An improved fixed-time disturbance observer-based dynamic positioning control for unmanned surface vehicles under sparse false data injection attacks

Exponential smoothing-based fixed-time path-guided coordinated control of unmanned surface vehicles under communication interruption

In this paper, a novel deep reinforcement learning algorithm Proximal Policy Optimization (PPO) based on F-divergence is proposed to realize the motion control of unmanned surface vehicle. Aiming at the nonlinear and underactuated characteristics of unmanned surface vehicle system, the new reinforcement learning algorithm can overcome the problem that PPO algorithm falls into local optimization in the training process, and improve the diversity of algorithm exploration. Based on the Open AI simulation environment, this paper analyzes the motion law of the unmanned surface vehicle on the water surface, and establishes a three-degree of freedom kinematics and dynamics mathematical model. An improved reinforcement learning algorithm is used to design the motion controller of unmanned surface vehicle, and a compound reward is designed, which effectively improves the learning efficiency of the network. Simulation results shows the effectiveness of the improved Proximal Policy Optimization algorithm in the motion control of unmanned surface vehicle, and verify the superiority of the improved algorithm.

This paper investigates the target-guided coordinated control (TACC) of unmanned surface vehicles (USVs). To enable USVs to rapidly track non-cooperative targets, a prescribed-time TACC method based on novel Chebyshev orthogonal neural network (CONN) is proposed in this paper. Firstly, a novel TACC architecture with decoupled kinematic and kinetic subsystems is designed, including dynamic surface control technology for tracking the non-cooperative target accurately. Secondly, a prescribed-time TACC system is proposed to make system states achieve convergence within the prescribed time. Finally, the prescribed-time theory is introduced into the weight update law of CONN, which enables more accurate and faster estimation of external time-varying disturbance. The effectiveness of the convergence time through the overall TACC system is verified through stability analysis. The comparative simulation results indicate the effectiveness of the proposed control method.

This paper investigates the formation control problem for unmanned surface vehicles (USVs) under stochastic false data injection (FDI) attacks. The primary objective is to ensure that the USVs in the formation can achieve a predefined formation configuration while resisting external disturbances and FDI attacks in a bandwidth-constrained communication environment. An extended diagonal matrix-based model is firstly established to characterize the impact of FDI attacks. Subsequently, an extended state observer is designed to estimate the unknown system states and disturbance signals. By integrating quantized estimation information and a disturbance compensation mechanism, a formation guidance law for USVs is formulated to mitigate the effects of disturbances and stochastic FDI attacks, thereby achieving the predefined formation configuration. Finally, simulation experiments are conducted to validate the effectiveness of the proposed strategy, providing empirical evidence for its practical applicability.

In this paper, the automatic control of a single unmanned surface vehicle (USV) pushing a floating load is developed and theoretically analyzed. This represents a challenging control problem, since the manipulated load is underactuated and its open-loop dynamics is inherently unstable. Thus, a stabilizing controller must be designed. To this end, a scheme that combines partial feedback linearization with local linearization of the remaining nonlinear terms is proposed. Such scheme simplifies the design of a variable structure controller, that has robustness characteristics to parametric uncertainties and matched disturbances. The proposed closed-loop control system has local stability properties. Small-scale experimental results in calm waters, and simulation results, illustrate the performance of the proposed control system.

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.

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.

For the way point-based path following control of under-actuated unmanned surface vehicle (USV), a new adaptive compensation line-of-sight (LOS) guidance law and speed solver are proposed, and the fuzzy heading control and integral S-plane speed control are designed. The path following control scheme is based on the separation control, and a speed control is added to improve the overall control structure. For adaptive compensation LOS guidance law, confirm the optimal circle radius of the adaptive LOS method first, and then the angle compensation of heading deviation of navigation is conducted for the LOS angle solved by adaptive LOS method, which is used to guide the USV to approach the desired path with the optimal improved LOS angle. Considering the rapidness of path following, a speed solver is designed according to the distance between the two path points, which can calculate the optimal desired speed of the USV in real time. The experimental results testify the effectiveness and superiority of the proposed guidance strategy, the speed solver and the control strategies.

In order to solve the problem of trajectory tracking control of unmanned surface vehicles (USV) with unknown speed information, an adaptive control algorithm based on Radial Basis Function (RBF) neural network and back-stepping method is proposed. This algorithm uses the back-stepping method to design an easy-to-implement control input based on the model parameters, uses the high-gain observer to estimate the speed information, and uses the RBF neural network to estimate the model parametric uncertainties and the environmental disturbances such as wind and wave. Then the control law and the weight update law of RBF neural network are designed. Finally, the systemic stability is proved by Lyapunov function. Simulational experiments and physical experiments verify the feasibility and effectiveness of this algorithm.

