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Guidance And Control Of Ocean Vehicles

AIAA Guidance, Navigation, and Control Conference

Ship collision avoidance methods : State-of-the-art

Deep reinforcement learning-based controller for path following of an unmanned surface vehicle

Although maritime transport is the backbone of world commerce, its digitalization lags significantly behind when we consider some basic facts. This work verifies the state-of-the-art as it currently applies to eight digital domains: Autonomous vehicles and robotics; artificial intelligence; big data; virtual reality, augmented and mixed reality; internet of things; the cloud and edge computing; digital security; and 3D printing and additive engineering. It also provides insight into each of the three sectors into which this industry has been divided: Ship design and shipbuilding; shipping; and ports. The work, based on a systematic literature review, demonstrates that there are domains on which almost no formal study has been done thus far and concludes that there are major areas that require attention in terms of research. It also illustrates the increasing interest on the subject, arising from the necessity of raising the maritime transport industry to the same level of digitalization as other industries.

https://www.mdpi.com/1424-8220/19/4/926/pdf

Toward Digitalization of Maritime Transport

In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control and backstepping technique. First, the radial basis function neural network with minimum learning parameter method (MLP) is constructed to online approximate the uncertain system dynamics, which uses single parameter instead of all weights online learning, leading to a reduction in the computational burdens. Then a hyperbolic tangent function is adopted to reduce the chattering phenomenon due to the sliding mode surface. Meanwhile, the auxiliary dynamic system and the adaptive technology are employed to handle input saturation and unknown disturbances, respectively. In addition, a neural shunting model is introduced to eliminate the “explosion of complexity” problem caused by the backstepping method for virtual control derivation. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulations are provided to validate the effectiveness of the proposed control scheme.

Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

This paper addresses three related issues concerning the path following control of a podded propulsion unmanned surface vehicle (USV), namely modeling, guidance and control. The pod is different from the general propeller-rudder propulsion device, and its essence is a vector thruster. Therefore, first, through various assumptions and simplification, the three-degree of freedom (DOFs) planar motion model of the podded propulsion USV is established. Then, the classical line-of-sight (LOS) guidance strategy is improved by adaptive sideslip angle and a time-varying lookahead distance. Based on the guidance system, the corresponding controllers for yaw rate and surge speed are presented, which are combined by backstepping technology, the neural network minimum parameter learning method and the neural shunting model. Specifically, the neural network minimum parameter learning method is proposed to compensate the uncertainty of the model and the immeasurability of external disturbances, and the neural shunting model is employed to cope with the “explosion of complexity” problem of backstepping. Meanwhile, an auxiliary dynamic system is introduced to prevent actuator saturation (input saturation). All error signals of the system are proven to be uniformly ultimately bounded (UUB) by employing Lyapunov stability theory. Finally, two numerical simulations are given to prove the correctness of the proposed scheme.

https://www.mdpi.com/2076-3417/7/12/1232/pdf

Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation

The response model of podded propulsion unmanned surface vehicle (USV) is established and identified; then considering the USV has characteristic of high speed, the course controller with fast convergence speed is proposed. The idea of MMG separate modeling is used to establish three-DOF planar motion model of the podded propulsion USV, and then the model is simplified as a response model. Then based on field experiments, the parameters of the response model are obtained by the method of system identification. Unlike ordinary ships, USV has the advantages of fast speed and small size, so the controller needs fast convergence speed and strong robustness. Based on the theory of multimode control, a fast nonsingular terminal sliding mode (FNTSM) course controller is proposed. In order to reduce the chattering of system, disturbance observer is used to compensate the disturbance to reduce the control gain and RBF neural network is applied to approximate the symbolic function. At the same time, fuzzy algorithm is employed to realize the mode soft switching, which avoids the unnecessary chattering when the mode is switched. Finally the rapidity and robustness of the proposed control approach are demonstrated by simulations and comparison studies.

/pdf/modeling-and-identification-of-podded-propulsion-unmanned-4s45arkz5k.pdf

Modeling and Identification of Podded Propulsion Unmanned Surface Vehicle and Its Course Control Research

In this paper, a response model of an Unmanned Surface Vehicle (USV) with a pod-like propulsion device is established. To improve the robustness of motion control in heavy sea states, an integrated nonlinear feedback course-keeping controller is proposed. First, to establish a response model of a USV with pod-like propulsion, model parameters are obtained by the method of system identification, then an integrated nonlinear feedback control strategy is proposed. The essence of this method is to make the original error signal pass through a nonlinear function, and then the output of this function is used to replace the original error signal. Simulation results show that under ordinary sea states, nonlinear feedback can save up to 34.5% of energy used compared with standard feedback methods; under heavy sea states, this can rise to 40.8%. A set of field experiments were carried out with a USV with pod-like propulsion. Results show that under heavy sea states, the test USV can maintain the target course well, which proves the correctness of the model and the robustness of the proposed method.

Course keeping Control Based on Integrated Nonlinear Feedback for a USV with Pod-like Propulsion

By deploying unmanned surface vehicles (USVs), the efficiency and reliability of mission execution can be improved. This paper is concerned with the problem of formation autonomous navigation system (FANS) for USVs. The decision layer of the system is composed of path planning subsystem and navigation control subsystem to realize fast and effective autonomous navigation system. The FANS is constructed by leader-follower structure and distributed control strategy, which make the individuals in the formation have certain autonomy. The dynamic domain tunable fast marching square algorithm proposed in the path planning subsystem can not only adjust the safety and length of the path planned, but also continuously re-plan the path according to the motion information of the USV formation and target-vessels. Navigation control subsystem based on finite control set model predictive control can quickly and safely guide and control formation in local sea environment. The simulation tests are carried out in static and dynamic harbor environments respectively, which verifies the ability of the FANS to achieve stable formation tracking and autonomous safe navigation.

Dongdong Mu

Papers

Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation

Modeling and Identification of Podded Propulsion Unmanned Surface Vehicle and Its Course Control Research

Course keeping Control Based on Integrated Nonlinear Feedback for a USV with Pod-like Propulsion

A Formation Autonomous Navigation System for Unmanned Surface Vehicles With Distributed Control Strategy