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It is common that underactuated surface vessels sailing on the sea suffer from strong external sea disturbances, such that the large roll motion can be probably caused resulting to the bad performance of path following. In order to realize the coordinate control of the rudder roll stabilization and path following, a robust controller with roll cons...
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The paper presents two variants of a Krylov-Simplex iterative method that combines Krylov and simplex iterations to minimize the residual $r = b-Ax$. The first method minimizes $\|r\|_\infty$, i.e. maximum of the absolute residuals. The second minimizes $\|r\|_1$, and finds the solution with the least absolute residuals. Both methods search for an...
Citations
... Straight-line path-following was achieved by defining virtual target points along the desired path and designing corresponding algorithms for both their motion and that of the USV. Owing to the influence of environmental factors, such as wind, waves, and currents, disturbance 2 of 19 observers [7,8] and neural networks [9,10] have been used to improve the anti-interference performance of the control system in most studies. Additionally, the authors of [11][12][13] further considered input-saturation issues. ...
The control problem of avoidance-path-following is a critical consideration in the research of unmanned surface vehicle (USV) navigation control, and it holds great significance for the navigation safety of USVs. A guidance and control scheme based on finite-distance convergence is proposed in this paper. First, the requirements for the USV to avoid obstacles from the perspective of path-following lateral error are analyzed. Then, a new performance function with finite-distance convergence is proposed to constrain the lateral error. Based on this, a heading guidance law and a backstepping controller are designed to ensure that the lateral error converges to a steady-state value within the prescribed navigation distance and that the stability is maintained, satisfying the requirements of obstacle avoidance for the USV. In addition, an adaptive velocity command is designed to adjust the velocity with the lateral error, which, to a certain extent, avoids the saturation of the heading actuator caused by the large lateral error. Finally, it is proven through theory and simulation that the control algorithm can guide the USV to achieve avoidance-path-following within a limited distance and to avoid obstacles effectively.
Path following is one of the indispensable tools for autonomous ships, which ensures that autonomous ships are sufficiently capable of navigating in specified collision-free waters. This study proposes a novel path following approach for autonomous ships based on the fast marching (FM) method and deep reinforcement learning (DRL). The proposed approach is capable of controlling a ship to follow different paths and ensuring that the path tracking errors are always within a set range. With the help of the FM method, a grid-based path deviation map is specially produced to indicate the minimum distance between grid points and the path. Besides, a path deviation perceptron is specifically designed to simulate a range sensor for sensing the set path deviation boundaries based on the path deviation map. Afterwards, an agent is trained to control a ship following a circular path based on the DRL. Particularly, the approach is validated and evaluated through simulations. The obtained results show that the proposed method is always capable of maintaining high overall efficiency with the same strategy to follow different paths. Moreover, the ability of this approach exhibits a significant contribution to the development of autonomous ships.
This paper studies close formation control problem with prescribed performance and time-varying state constraints for a group of 4-degrees-of-freedom (DOF) underactuated surface vehicles (USVs) subject to actuator faults, input saturation and input delay. A finite-time sliding mode control (SMC) scheme based on reinforcement learning (RL) algorithm is introduced to guarantee prescribed formation performance without violating velocity error constraints. By using actor-critic neural network (NN)-based RL algorithm, the actuator faults and system uncertainties are accurately estimated. Afterwards, an exponential decreasing boundary function is developed to suppress overshoot more reasonably, and a novel mechanism of switching gain is given to alleviate chattering inherent in SMC while the RL-based compensation term is constructed to handle the formation accuracy problem caused by the reduced switching gain. Besides, auxiliary nonlinear continuous function and Pade approximation have been successfully applied to process actuator saturation and input delay, respectively. Numerical simulations and experimental results are exhibited to verify the effectiveness and superior formation performance of the proposed control method.
