategy using the Biologically Inspired Neural Network (BINN) for USVs to improve tracking speed and accuracy. A virtual control law is designed to obtain the required virtual velocity for trajectory tracking control, in which the velocity error is calibrated to ensure that the position error converges to zero. To observe and compensate for unknown and complex environmental disturbances such as wind, waves, and currents, a nonlinear extended state observer (NESO) is designed. Then, a controller based on Non-singular Terminal Sliding Mode (NTSM) is designed to resolve the problems of singular value and controller chattering and to improve the controller response speed. A BINN is introduced to simplify the process of differentiation, reduce the input values of the initial state, and solve the problem of thruster input saturation. Finally, the Lyapunov stability theory is utilized to analyze the stability of the proposed algorithm. The simulation results show that the proposed algorithm has a higher trajectory tracking accuracy and speed than traditional methods.

Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking

The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified.

Neural Network-Based Adaptive Sigmoid Circular Path-Following Control for Underactuated Unmanned Surface Vessels under Ocean Disturbances

This study describes a circular curve path-following controller for an underactuated unmanned surface vessel (USV) experiencing unmodeled dynamics and external disturbances. Initially, a three degrees of freedom kinematic model of the USV is proposed for marine environmental disturbances and internal model parameter deterrence. Then, the circular path guidance law and controller are designed to ensure that the USV can move along the desired path. During the design process, a proportional derivative (PD)-based sigmoid fuzzy function is applied to adjust the guidance law. To accommodate unknown system dynamics and perturbations, a radial basis function neural network and adaptive updating laws are adopted to design the surge motion and yaw motion controllers, estimating the unmodeled hydrodynamic coefficients and external disturbances. Theoretical analysis shows that tracking errors are uniformly ultimately bounded (UUB), and the closed-loop system is asymptotically stable. Finally, the simulation results show that the proposed controller can achieve good control effects while ensuring tracking accuracy and demonstrating satisfactory disturbance rejection capability.

Energy-based trajectory tracking control of under-actuated unmanned surface vessels

In this paper, a novel energy-based trajectory tracking control strategy for under-actuated unmanned surface vessels (USVs) in the presence of unknown environmental disturbances is presented. The port-Hamiltonian framework is utilized to propose a passivity-based control model in body-fixed coordinates of the USVs. An adaptive disturbance estimation method is detailed and used to accurately estimate the environmental disturbances affecting USV motion. Furthermore, a passive and Hamiltonian structure-preserving controller is employed to achieve the desired trajectory of the USV system, and the stability of the desired target dynamic system is rigorously proven. The effectiveness of the proposed controller is demonstrated through simulations and experiments on a USV experimental platform, showcasing its capability of trajectory tracking performance and mitigating the effects of disturbances.

Event trigger based adaptive neural trajectory tracking finite time control for underactuated unmanned marine surface vessels with asymmetric input saturation

An adaptive finite time trajectory tracking control method is presented for underactuated unmanned marine surface vessels (MSVs) by employing neural networks to approximate system uncertainties. The proposed algorithm is developed by combining event-triggered control (ETC) and finite-time convergence (FTC) techniques. The dynamic event-triggered condition is adopted to avert the frequent acting of actuators using an adjustable triggered variable to regulate the minimal inter-event times. While solving the system uncertainties and asymmetric input saturation, an adaptive neural networks based backstepping controller is designed based on FTC under bounded disturbances. In addition, via Lyapunov approach it is proved that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, simulations results are shown to demonstrate the effectiveness of this proposed scheme.

Event-triggered sliding mode trajectory tracking control for underactuated unmanned surface vessel

This article considers the trajectory tracking control for underactuated unmanned surface vessel and actuator loss caused by frequent updating of the controller. An event-triggered sliding mode trajectory tracking control algorithm is proposed for the problems. A sliding mode tracking controller is designed by the integral sliding surface at the beginning to guarantee global asymptotic tracking of the desired trajectory. Based on the tracking controller, the event-triggered condition is designed in the channel of the controller to actuator, which effectively reduces the update frequency of the controller. Theoretical analysis proves that the states of underactuated unmanned surface vessel are uniformly stable, while the Zeno behavior is prevented. The results of the numerical simulation are presented to demonstrate the effectiveness of the proposed algorithm.

