Search Results (8)

Unmanned surface vehicles (USVs) course control research constitutes a vital branch of ship motion control studies and serves as a key technology for the development of marine critical equipment. Aiming at the problems of model uncertainties, external marine disturbances, performance optimization, and actuator constraints encountered by the autopilot system, this paper proposes a composite disturbance cancellation optimized control method based on fuzzy reinforcement learning. Firstly, a coupling design of the finite-time disturbance observer and fuzzy logic system is conducted to estimate and reject the composite disturbance composed of internal model uncertainty and ocean disturbances. Secondly, a modified backstepping control technique is employed to design the autopilot controller and construct the error system. Based on the designed performance index function, the fuzzy reinforcement learning is utilized to propose an optimized compensation term for the error system. Meanwhile, to address the actuator saturation issue, an auxiliary system is introduced to modify the error surface, reducing the impact of saturation on the system. Finally, the stability of the autopilot system is proved using the Lyapunov stability theory. Simulation studies conducted on the ocean-going training ship “Yulong” demonstrate the effectiveness of the proposed algorithm. Under the strong and weak ocean conditions designed, this algorithm can ensure that the tracking error converges within 7 s. Full article

Aiming at the uncertainty problem caused by the time-varying modeling parameters associated with ship speed in the course tracking control of underactuated surface vessels (USVs), this paper proposes a control algorithm based on the dynamic neural fuzzy model (DNFM). The DNFM simultaneously adjusts the structure and parameters during learning and fully approximates the inverse dynamics of ships. Online identification and modeling lays the model foundation for ship motion control. The trained DNFM, serving as an inverse controller, is connected in parallel with the fractional-order PI^λD^μ controller to be used for the tracking control of the ship’s course. Moreover, the weights of the model can be further adjusted during the course tracking. Taking the actual ship data of a 5446 TEU large container ship, simulation experiments are conducted, respectively, for course tracking, course tracking under wind and wave interferences, and comparison with five different controllers. This proposed controller can overcome the influence of the uncertainty of modeling parameters, tracking the desired course quickly and effectively. Full article

An overall framework of the virtual testing system has been established based on the analysis of the virtual testing requirements for autonomous navigation performance of unmanned surface vehicles (USVs). This system consists of several modules, including the environment module, motion module, sensor module, and 3D visualization module. Firstly, within the robot operating system (ROS) environment, a three-dimensional navigation environment was generated by combining actual wave spectra with Gerstner waves. By designing a power plugin for USV navigation, the system was made to reflects the coupled motion model of USVs in wind, waves and currents, along with predictive results. Regarding the four typical sensor information on USVs, the actual sensors were virtualized, and a simulation approach for virtual sensor information is provided. The three-dimensional visualization of USV’s motion enables the intuitive display and analysis of the virtual testing process. Based on the prediction of coupled motion characteristics in wind, waves and currents, the interaction between USVs and the virtual testing system has been realized. A platform for virtual testing experiments to determine the autonomous navigation performance of USVs was established, and the effectiveness of the platform was verified in terms of perception and environmental interference. In virtual environmental interference validation, the average amplitude deviation of the heave motion of USVs under sea state 3 reaches 0.74 m, and the average amplitude deviation of the pitch motion reaches 0.25 rad, showing the gradually increasing disturbance of the sea state. Finally, virtual testing experiments were conducted on a specific USV to evaluate its autonomous navigation perception performance, trajectory tracking performance, and autonomous obstacle avoidance. The evaluation results indicate that the platform can achieve the functionality of virtual testing for the autonomous navigation performance of USVs from the perspective of cost function, taking the reaction distance, regression distance, and obstacle avoidance time into consideration. A representative example is that the cost function deviation rates of overtaking obstacle avoidance between static and dynamic seas reach 5.11%, 8.98% and 18.43%, respectively. The gradually increasing data shows that the virtual simulating method matches the drifting-off-course tendency of boats in rough seas. This includes acquiring perception information of navigation and simulating the motion and navigation processes for visualization. The platform provides new means for testing and evaluating the autonomous navigation performance of USVs. Full article