This study proposes a deep learning-based method for trajectory tracking control of Unmanned Surface Vehicle (USV). Trajectory tracking control is an effective approach for autonomous sailing, making the USV to track towards a desired route. Dual Deep Neural Networks (Dual-DNN) are presented in this study to evaluate and revise the traditional Line-of-Sight (LOS) guidance algorithm. In particular, One DNN is used to evaluate the sailing effect of USV, while the other DNN is used to estimate the cross-track distance and lateral distance of the guidance law. The experimental results demonstrate that by adopting the proposed Dual-DNN model, the trajectory tracking error is reduced by 5.3% and 21.7% compared to the Single-DNN model and the traditional LOS model, respectively. The magnitude and frequency of throttle and rudder manipulations have been reduced. The smooth curves from the actuators are more consistent with the regular mode of surface vehicle maneuvering.

In this article, the formation control of unmanned surface vehicles (USVs) is addressed considering actuator saturation and unknown nonlinear items. The algorithm can be divided into two parts, steering the leader USV to trace along the desired path and steering the follower USV to follow the leader in the desired formation. In the proposed formation control framework, a virtual USV is first constructed so that the leader USV can be guided to the desired path. To solve the input constraint problem, an auxiliary is introduced, and the adaptive fuzzy method is used to estimate unknown nonlinear items in the USV. To maintain the desired formation, the desired velocities of follower USVs are deduced using geometry and Lyapunov stability theories; the stability of the closed-loop system is also proved. Finally, the effectiveness of the proposed approach is demonstrated by the simulation and experimental results.

In this paper, the consensus control of unmanned surface vehicles (USVs) is investigated by employing a distributed model predictive control approach. A hierarchical control structure is considered during the controller design, where the upper layer determines the reference signals of USV velocities while the lower layer optimizes the control inputs of each USV. The main feature of this work is that a post-verification procedure is proposed to address the failure states caused by local errors or cyberattacks. Each USV compares the actual state and the predicted one obtained at the previous moment. This allows the estimation of local perturbations. In addition, the failure state of the USV can also be determined if a preset condition is satisfied, thus forcing a change in the communication topology and avoiding further impact. Simulations show that the proposed method is effective in USV formation control. Compared with the method without post-verification, the proposed approach is more robust when failure states occur.

This article addresses the path-guided flocking control of unmanned surface vehicles (USVs) suffering from fully unknown kinetics. A model-free learning and anti-disturbance control method is developed to achieve path-guided flocking without using prior knowledge of model nonlinearities, ocean disturbances, or control input gains. Specifically, data-driven concurrent learning extended state observers (CLESOs) based on fuzzy systems are presented to estimate the unknown kinetics of USVs. With the proposed CLESO, a model-free path-following control law is proposed for a leader USV to follow a parameterized path. Then, model-free flocking control laws based on potential functions are proposed for follower USVs to avoid collisions and maintain network links within available communication ranges. Through cascade stability analysis, the closed-loop system is proven to be globally asymptotically stable. Simulation results substantiate the proposed CLESO-based anti-disturbance control approach for path-guided flocking of a swarm of USVs.

In this paper, under parametric uncertainties and complex disturbances, a leader–follower formation control strategy based on accurate disturbance observer (ADO) and a novel fixed-time fast terminal sliding mode (FTFTSM) control for unmanned surface vehicles (USVs) is proposed. The main contributions of this paper are: (1) A novel fixed-time fast terminal sliding mode tracking control (FTFTSM-TC) strategy is designed for the tracking control subsystem, which greatly improves the convergence rate of the leader USV in trajectory tracking. (2) An ADO is designed to observe lumped disturbances with the smallest approximation error. The ADO greatly reduces the interference of disturbances and improves the performance of the formation system. (3) An ADO-based fixed-time formation control (ADO-FTFC) strategy is developed for the formation control subsystem to maintain the desired formation. Stability of the formation control system is established by the Lyapunov theory. Simulation results show that the proposed control strategy is superior for the USVs formation control.

This paper investigates the problem of cooperative formation control of unmanned surface vehicles (USVs). According to the second-order consensus protocol with a virtual-leader, a distributed control law is derived, in which the consensus formation motion information can only be obtained by a part of USVs. Taking advantages of the shared information between adjacent multi-USVs, the USVs can control self-motion trajectory in a concerted cooperation way, meanwhile, they can also follow the virtual-leader movement along a desired route. The error function is defined and the stability of the system is proved based on the Lyapunov stability principle. Simulation results are presented and discussed for validating the proposed control strategy.