Finite Time Trajectory Tracking with Full-State Feedback of Underactuated Unmanned Surface Vessel Based on Nonsingular Fast Terminal Sliding Mode

Marine transportation and operations have attracted the attention of more and more countries and scholars in recent years. A full-state finite time feedback control scheme is designed for the model parameters uncertainty, unknown ocean environment disturbances, and unmeasured system states in the underactuated Unmanned Surface Vessel (USV) trajectory tracking control. The external wind, wave and current environmental disturbances and model parameters perturbation are extended by Nonlinear Extended State Observer (NESO) to the state of the system, namely complex disturbances. The complex disturbances, positions and velocities of USV can be observed by NESO and feedback to USV control system. Next, the underactuated USV error model is obtained by operating the obtained feedback information and the virtual ship model. According to the error model, a Nonsingular Fast Terminal Sliding Model surface (NFTSM) is constructed to realize finite-time control. The control law is deduced through the Lyapunov stability theory to ensure the stability of the system. The results of MATLAB numerical simulations under different disturbances show that the trajectory tracking algorithm has fast responses, and a good convergence of the errors is observed, which verifies the effectiveness of the designed scheme.

Augmented model-based dynamic positioning predictive control for underactuated unmanned surface vessels with dual-propellers

The dynamic positioning control for underactuated unmanned surface vessel (u-USV) is difficult but urgently needed. This paper aims to propose an effective dynamic positioning controller for u-USV. The accuracy of the internal dynamics of u-USV is improved by designing an augmented model (AM) based on a residual neural network. After that, an AM-based finite-time observer (AM-FTO) is designed. The disturbance observed by the AM-FTO is mainly the environmental disturbance, not the original composite disturbance anymore. Further, the direction of the environmental disturbance can be estimated. Taking the direction as the desired heading, a dynamic positioning controller is designed for u-USV based on the nonlinear model predictive control (NMPC) method. Simulations and experiments show that the proposed method can track the internal dynamics of u-USV with high accuracy. Additionally, the designed dynamic positioning controller has better performance than the existing popular NMPC method.

Distributed dynamic edge-based event-triggered formation control for multiple underactuated unmanned surface vessels

This paper investigates the distributed formation control problem for underactuated unmanned surface vessels (USVs) under communication resources constraints and unknown external disturbances. Based on the adaptive algorithm and the minimal learning parameter (MLP) algorithm, a distributed dynamic edge-based event-triggered controller is proposed to achieve the desired formation maneuver. Different from the node-based event-triggered strategy, the edge-based event-triggered mechanism in this paper is developed depends on the information of every edge. Besides, the internal dynamic variable is embedded in the triggering function to obtain larger triggered time interval. Only the triggered condition of every edge is reached can the communication happen, such that the communication burden will be effectively reduced. The theoretical result exhibits that the tracking errors will converge into a tiny region under this control scheme. Finally, numerical simulations are presented to demonstrate the validity of the proposed controller.

Trajectory Tracking of Underactuated Unmanned Surface Vessels: Non-Singular Terminal Sliding Control with Nonlinear Disturbance Observer

An underactuated unmanned surface vessel (USV) is a nonholonomic system, in which trajectory tracking is a challenging problem that has drawn more and more attention from researchers recently. The control of trajectory tracking is of critical importance since it determines whether the task can be carried out successfully. In this paper, a non-singular terminal sliding model (NTSM) controller is proposed for the trajectory tracking control of the underactuated USV, with a nonlinear disturbance observer which is designed to measure complex environmental disturbances such as wind, waves, and currents. Exploratory simulations were carried out and the results show that the proposed controller is effective and robust for the trajectory tracking of underactuated USVs in the presence of environmental disturbances.

Finite-time trajectory tracking control for under-actuated unmanned surface vessels with saturation constraint

This article investigates the robust finite-time trajectory tracking control problem of under-actuated unmanned surface vessels (USVs) subject to model uncertainties, saturation constraints, and external disturbances. Firstly, the kinematic and dynamic models of under-actuated USVs are transformed into an equivalent tracking error dynamic by resorting to a novel output redefinition-based dynamic transformation (ORDT). Secondly, a smooth dead-zone operator-based model (DOBM) is introduced to deal with the control input saturation constraints problem. On basis of these, a sliding mode-based controller (SMC) is developed, which possesses the properties of chattering-free and finite-time convergence. Later, Lyapunov stability proved that the proposed control strategy is capable of guaranteeing the boundedness of all closed-loop signals, in spite of the parametric uncertainties, external disturbance, and input saturation constraints. Finally, numerical simulations illustrate the effectiveness of the proposed control approach.

