A survey on tactical control algorithms for path tracking unmanned surface vehicles

In order to solve the four degrees of freedom dynamic positioning control problem under the wave disturbance for UUV(Unmanned Underwater Vehicle), disturbance observer based backstepping sliding mode controller is proposed. Firstly, the four degrees of freedom dynamic positioning simplified motion mode is established, then the sliding mode control law is designed based on backstepping method. Secondly, the disturbance observer is adopted to estimate the wave disturbance online in order to have an equivalent compensation. At last, a thruster allocation method is proposed based on the thrusters of the researched UUV. The stability of the whole controller is proved, and simulation results show that the proposed control strategy can improve the control accuracy, and it also has robustness under the wave disturbance.

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

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 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.

Unmanned Surface Vehicles (USVs) have widespread commercial, scientific and military applications. In some of these applications station keeping is essential. Since USVs have a bigger surface/ weight ration than large commercial ships, they are more sensitive to wind disturbances, especially during station keeping. The modeling, identification and control of a 4.9 meter long, 180 kilogram wave adaptive modular vehicle (WAM-V) USV are presented in this paper. The propulsion system of the USV is custom designed with two azimuthing thrusters, making the USV fully actuated. System identification tests were conducted to get the precise model of the USV. A sliding mode controller was first applied to the USV and a wind feedforward controller was designed to aid the nonlinear feedback control to mitigate wind disturbance. For the feedforward controller design, a wind model was formulated that uses the apparent wind speed and direction collected from a high accuracy anemometer. A model based adaptive mechanism was developed to tune the feedforward control gains for the best performance. Station keeping field tests were conducted at a location with an average wind speed of 9 knots. The results show that with the tuned feedforward controller, the USV can hold its position and heading better over extended periods of time than a single feedback controller.

This paper focuses on motion analysis of a coupled unmanned surface vehicle (USV)–umbilical cable (UC)–unmanned underwater vehicle (UUV) system to investigate the interaction behavior between the vehicles and the UC in the ocean environment. For this, a new dynamic modeling method for investigating a multi-body dynamics system of this coupling system is employed. Firstly, the structure and hardware composition of the proposed system are presented. The USV and UUV are modeled as rigid-body vehicles, and the flexible UC is discretized using the catenary equation. In order to solve the nonlinear coupled dynamics of the vehicles and flexible UC, the fourth-order Runge–Kutta numerical method is implemented. In modeling the flexible UC dynamics, the shooting method is applied to solve a two-point boundary value problem of the catenary equation. The interaction between the UC and the USV–UUV system is investigated through numerical simulations in the time domain. Through the computer simulation, the behavior of the coupled USV–UC–UUV system is analyzed for three situations which can occur. In particular, variation of the UC forces and moments at the tow points and the configuration of the UC in the water are investigated.

A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

To solve the problem of path tracking control for search and rescue unmanned surface vehicle(USV), an adaptive path tracking control method based on line-of-sight(LOS) navigation algorithm and fuzzy PID control is proposed. Firstly, the motion model of USV is constructed. Secondly, the path tracking problem is transformed into a heading control problem according to the LOS algorithm. Then, a fuzzy adaptive PID path tracking controller based on error-driven is designed, which can achieve real-time optimization of PID parameters. Finally, the control effect of the fuzzy PID controller is verified by taking the expanded square search and parallel line search as examples. The simulation results show that the fuzzy PID controller has better adaptability and robustness than the classical PID controller. And it has better control effect under different paths.

