Abstract
In this paper, the ant colony optimization (ACO) method is used to identify the parameters of a 3-DOF nonlinear vessel model. Identifying the parameters is abstracted as a nonlinear optimization problem to solve through the ant colony optimization algorithm. The identification procedure is divided into two parts. The first part of the identification procedure is to identify the parameters related to surge motion. The second part of the identification procedure is to identify the rest parameters of the vessel’s kinetics model. In the surge model identification procedure, the transient motor speed is used to generate the training data, and in the sway and yaw motion identification procedure, the zigzag maneuvering with different motor speeds is used to generate the training data. All the parameters are identified by the ACO method and the least-square (LS) method based on the training data and then validated on the validation data. The prediction performance of parameters identified by different methods is compared in the simulation to demonstrate the effectiveness of the ACO algorithm.
Highlights
Unmanned technology has developed rapidly in recent years and attracted more and more attention from academia and industry
As an intelligent device that works on the water, the unmanned surface vessel can work as a node of the overall unmanned network and extend the working range of the entire unmanned network to the surface of the water and underwater. e level of intelligence of the unmanned network is related to the level of intelligence of every node in the network
It is worth noting that the ants in ant colony optimization (ACO) or CACO are not a container but a solution builder
Summary
Unmanned technology has developed rapidly in recent years and attracted more and more attention from academia and industry. E modeling of the unmanned surface vessel is imperative for both control method design and simulation study purposes [1]. Except for the Nomoto model, the 3-DOF model, including surge velocity, sway velocity, and yaw angular velocity, is widely used in vessel control [11,12,13]. Support vector machines have the advantage in solving the nonlinear optimization problem, and it was used to identify the parameters of the vessel in [24,25,26,27]. The ant colony optimization method is used to identify the parameters of the vessel kinetics model. E parameter-identified problem is summarized as a nonlinear least-square problem, and the ant colony optimization method is used to solve the nonlinear least-square problem.
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