基于遗传算法的水面无人艇操纵响应模型参数辨识仿真研究

Simulation Research of Parameter Identification of Maneuvering Response Model of Unmanned Surface Vessel Based on Genetic Algorithms

为实现水面无人艇(Unmanned Surface Vessel, USV)的操纵性预报,针对USV二阶非线性操纵响应模型,提出了一种基于遗传算法(Genetic Algorithms, GA)的辨识方法用于辨识模型参数。首先在仿真平台采用龙格库塔法(Runge-Kutta)开展Z型操纵仿真实验,基于差分离散法设计辨识模型,构造辨识模型适应度准则函数,初始种群在经过数代选择、交叉、变异后逐渐收敛至最优解,通过其收敛值计算模型辨识结果。辨识结果在精度和收敛性上均优于粒子群(Particle Swarm Optimization, PSO)算法,且在仿真试验情况下最大辨识误差率为4.19%,仿真试验验证了辨识结果的有效性和泛化性。GA是一种有效的高精度辨识算法,且辨识精度优于PSO算法,辨识结果能有效预报水面无人艇的操纵性。

In order to solve the maneuverability forecast problem of unmanned surface vessel(USV),a identification method based on genetic algorithms(GA)is used to obtain the parameters of second-order nonlinear maneuver response model.Firstly,the zigzag simulation manipulation experiment is carried out on the simulation platform based by Runge-Kutta method.Then,the identification model is designed based on the difference method.Constructing fitness criterion function of identification model,the initial population gradually converges to the optimal solution after several generations of selection,crossover,and mutation,and the identification result can be calculated by convergence value.The identification results are superior to the Particle Swarm Optimization(PSO)algorithm in terms of accuracy and convergence,with a maximum identification eror rate of 4.19%in simulation experiments.Simulation experiments have verified the effectiveness and generalization of the identification results.GA is an effective high-precision identification algorithm,and the identification accuracy is better than PSO algorithm.The identification results can effectively predict the maneuverability of USV.