Unmanned surface vehicles: An overview of developments and challenges

This paper describes planar motion modeling for an unmanned surface vehicle (USV), including a comparative evaluation of several experimentally identified models over a wide range of speeds and planing conditions. The modeling and identification objective is to determine a model that is sufficiently rich to enable effective model-based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hullforms and actuator configurations. We focus, however, on a specific platform: a modified rigid hull inflatable boat with automated throttle and steering. Analysis of experimental results for this vessel indicates that Nomoto's first-order steering model provides the best compromise between simplicity and fidelity at higher speeds. At low speeds, it is helpful to include a first-order lag model for sideslip. Accordingly, we adopt a multiple model approach in which the model structure and parameter values are scheduled based on the nominal forward speed. The speed-scheduled planar motion model may be used to generate dynamically feasible trajectories and to develop trajectory tracking control laws. The paper describes the development, analysis, and experimental implementation of two trajectory tracking control algorithms: a cascade of proportional-derivative controllers and a nonlinear controller obtained through backstepping. Experimental results indicate that the backstepping controller is much more effective at tracking trajectories with highly variable speed and course angle. © 2013 Wiley Periodicals, Inc.

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Modeling, Identification, and Control of an Unmanned Surface Vehicle

An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests

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

This paper proposes a path-following control method for an unmanned surface vessel (USV) based on an identified dynamic model. To handle the USV dynamic model effectively, a three degree-of-freedom model is employed instead of a full nonlinear dynamic model and linearized at specific equilibrium condition. The linearized model is identified with real data from several experiments by utilizing a particle swarm optimization method. Then, based on the identified model, an adaptive control algorithm is proposed to follow several waypoints and velocity command. The proposed control method utilizes virtual control input, dynamic surface control method, and adaptive terms to handle matched and unmatched uncertainties simultaneously. The overall closed-loop stability is analyzed by introducing deadzone errors composed of tracking error and saturation function. Finally, some experiment with a remodeled commercial fishing boat are conducted and analyzed to validate the performance of the proposed methods.

Adaptive Path-Following Control for an Unmanned Surface Vessel Using an Identified Dynamic Model

System identification for nonlinear maneuvering of large tankers using artificial neural network

This paper deals with the application of nonparametric system identification to the nonlinear maneuvering of ships using neural network method. The maneuvering equations contain linear as well as nonlinear terms, and one does not attempt to determine the parameters (or hydrodynamic derivatives) associated with nonlinear terms, rather all nonlinear terms are clubbed together to form one unknown time function per equation, which are sought to be represented by neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag and spiral maneuvers. The neural network has one middle or hidden layer of neurons and the Levenberg-Marquardt algorithm is used to obtain the network coefficients. Using the best choices for number of hidden layer neurons, length of training data, convergence tolerance, and so forth, the performances of the proposed neural network models have been investigated and conclusions drawn.

System Identification for Nonlinear Maneuvering of Ships Using Neural Network

A fuzzy autopilot algorithm for maneuvering of surface ships is proposed and its performance studied. A mathematical model for nonlinear maneuvering of cargo ships has been used in conjunction with the proposed fuzzy autopilot. The autopilot works with two error measures that are inputs, error in heading angle and the offset error from the desired path, and has the command rudder angle as the output. Input variable fuzzification, fuzzy associative memory rules and output set defuzzification schemes using Mamdani minimum inference method are described. The proposed control scheme has been verified for a variety of straight line and curved trajectories using a nonlinear maneuvering model of Mariner class vessel.

G. Rajesh

Papers

System identification for nonlinear maneuvering of large tankers using artificial neural network

System Identification for Nonlinear Maneuvering of Ships Using Neural Network

Fuzzy autopilot for ship maneuvering