CONTROLLING THE PARAMETRIC ROLL OF A CONTAINER SHIP MODEL BY CHANGING THE HEADING ANGLE

Parametric roll on ships is an auto-parametric resonance phenomenon whose onset causes a sudden rise in roll oscillations leading to dangerous situations for the ship, the cargo and the crew. In the paper, we have numerically investigated the effect of modifying the heading angle on the roll amplitudes. We followed three strategies. In the first, we allowed the heading angle to decrease with a constant angular acceleration so that the encounter frequency has left the dangerous region of the resonance. However, this option involves changing the course of the ship in the long run, which is of course a shortcoming. In the second strategy, we changed the heading angle up and down around an average value that generates large roll amplitudes, by using different periodic sinusoidal or triangular profiles. The beneficial effect of this action is to keep the course, even if at the cost of a momentary delay. We noticed that both control techniques listed above generally managed to significantly reduce the roll amplitudes if certain thresholds have been exceeded. As a last idea for decreasing the parametric roll amplitudes, we used the combined effect of ship forward speed and heading angle change.

Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The proposed method extracts lines from images using a line filter and a Hough transform, and it uses a convolutional neural network to robustly extract parking lines from the environment. In addition, a parking path consisting of curved and straight sections is created and used to control the vehicle. Perpendicular, angle, and parallel parking paths can be created; however, parking control is applied according to the shape of each parking slot. The results of our experiments confirm that the proposed method has an average offset of 10.3 cm and an average heading angle error of 0.94°.

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.

Output-Based Tracking Control for a Class of Car-like Mobile Robot Subject to Slipping and Skidding Using Event-Triggered Mechanism

This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as well as to estimate the uncertainties and disturbances. The constrained longitudinal velocity is established, conforming to the traffic flow theory on the kinematic level. The velocity control law and heading angle control law are developed on the dynamic level, respectively. The input to state stability (ISS) of the closed-loop system is analyzed via cascade theory. Simulation results are given to demonstrate the effectiveness of the proposed tracking controller for CLMR subject to slipping and skidding.

Seakeeping index as generalized indicator of ship seakeeping performance

Object and purpose of research. This paper discusses the possibility of a ship design process that would consider seakeeping performance to the greatest extent possible. The purpose of this study was to work out a numerical indicator, an index, reflecting all the seakeeping properties relevant for suitability of given ship to its intended operational conditions. Materials and methods. The study was based on the data about various operational parameters of the ship under investigation. These data were further synthesized so as to obtain the most comprehensive picture of ship seakeeping behaviour in different operational conditions. Main results. The study yielded the method and the algorithm for the “seakeeping index” as an average annual probability that seakeeping performance of given ship will be adequate to the conditions of given water area. The method sug-gested in this paper for a generalized comparison of seakeeping properties can handle whatever variety of target parameters and whatever seakeeping standards for any kind of ship intended to operate in given water area, and the result of this comparison is given in form of a single number that can be further used to improve seakeeping parameters of given ship, as well as to estimate possible time of its fully-featured operation in given conditions, including cost efficiency analysis. Conclusion. For more accurate comparison, it is recommended to analyse target parameters as functions of both ship speed and wave heading angle keeping in mind that the assumption introduced, i.e. that these curves as functions of wave heading angle are cosines, is not necessarily true. In other words, it is recommended to rely on more accurate data, experimental or analytical, so as to take into account the effect of apparent frequencies upon these curves.

Iterative Self-Tuning Minimum Variance Control of a Nonlinear Autonomous Underwater Vehicle Maneuvering Model

This paper addresses the problem of control design for a nonlinear maneuvering model of an autonomous underwater vehicle. The control algorithm is based on an iteration technique that approximates the original nonlinear model by a sequence of linear time-varying equations equivalent to the original nonlinear problem and a self-tuning control method so that the controller is designed at each time point on the interval for trajectory tracking and heading angle control. This work makes use of self-tuning minimum variance principles. The benefit of this approach is that the nonlinearities and couplings of the system are preserved, unlike in the cases of control design based on linearized systems, reducing in this manner the uncertainty in the model and increasing the robustness of the controller. The simulations here presented use a torpedo-shaped underwater vehicle model and show the good performance of the controller and accurate tracking for certain maneuvering cases.

Clothoid curve optimization for parallel parking path planning and tracking control

In order to solve the problem of abrupt curvature change at the connection between arcs and straight lines in circle-line-circle (C-L-C) combined parallel parking paths, curvature optimization was carried out by using a cycloid curve, and the trajectory curvature and heading Angle were made to meet the pose requirements of parallel parking. By establishing the kinematics model state equation and taking the optimized C-L-C trajectory as the reference trajectory, the error model was obtained. The model predictive controller based on the error model was designed, and the effectiveness of the path planning and model predictive controller was verified on Carsim and Matlab/Simulink co-simulation platform.

CFD Prediction of Ship Seakeeping and Slamming Behaviors of a Trimaran in Oblique Regular Waves

The main hull encounters waves at first and causes waves to break, when trimarans are subject to the slamming in head waves. At this moment, emergence phenomena of side hulls will not occur. Thus, the slamming study of trimarans in oblique waves presents further practical significance. In this study, a CFD method is used for trimaran seakeeping and slamming analysis. An overset grid technique is adopted to simulate ship motions in waves. Firstly, to further verify the present method, a series of verification and validation studies is conducted. Then, the motion responses and slamming pressure with different control parameters, such as forward speed and ship heading angle, are calculated and discussed. The comparative results indicate that the seakeeping and slamming behaviors of trimarans differ significantly from those of conventional monohull ships. Finally, severe bow slamming and green water in oblique waves are also observed and investigated, which should be given enough attention during ship design and evaluation.

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

In the process of ship navigation, due to the characteristics of large inertia and large time delay, overshoot can easily occur in the process of path following. Once the ship deviates from the waypoint, it is prone to grounding and collision. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. The desired heading angle is designed according to a backstepping algorithm. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The second-order Taylor expansion is used to predict the future position, the current error and future error functions are constructed, and the total errors are fed back to the desired heading angle. In the sliding mode control system, the RBF neural network is used to approximate the total unknown term, and a velocity observer is introduced to obtain the surge velocity and sway velocity. To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. The simulation results show the effectiveness and superiority of the designed controller. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.