In this paper, we take a water quality testing Unmanned Surface Vessel (USV) as the research object. We propose a heading keeping strategy based on Human Simulated Intelligent Control (HSIC) algorithm and a trajectory tracking strategy under line-of-sight (LOS) algorithm. The practicality of the proposed control strategies was verified by combining simulations and experiments. The main contents were constructed with three parts: Firstly, we designed a complete control system of a water quality inspection unmanned boat with Arduino microcontroller as the core processor. Secondly, we derived the mathematical model of motion after reasonable simplification. Combined with the cycle experiment, the mapping relation between virtual rudder angle and motor speed was established. Then, the USV heading direction control strategy of HSIC was presented and the reliability of the proposed strategy was verified by the course control experiment of USV. Finally, aiming at the defects and shortcomings of the upper-level trajectory tracking LOS algorithm in practical application, we propose the trajectory correction and precise steering control strategies, and the practicality of the improved algorithm was verified by multi-point trajectory tracking experiments. The autonomous fixed-point water quality testing experiment was designed and verified the effectiveness of the proposed strategies. Full article

This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration. Full article

Trajectory tracking is a basis of motion control for Unmanned Surface Vehicles (USVs), which has been researched well for common USVs. The twin-propeller and twin-hull USV (TPTH-USV) is a special vehicle for applications due to its good stability and high load. We propose a three-layered architecture of trajectory tracking for the TPTH-USV which explicitly decomposes into trajectory guidance, a motion limitator and controller. The trajectory guidance transforms an expected trajectory into an expected speed and expected course in a kinematic layer. The motion limitator describes some restriction for motion features of the USV in the restriction layer, such as the maximum speed and maximum yaw rate. The controller is to control the speed and course of the USV in the kinetic layer. In the first layer, an adaptive line-of-sight guidance law is designed by regulating the speed and course to track a curved line considering the sideslip angle. In the second layer, the motion features are extracted from an identified speed and course coupled model. In the last layer, the course and speed controller are designed based on a twin-PID controller. The feasibility and practicability of the proposed trajectory tracking scheme is validated in sea experiments by a USV called ‘Jiuhang 490’. Full article

Unmanned Surface Vehicles (USV) are increasingly used to perform numerous tasks connected with measurements in inland waters and seas. One of such target applications is hydrography, where traditional (manned) bathymetric measurements are increasingly often realized by unmanned surface vehicles. This pertains especially to restricted or hardly navigable waters, in which execution of hydrographic surveys with the use of USVs requires precise maneuvering. Bathymetric measurements should be realized in a way that makes it possible to determine the waterbody’s depth as precisely as possible, and this requires high-precision in navigating along planned sounding profiles. This paper presents research that aimed to determine the accuracy of unmanned surface vehicle steering in autonomous mode (with a Proportional-Integral-Derivative (PID) controller) along planned hydrographic profiles. During the measurements, a high-precision Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning system based on a GNSS reference station network (positioning accuracy: 1–2 cm, p = 0.95) and a magnetic compass with the stability of course maintenance of 1°–3° Root Mean Square (RMS) were used. For the purpose of evaluating the accuracy of the vessel’s path following along sounding profiles, the cross track error (XTE) measure, i.e., the distance between an USV’s position and the hydrographic profile, calculated transversely to the course, was proposed. The tests were compared with earlier measurements taken by other unmanned surface vehicles, which followed the exact same profiles with the use of much simpler and low-cost multi-GNSS receiver (positioning accuracy: 2–2.5 m or better, p = 0.50), supported with a Fluxgate magnetic compass with a high course measurement accuracy of 0.3° (p = 0.50 at 30 m/s). The research has shown that despite the considerable difference in the positioning accuracy of both devices and incomparably different costs of both solutions, the authors proved that the use of the GNSS RTK positioning system, as opposed to a multi-GNSS system supported with a Fluxgate magnetic compass, influences the precision of USV following sounding profiles to an insignificant extent. Full article

The performance of bathymetric measurements by traditional methods (using manned vessels) in ultra-shallow waters, i.e., lakes, rivers, and sea beaches with a depth of less than 1 m, is often difficult or, in many cases, impossible due to problems related to safe vessel maneuvering. For this reason, the use of shallow draft hydrographic Unmanned Surface Vessels (USV) appears to provide a promising alternative method for performing such bathymetric measurements. This article describes the modernisation of a USV to switch from manual to automatic mode, and presents a preliminary study aimed at assessing the suitability of a popular autopilot commonly used in Unmanned Aerial Vehicles (UAV), and a low-cost multi-Global Navigation Satellite System (GNSS) receiver cooperating with it, for performing bathymetric measurements in automated mode, which involves independent movement along a specified route (hydrographic sounding profiles). The cross track error (XTE) variable, i.e., the distance determined between a USV’s position and the sounding profile, measured transversely to the course, was adopted as the measure of automatic control precision. Moreover, the XTE value was statistically assessed in the publication. Full article