With the development of the unmanned marine systems, autonomous recovery of Unmanned Underwater Vehicles(UUV) based on the Unmanned Surface Vehicles(USV) has become an important research field. Comparatively analyzing the current mainstream recovery methods, this paper proposes a new recovery scheme based on the previous research results. In this scheme, one end of the cable is connected to a winch fixed to the USV and the other is connected to the V-shaped wing, which can provide downward pressure to keep the line tight, and the rudders mounted on it can be adjusted to assure the attitude stability, which facilitate Docking and Line Capture Line Recovery(LCLR) of the UUV. After structure optimization, the V-shaped wing was modeled in hydrodynamic software STAR-CCM+. The numerical simulation shows that the improved V-shaped wing can provide about 20N downward pressure when the towing speed is 2 knots. In order to improve the reliability of the LCLR system and reduce mechanism sticking, a new type of capture mechanism is designed and it is driven by two motor respectively instead of just one. Besides, the LED beacon ranging tests were conducted in the Xiu Lake. It shows the accuracy of optical guidance algorithm is 2%, and the maximum optical recognition distance is 16m in the air and 10m in the clean water, which satisfies the docking accuracy. From October 2018, two Lake tests were carried out in Xiu Lake in Shenyang and Qiandao Lake in Hangzhou. The performance of the LCLR system was tested, and the V-shaped wing was towed at different speeds range from 1 to 1.5 knots and the length of the cable is 8m. The results show the V-shaped wing can maintain depth and attitude stability. Finally, through the improved acousto-optic joint guidance algorithm, the docking process is successfully completed and the Towed Line and Cable were captured by the UUV line capture mechanism on nose. The feasibility of the presented recovery scheme was indicated by the results from the two trials, which has a certain guiding significance for the dynamic recovery of an UUV in the future.

Designing the NGC system of a small ASV for tracking underwater targets

This paper describes a study on the dynamic modeling and the motion simulation of an unmanned ocean platform to overcome the limitations of existing unmanned ocean platforms for ocean exploration. The proposed unmanned ocean vehicle combines an unmanned surface vehicle and unmanned underwater vehicle with an underwater cable. This platform is connected by underwater cable, and the forces generated in each platform can influence each other’s dynamic motion. Therefore, before developing and operating an unmanned ocean platform, it is necessary to derive a dynamic equation and analyze dynamic behavior using it. In this paper, Newton’s second law and lumped-mass method are used to derive the equations of motion of unmanned surface vehicle, unmanned underwater vehicle, and underwater cable. As the underwater cable among the components of the unmanned ocean platform is expected to affect the motion of unmanned surface vehicle and unmanned underwater vehicle, the similarity of modeling is described by comparing with the cable modeling results and the experimental data. Finally, we constructed a dynamic simulator using Matlab and Simulink, and analyzed the dynamic behavior of the unmanned ocean platform through open-loop simulation.

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.

Advances in robotic mapping, planning, and perception have spurred applications-based robotics research in the domain of infrastructure inspection and preservation. Though a significant portion of this research has centered around the use of unmanned aerial, ground, and underwater vehicles, research in the use of unmanned surface vehicles (USVs) is limited. USVs present a unique opportunity to capture combined maps above and below water, which is essential for the inspection of waterspanning bridges, harbors, dams, and levees. In this paper, we investigate the use of USVs for infrastructure inspection by outfitting a USV platform with a multibeam sonar, horizontally and vertically mounted lidars, several ruggedized RGB cameras, and a high-rate inertial measurement unit (IMU). By time-synchronizing all sensors, we are able to fuse information collected from lidar, camera, and IMU units via tightly-coupled lidar-visual-inertial (LVI) simultaneous mapping and localization (SLAM). We validate our methodology by collecting sensory data of an abandoned quarry and by generating a combined 3D point cloud map using lidar data, multibeam sonar data, and maximum a posteriori trajectory from the LVI SLAM approach. Experiments validate the performance of the proposed USV system, highlighting challenges in extrinsic calibration of non-overlapping sensors, sonar denoising, and refined inter-keyframe pose estimation for key-frame based SLAM approaches.

The path following of an underactuated unmanned surface vessel (USV) in a complex marine environment requires minimizing both the track error and yaw angular velocity, whereas the underactuated characteristics of USV and variability of model parameters need to be considered. To achieve this, the problem of path tracking is modelled using a first-order Nomoto model in the Serret-Frenet coordinate system. An efficient algebraic model predictive control (AMPC) approach is employed to determine the optimal control law. This can significantly reduce the number of prediction points in traditional predictive control, simplify the computation of online optimization, and contribute to the implementation of robust control for USV path following. The results of simulation demonstrate that the developed controller achieves asymptotic stability with respect to the track error deviation, heading angle deviation, and heading angle speed. The performance of the controller in terms of good stability and robustness is also verified.

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.