Practical Finite-Time Event-Triggered Trajectory Tracking Control for Underactuated Surface Vessels With Error Constraints

This paper proposes a robust trajectory tracking control method for underactuated unmanned surface vessels (USVs) with input saturation based on a relative threshold event-triggered mechanism (ETM), prescribed performance and finite-time convergence. First, to address the challenges posed to controller design by the nondiagonal inertia matrix, coordinate transformation technology is adapted to change the vessel position. An asymmetric smooth saturation function is employed to address the input saturation problem. Second, an adaptive neural network (NN) is developed to approximate the uncertain nonlinear dynamics and external environmental disturbances. Third, the performance function constraints are integrated into the tan-type barrier Lyapunov function (BLF) to greatly enhance the robustness of the system by combining with the finite-time convergence property. In addition, the dynamic surface control (DSC) technique is employed to address the differential explosion problem. Subsequently, a relative threshold ETM is incorporated into the controller to reduce the communication burden. The rationality and feasibility of the proposed algorithm are confirmed by theoretical analysis and numerical simulations.

Trajectory Tracking Control for Underactuated USV with Prescribed Performance and Input Quantization

This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations.

Path Following Optimization for an Underactuated USV Using Smoothly-Convergent Deep Reinforcement Learning

This paper aims to solve the path following problem for an underactuated unmanned-surface-vessel (USV) based on deep reinforcement learning (DRL). A smoothly-convergent DRL (SCDRL) method is proposed based on the deep Q network (DQN) and reinforcement learning. In this new method, an improved DQN structure was developed as a decision-making network to reduce the complexity of the control law for the path following of a three-degree of freedom USV model. An exploring function was proposed based on the adaptive gradient descent to extract the training knowledge for the DQN from the empirical data. In addition, a new reward function was designed to evaluate the output decisions of the DQN, and hence, to reinforce the decision-making network in controlling the USV path following. Numerical simulations were conducted to evaluate the performance of the proposed method. The analysis results demonstrate that the proposed SCDRL converges more smoothly than the traditional deep Q learning while the path following error of the SCDRL is comparable to existing methods. Thanks to good usability and generality of the proposed method for USV path following, it can be applied to practical applications.

Augmented safety guarantee-based area keeping control for an underactuated USV with environmental disturbances

This paper proposes an accurate area keeping control method with augmented safety guarantee for an underactuated unmanned surface vessel (UUSV) with environmental disturbances. It can mediate the safety and the stability during the UUSV area keeping control. Firstly, inspired by the notion of control barrier function (CBF) in general manifolds, an augmented safety guarantee is constructed to deal with the nonlinear safety constraints of the area keeping control for the UUSV. Then, the stability constraints for the UUSV area keeping control are formed by involving a control Lyapunov functions (CLF) and a weather optimal control (WOC) method. Finally, by unifying the augmented safety guarantee and the stability constraints, an accurate area keeping controller is designed through a quadratic program (QP), which can consider the safety problem into the optimal area keeping control of the UUSV under environmental disturbances. Comparing the proposed method to the WOC, simulation results validate the feasibility and the superiority of the proposed method.

Formation path following control of underactuated USVs

This paper proposes a formation control method for two underactuated unmanned surface vessels (USVs) to follow curved paths in the presence of ocean currents. By uniting a line-of-sight (LOS) guidance law and the null-space-based behavioral control (NSB) framework, we achieve curved path following of the barycenter, while maintaining the desired vessel formation.The closed-loop dynamics are investigated using cascaded systems theory, and it is shown that the closed-loop system is USGES and UGAS, while the underactuated sway dynamics remain bounded. Simulations show that the barycenter task errors converge to zero, validating the theoretical results, while a small cross-track error is observed in the experiments.

Model-Free Containment Control of Underactuated Surface Vessels Under Switching Topologies Based on Guiding Vector Fields and Data-Driven Neural Predictors.

This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according to the underlying switching topologies. Next, guiding-vector-field-based guidance laws are designed such that each USV can track its reference point, enabling smooth transitions during topology switching. Finally, data-driven neural predictors by utilizing real-time and historical data are developed for estimating total uncertainties and unknown input gains, simultaneously. Based on the learned knowledge from neural predictors, adaptive kinetic control laws are designed and no prior information on kinetic model parameters is required. By using the proposed method, the fleet is able to converge to the convex hull spanned by multiple virtual leaders under switching topologies regardless of fully unknown kinetic models. Through stability analyses, it is proven that the closed-loop control system is input-to-state stable and the tracking errors are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed cooperative control architecture for multiple underactuated USVs with fully unknown kinetic models.