Control Of Sailboat Research Articles

V-Stability Based Control for Energy-Saving Towards Long Range Sailing

Ensuring an adequate supply of energy is a challenge in the development of autonomous sailboats for long-range sailing capability, as the continuous high-frequency execution of control commands leads to high energy consumption. However, as the control of sailboat is much more complex than the other USVs, especially in a complicated and windy environment, simply reducing control frequency will lead to large path tracking error, and even unsuccessful navigation. Based on the V-stability controller of sailboats, this letter proposes an energy-saving (E-saving) control scheme for a sailboat, to enable the energy planning of actuators. While keeping the sailboat traveling within the acceptable range along a desired path, we develop an approach to analyze and reach a satisfying tradeoff between path tracking error and consumed energy. The E-saving method is examined in simulated environments and field experiments using an autonomous sailboat (OceanVoy). The results show that E-saving method reduces energy consumption by approximately 11% compared to that of the previous V-stability controller, indicating that our method can elongate sailing distance.

A Kelvin Wake Avoidance Scheme for Autonomous Sailing Robots Based on Orientation-Restricted Dubins Path

Sailboats have a wide range of applications on account of their energy-saving, environmentally benign and low-noise characteristics. This has led to the increasing popularity of research in autonomous sailing robots, with concomitant attention paid to the development of methods that ensure their safety during sailing. Sailboats are affected by obstacles and by nonlinear aerodynamic and hydrodynamic factors. In particular, sailboats are affected by the wake generated by a moving vessel, which in this letter is denoted as “moving boat”. Wakes are swift and can have severe adverse effects on a sailboat by causing it to veer off course, stall or capsize. Moreover, sailboats have low mobility and large inertia and will therefore struggle to avoid a wake. Thus, in this letter, we describe our efforts to develop a method to enable a sailboat to perform wake avoidance. We model the wake as a Kelvin wake and generate waypoints for an orientation-restricted Dubins path-based Kelvin wake-avoidance method. A V-stability-based control method has proven suitable for generating the path, after which the desired heading angle for sailboat control is obtained. The resulting wake-avoidance method is verified in simulations and experiments. It enables the sailboat to avoid alongside and toward wakes, and the qualitative results have revealed that the maximum range of roll in alongside scenario and maximum range of surge acceleration in toward scenario are reduced by 57.4% and 23.4%, respectively, relative to a situation when no wake-avoidance method was used. The maximum range of roll in toward scenario is acceptable.

Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network

Wind speed is a key factor for unmanned sailboats, and accurate prediction of wind speed is of great significance to the safety and performance of unmanned sailboats. In this study, a novel hybrid neural network scheme based on convolutional neural network (CNN) and long short-term memory (LSTM) is proposed for multi-step wind speed prediction. The scheme consists of two parts: a data processing module and a model module. We improved the grid search method to determine the selection of learning rate and input length hyperparameters. Simulations were performed on three different data sets and four types of other benchmark models were developed for comparison with the CNN-LSTM, such as recurrent neural network (RNN) and LSTM model, etc. The forecasts are evaluated by looking at the mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (CC) and R squared (R2). The evaluation metrics showed that the MAE and RMSE of CNN-LSTM are lower than the other benchmark models most of the time, while both CC and R2 are higher than the other models, which means the CNN-LSTM performs better accuracy and stability. It is accurate enough to provide a reliable wind input to the unmanned sailboat control system.

Event-triggered composite adaptive fuzzy control of sailboat with heeling constraint

In this paper, the event-triggered composite adaptive fuzzy control laws are developed to control both the surge speed and the heading angle of the sailboat. The fuzzy logic systems (FLSs) are employed to approximate the nonlinearities in the model of the sailboat. To enhance the approximating accuracy, a serial–parallel estimation model is established at first, and the composite adaptive laws of the FLS weights are then derived by combining the tracking errors with the prediction errors between the estimation model and the dynamic loop of the sailboat. To prevent the sailboat from capsizing, we consider the heeling constraint in the roll motion during the voyage. By using the backstepping method, the heeling constraint is transformed to the input saturation of the sail. The auxiliary systems are fabricated to offset the input saturation of the sail and the rudder, which are incorporated into the control laws. To reduce the acting frequencies of the actuators, the control laws for the sail and the rudder are designed in the event-triggered form, and the triggering conditions are constructed separately. The uncertain gain of the rudder is estimated by the adaptive laws. The proposed control scheme can guarantee the semi-global uniformly ultimate boundedness (SGUUB) of all the errors. Through the numerical path-following test, the effectiveness of the proposed scheme is verified.

Data-Driven Online Speed Optimization in Autonomous Sailboats

This paper addresses the issue of data-driven online velocity optimization of an autonomous sailboat. Autonomous sailboats represent an ideal for long range and duration reconnaissance missions. Sailboat control is a challenging control task; sailboats are characterized by a number of control variables, all of which affect the ship trajectory and state in a highly nonlinear fashion. In this paper, a path-following automatic sailboat controller is presented. The control system has two main components: a heading control, acting on the rudder, and a velocity optimizer, acting on the sails. The optimizer is based on a modified extremum seeking approach. This paper also derives a first-principle-based 4 DoF sailboat model that is experimentally validated and used to guide the design and tuning of the control system. In fact, the control system is first tuned and validated in simulation. The simulation environment enables the comparison of the proposed model against a theoretical benchmark and a state-of-the-art controller. The analysis reveals that the proposed control system achieves near-optimal performance and considerably outperforms the state-of-the-art solution. Finally, the controller is tested and validated on an instrumented scale model.