Underactuated Unmanned Surface Vehicle Research Articles

Distributed Formation Maneuvering Quantized Control of Under-Actuated Unmanned Surface Vehicles with Collision and Velocity Constraints

This paper focuses on a distributed cooperative time-varying formation maneuvering issue of under-actuated unmanned surface vehicles (USVs). A fleet of USVs is guided by a parameterized path with a time-varying formation while avoiding collisions and preserving the connectivity in the environment with multiple obstacles. In some surface missions, due to the obstacles in the external environment, the bandwidth limitations of the communication channel, and the hardware components/performance constraints of the USVs themselves, each vehicle is considered to be subject to model uncertainty, actuator quantization, sensor dead zone, and velocity constraints. During the control design process, the radial basis function (RBF) neural networks (NNs) are utilized to deal with nonlinear terms. Based on a nonlinear decomposition method, the relationship between the control signal and the quantization one is established, which overcomes the difficulty arising from actuator quantization. A Nussbaum function is introduced to handle the unknown output dead zone problem caused by reduced sensor sensitivity. Moreover, a universal-constrained function is employed to satisfy both the constrained and unconstrained requirements during formation keeping and obstacle avoidance. The Lyapunov stability theory confirmed that the error signals are uniformly ultimately bounded (UUB). The simulation results demonstrate the effectiveness of the proposed distributed formation control of multiple USVs.

Comparison of Linear and Nonlinear Model Predictive Control in Path Following of Underactuated Unmanned Surface Vehicles

Model predictive control (MPC), an extensively developed rolling optimization control method, is widely utilized in the industrial field. While some researchers have incorporated predictive control into underactuated unmanned surface vehicles (USVs), most of these approaches rely primarily on theoretical simulation research, emphasizing simulation outcomes. A noticeable gap exists regarding whether predictive control adequately aligns with the practical application conditions of underactuated USVs, particularly in addressing real-time challenges. This paper aims to fill this void by focusing on the application of MPC in the path following of USVs. Using the hydrodynamic model of USVs, we examine the details of both linear MPC (LMPC) and nonlinear MPC (NMPC). Several different paths are designed to compare and analyze the simulation results and time consumption. To address the real-time challenges of MPC, the calculation time under different solvers, CPUs, and programming languages is detailed through simulation. The results demonstrate that NMPC exhibits superior control accuracy and real-time control potential. Finally, we introduce an enhanced A* algorithm and use it to plan a global path. NMPC is then employed to follow that path, showing its effectiveness in tracking a common path. In contrast to some literature studies using the LMPC method to control underactuated USVs, this paper presents a different viewpoint based on a large number of simulation results, suggesting that LMPC is not fit for controlling underactuated USVs.

Fixed-Time Path-Following-Based Underactuated Unmanned Surface Vehicle Dynamic Positioning Control

The development of dynamic positioning (DP) algorithms for an unmanned surface vehicle (USV) is attracting great interest, especially in support of complex missions such as sea rescue. In order to improve the simplicity of the algorithm, a DP algorithm based on its own path following control ability is proposed. The algorithm divides the DP problem into two parts: path generation and path following. The key contribution is that the DP ability can be realized only by designing the path generation method, rather than a whole complex independent DP controller. This saves the computing power of the USV onboard computer and can effectively reduce the complexity of the algorithm. In addition, the fixed-time LOS guidance law is designed to improve the convergence rate of the system state in path-following control. The reasonable selection of speed and a heading controller ensures that the number of design parameters to be determined is at a low level. The above algorithms have been thoroughly evaluated and validated through extensive computer simulations, demonstrating their effectiveness in simulated and real marine environments. The simulation results verify the ability of the proposed algorithm to realize the dynamic positioning of USVs, and provide a practical scheme for the design of the dynamic positioning controller of USVs.

Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Unmanned surface vehicles (USVs) are highly manoeuvrable and autonomous, and hold significant potential for both military and civilian applications, particularly in formation operations. However, because of their underactuated nature, USVs struggle to navigate in complex maritime conditions during formation. At present, most of the technology is devoted to Unmanned Areial Vehicles and ground robots; these methods cannot be well applied to underactuated USVs. Moreover, the rationality of local path planning decision-making for underactuated USVs formation is still lacking. This study proposes an interfered fluid dynamic system (IFDS)-based local path planning method, called USV-IFDS, specifically designed for the formation of underactuated USVs. This method incorporates the IFDS obstacle avoidance approach, while adapting it through modifications and the inclusion of the kinematic constraints of USVs, thereby enhancing its applicability to the maritime environment. By decomposing the flow field velocity vector and implementing a formation control strategy, we effectively address the challenges in forming underactuated USVs and enhance the efficiency of USV formation local path planning. The proposed formation technique is predicated on the highly robust virtual structure method. Simulations of formation local path planning indicate that our method produces smooth paths, therefore validating its practical applicability to underactuated USV formations.

Event-Triggered Based Trajectory Tracking Control of Under-Actuated Unmanned Surface Vehicle With State and Input Quantization

Event-Triggered Based Trajectory Tracking Control of Under-Actuated Unmanned Surface Vehicle With State and Input Quantization

Global finite-time guidance algorithm and constrained formation control using novel nonlinear mapping function for underactuated multiple unmanned surface vehicles

The formation control issue of multiple underactuated unmanned surface vehicles (USVs) with state constraints is discussed in this work. First, the leader–followers system is used to present a global finite-time guidance algorithm for followers with velocity constraints. This algorithm can handle the case where the starting formation is not the intended formation. Second, the state constraints of the USV control subsystem is then handled by a novel nonlinear mapping function (NMF), which has the following benefits: (1) The designed NMF can respond to changing conditions with flexibility since it can handle symmetric constraints. (2) The situation where constraint bounds trend to infinity can be handled by it. (3) The designed NMF has a speed factor that enables it to be modified to track speed. Third, finite-time constrained controllers are proposed to provide formation control for multiple USVs by utilizing the unconstrained system. Next, while guaranteeing that the states are contained within the bounds, the theoretical analysis verifies that the system tracking error stabilizes in finite time and establishes the boundedness of all tracking error signals. To confirm the accuracy and superiority of the theoretical results, two case studies are created.

Flocking of Under-Actuated Unmanned Surface Vehicles via Deep Reinforcement Learning and Model Predictive Path Integral Control

Flocking of Under-Actuated Unmanned Surface Vehicles via Deep Reinforcement Learning and Model Predictive Path Integral Control

Kinodynamic Motion Planning via Funnel Control for Underactuated Unmanned Surface Vehicles

Kinodynamic Motion Planning via Funnel Control for Underactuated Unmanned Surface Vehicles

Path Following Control With Sideslip Reduction for Underactuated Unmanned Surface Vehicles

Path Following Control With Sideslip Reduction for Underactuated Unmanned Surface Vehicles

Event-Triggered Path Following Robust Control of Underactuated Unmanned Surface Vehicles with Unknown Model Nonlinearity and Disturbances

An effective path-following controller is a guarantee for stable sailing of underactuated unmanned surface vehicles (USVs). This paper proposes an event-triggered robust control approach considering an unknown model nonlinearity, external disturbance, and event-triggered mechanism. The proposed method consists of guidance and dynamic control subsystems. Based on the tracking error dynamics equations, the guidance subsystem is designed to achieve the guidance law. For the dynamic control subsystem, the radial basis function neural networks (RBFNNs) are designed to approximate the unknown model nonlinearity and external disturbances to improve the robustness of the proposed method. In addition, an event-triggered mechanism is constructed to reduce the triggering times. The closed-loop system is proven to be stable, and the effectiveness of the proposed method is illustrated through simulation results.

