Deep Neural Network-Based Adaptive Control for Unmanned Surface Vehicles With Uncertain Dynamics

A consensus algorithm for coordinated control of unmanned surface vehicles (USVs) is studied. The USVs have different driving directions and initial velocities, so the consensus algorithm is needed to coordinate the directions and velocities of the USVs. Firstly, based on the first order linear model of USVs, get the first order nonlinear differential equations of high frequency, and the simulate environment of USVs. Then, based on the Vicsek model, the consensus algorithm in cooperative control of USVs is proposed. Aiming at the problem that USV's angular velocity lags behind the rudder angle, this paper improves the traditional PD control method, and adopts cascade PID control. So, USV can reach the same driving direction in a shorter time. Finally, simulations prove that the algorithm is feasible and compare the difference of efficiency between the PD controller and the cascade PID controller.

The waypoint behavior is a method of trajectory tracking control for unmanned surface vehicle (USV). We take the waypoint behavior as the research object, choose speed, steering angle, capture radius, slip radius, lead distance, lead damper as features, and establish a prediction model for trajectory tracking control of USV based on deep neural network. The model effectively predicts the navigation effect and provides assistance and reference for the maneuvering decision of the USV. Test results show that the predicted results and the reference samples have same tendency. The proposed model can improve the performance of trajectory tracking control for the USV.

This article addresses the challenge of designing obstacle avoidance control strategies for unmanned ship systems operating in environments with moving obstacles and unmodeled dynamics. First, we utilize an enhanced artificial potential field method to generate real-time paths that allow unmanned ships to avoid obstacles effectively, overcoming the design challenges posed by moving obstacles. Next, we incorporate convex optimization techniques to create a novel adaptive neural network control strategy aimed at tackling potential dynamic uncertainties in unmanned ship systems. Finally, we present simulation results that demonstrate the effectiveness of the proposed dynamic obstacle avoidance control strategy.

In this paper, the formation control problem of unmanned surface vehicles (USVs) is investigated. Unlike the classical formation control problem where the reference signal is required to be second-order differentiable with respect to time, we consider a more general autonomous dynamic system as the reference system. A novel adaptive fuzzy output regulation approach is presented to solve the formation control problem, where a set of regulator equations using only approximation information and a distributed observer are constructed to obtain the feedforward information of the reference system. Based upon this, a distributed adaptive fuzzy control law is designed by using the backstepping technique. It is shown that USVs can effectively achieve the desired formation with the tracking error being adjusted as small as possible. Simulation studies demonstrate that the proposed formation control law is effective and efficient.

Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm.

Model-based deep reinforcement learning for data-driven motion control of an under-actuated unmanned surface vehicle: Path following and trajectory tracking

This study explores the challenge of formation control for unmanned surface vehicles (USVs) with dynamic uncertainties and external disturbances. A hierarchical fixed-time control (HFTC) framework is designed to solve the estimation problem and the formation tracking problem for USVs. The HFTC framework consists of estimators based on the impulsive control, which help the USVs in the network obtain states of leader accurately, and local controllers with fixed-time extended state observer (FTESO), which drive the USVs achieve predefined configuration in fixed time. Through systematic analysis, sufficient conditions are given for guaranteeing the stability and convergence of the studied closed-loop system. Ultimately, this paper presents simulation results to validate the effectiveness of the implemented control scheme.

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.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

In this paper, a finite-time trajectory tracking control approach is proposed for an unmanned surface vehicle (USV) with unknown dead-zones and unknown disturbances. External disturbances can be observed exactly by a robust homogeneous differentiator with finite-time convergence. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as system uncertainties, a finite-time controller for trajectory tracking of an USV is eventually addressed via the non-singular terminal sliding mode control technique. Moreover, the boundary layer method with a novel saturation function is employed to reduce the chattering and to ensure that tracking errors can be rendered to zero in a finite time. Simulation results demonstrate the effectiveness of the proposed approach.

In order to solve the problem of large course control error of underactuated unmanned surface vehicle (USV), a controller is designed based on sliding mode control algorithm, and the sliding mode surface is optimized by combining fuzzy control algorithm to accelerate error convergence speed and reduce steady-state error. Finally, based on the “Jinghai 8-a” small unmanned measurement vehicle of Shanghai University, the simulation verifies the stability of the USV in course keeping control and its robustness to external disturbances.

A path following control scheme based on virtual transition line-of-sight (LOS) guidance law is proposed for model-free unmanned surface vehicle (USV). In the guidance part, an adaptive LOS circle radius based on the transition layer is designed to improve the convergence speed of USV. Aiming at reducing the overshoot of turning, a small angle transition steering strategy is proposed. In the heading and speed control part, the intelligent adaptive S-plane (IAS) heading controller and intelligent adaptive integral S-plane (IAIS) speed controller with strong robustness are designed respectively. In order to improve the following efficiency, a speed solver based on heading deviation and path point distance is designed. The effectiveness of the guidance strategy and the control algorithms of heading and speed proposed in this paper are verified by the comparative simulations.

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.

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the "singularity" problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.

SummaryIn this article, an optimized formation control algorithm is presented for unmanned surface vehicles (USVs) with collision avoidance and prescribed performance. The prescribed formation geometry is designed in the leader‐follower formation architecture, in which each vehicle tracks its intermediary leader with preserving a desired separation. A prescribed performance control design technique is introduced to guarantee the transient and steady‐state performance specifications on formation errors. Radial basis function neural networks (NNs) are employed to approximate modeling uncertainties including damping terms and unmodeled dynamics. Based on an actor‐critic learning strategy, a reinforcement learning (RL) algorithm is proposed to ensure the optimality of formation control and the specified tracking accuracy simultaneously, in which actor NNs take appropriate control behaviors by interacting with the external environment, and critic NNs evaluate the control performance and generate a reinforcement signal to actor NNs for facilitating the improvement of subsequent behaviors. Stability analysis shows that the proposed optimal formation controller achieves semi‐global uniform ultimate boundedness of closed‐loop adaptive systems with prescribed performance. Comparative simulation results illustrate the effectiveness and superiority of the presented control algorithm.