【作者】 戴运桃；

【导师】 赵希人；

【作者基本信息】 哈尔滨工程大学 ， 系统工程， 2010， 博士

【摘要】 在船舶运动控制领域,建立船舶运动数学模型有两个目的:建立船舶操纵模拟器,为研究闭环系统性能提供一个基本的仿真平台;直接为设计船舶运动控制器服务。通过理论计算或实验得到的水动力参数精度难以保证,因此,目前大多采用辨识的方法得到水动力参数。从算法执行过程来看,水动力参数辨识问题可以归结为优化搜索问题。粒子群优化算法是20世纪90年代提出的一种群智能优化算法。其优越的问题求解模式在优化问题的求解中取得了极大成功,引起了相关领域学者的广泛关注。实践已经证明,粒子群优化算法能够很好的解决复杂非线性条件下的多约束优化问题。本文主要针对粒子群优化算法及其在船舶运动水动力参数辨识中的应用进行研究,论文主要研究工作如下:1.概述了粒子群优化算法的基本原理,并对粒子群优化算法的拓扑结构进行了详细的分析,阐述了粒子群优化算法的拓扑结构对算法性能的影响,给出了不同结构的粒子群优化算法对标准函数在算法性能、收敛速度、达优率等方面的测试结果。2.分析了简化粒子群优化算法的收敛特性,并通过大量实验,详细分析了粒子群优化算法中各参数对算法收敛精度、达优率等方面的影响,提出了一种利用一个顶层粒子群优化算法对底层用来优化函数的粒子群优化算法的参数进行优化选取的方法。算法仿真和性能对比试验结果表明,该方法能够方便有效的实现粒子群优化算法参数的优化选取,有利于粒子群优化算法的应用和推广。3.基于提高算法寻优能力和算法寻优速度两方面考虑,分别设计了基于进化的粒子群优化算法和基于分阶段搜索的粒子群优化算法。基于进化的粒子群优化算法在标准粒子群优化算法的基础上引入一种进化策略,增加粒子的多样性。在算法迭代寻优的过程中,通过对群体中的粒子进行选择、变异等进化操作,构造进化粒子群优化算法,提高算法的全局搜索能力。基于分阶段搜索的粒子群优化算法是利用粒子群搜索过程中参数收敛特性,对待辨识参数进行分组,采用分阶段搜索的方式进行辨识。求解结果表明,该算法能够快速的辨识各参数,验证了算法的有效性,该算法尤其适合高维数的复杂系统辨识。4.对船舶纵向运动参数辨识问题进行了描述,分析了对船舶纵向运动参数辨识所要考虑的各方面因素。对观测数据的特性进行了分析,并给出了数据预处理的方法,另外,还设计了两种不同的输入参数建模方式,并分别进行了仿真。基于模糊CMAC神经网络的有关理论,以及通过切片理论计算和水池实验获得的数据,建立了任意航向、航速和海情的自适应变化的非线性参数智能化模型,为水动力参数辨识提供了有效的搜索区间。为了考察算法对噪声的适应能力,在有噪声干扰和没噪声干扰两种情况下都做了仿真,并进行了对比。仿真实验证明,基于改进的粒子群优化算法能够正确的辨识船舶纵向运动水动力参数,为船舶水动力参数辨识提供了一种新的解决方案。5.对船舶横向运动参数辨识问题进行了描述,分析了船舶横向运动参数辨识的难点和解决方案。针对船舶横向水动力参数多、参数之间耦合度高的特点,提出了一种计算参数敏感性系数的方法,并依据敏感性系数对参数进行了分类,采用分阶段的方法对参数进行辨识。仿真结果表明,该方法能够正确有效的对船舶横向运动水动力参数进行辨识。更多还原

【Abstract】 In the field of ship motion control, there are two purposes to establish the ship motion model. One is to establish a ship maneuvering simulator, providing a basic simulation platform for the study about close-loop system. The other is to service the design of ship motion controller. It is difficult to guarantee the accuracy of hydrodynamic parameters obtained through theoretical calculations or experiments, so many people obtain the hydrodynamic parameters by the identification algorithm. In the process of algorithm execution, this problem can be regarded as an optimization search.Particle Swarm Optimization (PSO) algorithm is a swarm intelligence optimization algorithm proposed in the 1990s. The superiority of distributed solution model of problem in solving combinatorial optimization problem is great, and this causes large attention of the concerned fields. The practice has shown that, particle swarm optimization algorithm can well solve problems of multi-constraint optimization under complicated nonlinear conditions. This paper is mainly about particle swarm optimization algorithm and its application in the hydrodynamic parameter identification of ship motion, including:1. The basic principle of the particle swarm optimization algorithm is summarized and the topology of PSO algorithm is analyzed in detail. Then, the influence of the topology to algorithm performance is analyzed. Last, some test results about PSO algorithm of different structures to the benchmark function in different aspects, such as algorithm performance, convergence rate, and rate of searching the optimization and so on are given.2. The convergence of PSO algorithm is analyzed in detail. And the influence of parameters in PSO algorithm to some aspects, such as algorithm accuracy, rate of up to optimization and so on is analyzed in detail. Then a method which optimizes and selects parameters of the bottom PSO algorithm by using a top PSO algorithm is presented. The bottom PSO algorithm is applied to optimize function. The comparison of aloogorithm simulation and performance shows that this method can implement parameters optimization and the selection of PSO algorithm convenient effectively.3. Considered the improvements of optimization capacity and speed, PSO algorithms based on evolutionary and phased search are designed. PSO algorithm based on evolutionary introduces an evolutionary strategy to increase the diversity of particles based on the standard PSO algorithm. In the process of algorithm iteration and optimization, the evolutionary PSO algorithm is constructed and the global search capabilityof the algorithm is improved through some operations on particles such as selection, mutation and so on. Making use of parameters convergence characteristics, PSO algorithm based on phased search divides parameters into groups, then identifies them. The solution makes clear that this algorithm can identify them quickly, and verify the effectiveness of the algorithm. The algorithm is particularly suitable for high-dimensional complex identification system.4.The paper describes problem about parameter identification of ship vertical motion, and analyzes all factors to be taken into account. Then it analyzes the characteristics of these observed data, and gives a data pretreatment method. Moreover, two different modeling methods of parameters input are designed and simulated. Based on the relative theories in fuzzy CMAC neural network and the data from strip theory calculating and pool experiment, the adaptive nonlinear parameter model, which can adapt to any changes of course, speed and sea condition is built. The model provides the effective searching space of hydrodynamic parameters identification. In order to investigate the algorithm ability to adapt to noise, two simulations with and without noise are taken and compared. The result shows that the improved PSO can identify ship vertical motion hydrodynamic parameters accurately, and provides a new solution for hydrodynamic parameters identification.5.The paper also describes problem about parameter identification of ship lateral motion, and analyses problems and solutions about this problem. It comes up with a method to calculate parameters sensitivity coefficients due to the characteristic of many ship lateral movement hydrodynamic parameters and high coupling, and classifies parameters according to sensitivity coefficients, then identifies them with phased method. The simulation results indicate that this method can carry on ship lateral motion hydrodynamic parameters identification correctly and effectively.更多还原