Smooth Sliding Mode Control for Path Following of Underactuated Surface Vehicles Based on LOS Guidance

In this paper, a solution to the problem of following a curved path for underactuated unmanned surface vehicles (USVs) with unknown sideslip angle and model uncertainties is studied. A novel smooth sliding mode control (SSMC) based on a finite-time extended state observer (FTESO) for heading control is proposed. Firstly, the model of a USV with rudderless double thrusters is established. Secondly, the path-following error dynamics of a USV is established in a path-tangential reference frame. Thirdly, a finite-time observer is introduced to estimate the unidentified sideslip angle, and the line-of-sight (LOS) guidance law is applied to produce the desired heading angle. Finally, an SSMC controller is proposed to force USV tracking at the desired heading angle and surge speed, in which FTESO is used to estimate and compensate the unknown disturbance in sliding mode dynamics. The theoretical analysis for FTESO-SSMC verifies that the controller can provide finite-time convergence to and stability on the sliding surface. Simulation studies and contrast test are conducted to demonstrate the robustness and rapidity of the proposed FTESO-SSMC controller.

Adaptive NN formation tracking control for the multiple underactuated USVs with prescribed performance and input saturations

A novel neural network adaptive prescribed performance formation control algorithm is investigated for multiple underactuated unmanned surface vehicles (USVs) with asymmetric input saturations and unknown external disturbances. Initially, a new prescribed performance function is adopted to ensure that USV formation errors reach and keep within a predetermined range at a preset time. Then, the underactuated problem of USVs is addressed by an additional term generated by a Nussbaum function, and the model uncertain dynamics set is compensated by the radial basis function neural network (RBF NN) minimum parameter method. Meanwhile, adaptive techniques are employed to deal with external disturbances and unknown approximation errors. Furthermore, a continuously differentiable asymmetric saturation model based on the Gaussian error function is introduced to design the controller, eliminating the influence of non-smooth input saturations. Finally, the boundedness of all signals in the closed-loop system is given by the Lyapunov stability theory proof, and contrast simulation experiments verify the effectiveness and superiority of the proposed algorithm.

Fuzzy optimal tracking control for nonlinear underactuated unmanned surface vehicles

In this paper, a reinforcement Q-learning fuzzy optimal tracking control method is investigated for nonlinear underactuated unmanned surface vehicles (USVs) with external disturbances. Firstly, the motion dynamics of USVs are described by the Takagi–Sugeno (T–S) fuzzy discrete-time systems. Secondly, by applying parallel distributed compensation (PDC) method and constructing cost function, the existence condition of optimal solutions is derived and reduced to the algebraic Riccati equations (AREs). To solve the solutions of the GAREs, a Q-learning value iteration (VI) algorithm is further developed, which can be implemented by eliminating the demand of initial allowable control policy and system information. The developed strategy has the advantage that the optimal control policy can ensure the USV to be stable and the desired reference signal can also be well tracked by the USV output. Finally, the proposed fuzzy optimal control algorithm is applied to control the USV, the simulation results verify the feasibility of the proposed optimal control algorithm.

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

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.

Global fixed-time adaptive fuzzy path following control for unmanned surface vehicles subject to lumped uncertainties and actuator saturation

A global fixed-time control strategy is presented for addressing the path following problem of underactuated unmanned surface vehicles (USVs) with lumped uncertainties and actuator saturation in this paper. Firstly, a novel fixed-time dual adaptive line-of-sight (FTDALOS) guidance law is proposed, which provides the desired heading angle and surge velocity. Subsequently, considering the complex marine environment, a novel fixed-time adaptive fuzzy controller (FTAFC) is developed. Additionally, to solve the issues of lumped uncertainties and actuator saturation, a fixed-time adaptive fuzzy control strategy and a fixed-time dynamic auxiliary saturation system are proposed. It is demonstrated that the proposed strategy can drive the USV along the desired path smoothly and rapidly, and all errors of the closed-loop system can converge to arbitrary small neighborhoods of the origin within fixed-time intervals. Finally, the effectiveness and superiority of FTDALOS guidance law and FTAFC are verified by theoretical analysis and simulation studies.

