Physics-Informed Neural Network Model for Trajectory Tracking Control of Unmanned Surface Vehicles

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.

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.

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.

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments.

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

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.

Event-triggered adaptive fuzzy trajectory tracking control of unmanned surface vehicle with time-varying output constraints and input quantization.

With respect to the accurate tracking control for unmanned surface vehicles (USVs), this paper proposes a novel hierarchical control structure consisting of a velocity planner and a tracking controller. With full consideration of control accuracy and tracking safety, a model predictive control planning method based on dynamic artificial potential field method (DAPF-MPC) is proposed, aiming to generate the current optimal reference tracking velocity and achieve online obstacle avoidance guidance. Simultaneously, a fixed-time generalized super-twisting controller based on an extended state observer (ESO-FiTGST) is proposed to restrict the convergence time and tackle with challenge resulting from the uncertain disturbances and model parameters. Noticeably, the design process of the proposed control law and its rigorous stability analysis, especially for the computable fixed-time convergence property, are detailed. Ultimately, the simulation and experimental results demonstrate the superiority and feasibility of the proposed method, providing a reliable and effective reference solution for tracking control tasks of USVs in aquatic scenarios with complex obstacles.

HighlightsWhat are the main findings?In response to the limited existing research on high-speed autonomous driving under low-adhesion, slippery road conditions, a novel MPC-PID fused trajectory tracking control algorithm is proposed. This approach significantly enhances tracking accuracy and vehicle stability under such complex scenarios.Joint simulation results demonstrate that the proposed method improves trajectory tracking accuracy by at least 22.2%, while comfort, fuel economy, and safety are improved by 9.4%, 19.8%, and 5.3%, respectively.What is the implication of the main finding?This work provides theoretical support and simulation validation for high-speed trajectory tracking control under low-adhesion conditions, offering high engineering applicability and practical significance.The proposed approach facilitates real-time, precise, and robust autonomous driving in dynamically complex environments, promoting the deployment of high-performance control strategies in real-world applications.Autonomous vehicle trajectory tracking control is one of the hot topics in the autonomous driving field. One of the most widely used control methods is MPC (Model Predictive Control). As the control system generally becomes more nonlinear and complex, more nonlinear system factors are added to the MPC method. However, tracking accuracy and the amount of calculation needed are both dependent on a lot of contradictions for NMPC (Nonlinear Model Predictive Control). This research proposes a control algorithm for MPC-fused PID (Proportional-Integral-Derivative) control that ensures tracking accuracy under different high-speed driving conditions on wet and slippery road surfaces. The objective of the algorithm is twofold: first, to enhance trajectory tracking accuracy, and second, to ensure real-time control and optimize the vehicle’s comfort, economy, and safety indexes. The results of the joint simulation in Carsim/MATLAB Simulink show that trajectory tracking accuracy is improved by at least 22.2% under high-speed driving conditions of a vehicle on a wet and slippery road. At the same time, the comfort, economy, and safety of the vehicle are improved by at least 9.4%, 19.8%, and 5.3%, respectively.

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.

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 paper discusses the course tracking control of unmanned surface vehicles with actuator input quantization and an event-triggered mechanism. The system control laws are designed based on the backstepping method, combining dynamic surface control technology to mitigate the computational complexity expansion of virtual control laws. A fuzzy logic system can be used to approximate the uncertainties in the control system. The control system’s control inputs are quantized by using uniform quantizers. Then, the event-triggered adaptive fuzzy quantization control method is introduced, which can reduce the frequency of control actions and effectively reduce the communication burden. The stability of the control system is rigorously proven using Lyapunov stability theory, ensuring that all signals in the closed-loop system remain bounded. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness.

This article focuses on the application issue of deep neural networks for the control strategy in unmanned surface vehicles. A novel adaptive law is developed to update the weights of the deep neural networks, thereby enhancing the control strategy’s ability to handle uncertain dynamics. We establish the relationship between the output of the deep neural networks and the dynamic response of the unmanned surface vehicle using the command filter method. And then, we develop a convex optimization-based adaptive approach to address the challenges of online learning within the deep neural network. We also prove the convergence of the signals in our controller using the Lyapunov stability theorem. Finally, simulation and experiment results are given to demonstrate the effectiveness of our proposed method.

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.