Vector field-based integral LOS path following and target tracking for underactuated unmanned surface vehicle

The paper focuses on developing a method that enables the unmanned surface vehicle (USV) to effectively track dynamic objects on the water surface. The integral line-of-sight (LOS) guidance law is improved by introducing the concept of vector field for curved paths and the time-varying lookahead distance with a smoothing control parameter for straight paths. These enhancements facilitate faster convergence to the desired path with reduced oscillations and allow for mitigating the impact of constant external disturbances. The research demonstrates that the proposed guidance law exhibits κ-exponential stability when converging towards a desired path composed of both straight lines and curves. The results presented in the paper demonstrate that the proposed method effectively improves the accuracy of tracking dynamic targets for unmanned surface vehicles while ensuring the safety of docking operations.

Threat potential field based Pursuit–Evasion Games for underactuated Unmanned Surface Vehicles

Unmanned Surface Vehicles (USVs) have been widely applied in ocean engineering. However, there are few studies for Pursuit–Evasion Game based on USVs. Most relevant pursuit game strategies based on nonrepresentational agents are simple and do not consider the USV’s underactuation. In order to solve this problem, this paper proposes a distributed pursuit game algorithm for USVs based on Threat potential fields (TPFs). The pursuit planning can be divided into three parts based on algorithm structure: First, according to the evader’s information, the proposed algorithm can quantitatively establish a unique TPF for evaluating the danger of the evader. Second, in the TPF, energy peak and energy valley concepts are innovatively presented to provide suitable conditions for the hunters’ pursuit. Third, the pursuit game optimization model is established based on a TPF for the hunters’ game decision-making by considering the orientation, heading, and formation shape of the USVs in the pursuit process. The proposed game optimization model is then solved using the particle swarm optimization algorithm to obtain the strategies. Finally, the effectiveness of the proposed algorithm is demonstrated via numeric simulation of USVs.

An adaptive error constraint line-of-sight guidance and finite-time backstepping control for unmanned surface vehicles

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.

Constrained control using novel nonlinear mapping for underactuated unmanned surface vehicles with unknown sideslip angle

This paper addresses the problem of guidance and control for underactuated unmanned surface vehicles (USVs) with state constraints and input saturation, in support of enabling an underactuated USV to follow a parameterized curved path in the case of unknown sideslip angle and cross-tracking error constraint. First, a cross-tracking error constraint line-of-sight (LOS) guidance law with sideslip angle compensation is originally designed to guide an underactuated USV to convergence to the desired path within a time-varying cross-tracking error constraint. Second, a novel nonlinear mapping (NM) function is first constructed to map the heading and surge control subsystems with state constraints to unconstrained nonlinear systems, transforming the constrained control problem into the unconstrained control problem. Subsequently, adaptive fuzzy control laws are designed to achieve the control objectives for the USV using the new unconstrained nonlinear systems with unknown disturbance and input saturation. Then, a series of theoretical analyses using input-to-state stability theories are presented to prove the boundness of the tracking errors for the underactuated USV during path following. Finally, numerical results obtained using a physics-based simulation model are shown to reveal the effectiveness of the guidance and control algorithms.

Trajectory tracking control of unmanned surface vehicle based on exponential global fast terminal sliding mode control

This article studies the trajectory tracking of an underactuated unmanned surface vehicle. The trajectory tracking controller is divided into two parts: the kinematics controller and the dynamic controller. Taking the hull coordinate system of the unmanned surface vehicle as the datum plane, the kinematics controller is designed based on the backstepping method, and the virtual control input of the speed and the heading deviation of the ship are obtained. A dynamic controller is designed through an exponential sliding surface to stabilize the error of speed and heading deviation, thereby stabilizing the position error. In the process of designing the controller, the stability of the entire closed-loop control system is analyzed using Lyapunov stability theory. Finally, the designed numerical simulation experiment is carried out. The proposed exponential sliding mode controller is compared with the global integral sliding mode controller, and it is verified that the proposed controller has better effectiveness and robustness in the trajectory tracking of the underactuated unmanned surface vehicle.