【作者】 张丽娜；

【导师】 袁赣南；

【作者基本信息】 哈尔滨工程大学 ， 导航、制导与控制， 2017， 博士

【摘要】 水动力参数是航行器设计的重要参数,一些敏感度较高的水动力参数难以用实航或水池实验的方法获得,且用理论方法计算得到的结果与实航情况有很大的差别。因此通过先进的辨识技术,建立精确的航行器水动力参数与运动状态之间的数学模型,对航行器的优化设计和性能的提高具有非常重要的意义。在确定辨识准则的情况下,水动力参数辨识问题可归结为复杂的函数优化问题。萤火虫算法是21世纪初提出来的一种新型的智能优化算法。由于萤火虫算法结构简单、易于实现、需要调节的参数少等特点,及其在解决实际工程问题和函数优化问题时所表现出来的优越性,一经提出便得到了国内外学者的广泛关注。本文主要针对萤火虫算法的基础理论及其在船舶纵向运动水动力参数辨识中的应用进行研究,主要工作如下:1、对萤火虫算法(FA)的数学模型、参数设置机制、时间复杂度和算法的有效性等内容进行了深入的理论研究。由于萤火虫算法属于随机搜索算法,随机机制的引入在提高算法的收敛速度和精度的同时,也对算法的理论研究提出了巨大的挑战。相对于萤火虫算法的广泛应用,其理论研究方面较为匮乏,本文对萤火虫算法的基础理论进行了深入的研究,并给出了算法参数的选取原则。2、深入研究了随机机制对算法性能的影响,提出了基于Lévy飞行的改进萤火虫算法和基于随机分布策略的改进萤火虫算法。随机机制是智能优化算法的灵魂,对算法性能的影响非常大。仿真实验结果显示,恰当的随机机制的选取可以使算法有效的跳出局部最优,提高算法的寻优性能,反之将会使算法过早地陷入停滞状态,寻优结果较差甚至不可用。3、深入研究了智能优化算法的多算法融合机制,提出了具有同步式并行结构的混合萤火虫算法(HFA)。算法子迭代过程中并行执行萤火虫算法和差分进化算法,然后对得到的种群进行混合和随机重组。仿真实验表明,并行计算时,萤火虫算法的寻优机制可以增加算法的全局搜索性能进而加快算法的收敛速率,差分进化算法的寻优机制可以增加算法的局部探测能力进而提高算法的求解精度。另外算法中种群的混合和随机重组机制,既能增加种群间的信息交流又能增加算法的多样性,避免算法寻优出现停滞现象。4、深入研究了智能算法中全局探测能力和局部挖掘能力的平衡对算法性能的影响,提出了基于参数自适应调节的混合萤火虫算法(SaHFA)。理论研究证明,全局探测和局部挖掘性能的平衡可大大提升优化算法的性能。本改进算法在HFA算法的基础上对参数进行自适应性的调整,将参数的调节和选取隐含在算法迭代过程中。仿真实验表明,该机制可以使算法在运行过程中自适应地调节和平衡算法中的全局探测和局部挖掘能力,大大提高算法的收敛速度、求解精度和鲁棒性。5、深入研究了对船舶纵向参数辨识问题,利用本文提出的参数自适应调节的混合萤火虫算法进行船舶纵向运动参数辨识。首先建立船舶纵向运动的离散状态方程。其次,分别对舵扰动和海浪扰动进行建模,得到舵角扰动模型和海浪扰动力、扰动力矩模型。第三,选取最小二乘误差准则作为适应度函数,并详细介绍利用改进萤火虫算法辨识船舶纵向运动水动力参数的思想和实现步骤。第四,分舵扰动和海浪扰动两种情况,对船舶纵向运动水动力参数进行辨识,并探讨观测噪声对算法性能的影响。最后,对仿真结果性能进行评估。仿真实验表明,利用该改进算法可以成功地同时辨识出船舶纵向运动的8个参数,且辨识精度较高。更多还原

【Abstract】 Hydrodynamic parameters are very important during the aircraft design process,while some hydrodynamic parameters with high sensitivity are hard to obtain by real cruising or tank experiment,and there is a great difference between theoretical calculating and real cruising.Establishing the precise mathematical model descripting craft hydrodynamic parameters and motion states by advanced identification technology is of great significant for the craft optimization design and performance improvement.The problem of hydrodynamic parameters identification can boil down to a complex function optimization problem after determining the identification criteria.Firefly algorithm was proposed in the early 21^st century as a new intelligent optimization algorithm.It has attracted attentions from researchers home and abroad with advantages of simple structure,easy implementation,less parameters needed to be adjusted,and superiority in solving practical engineering problems and function optimizations.This paper focuses on the basic theory of firefly algorithm and its applications in the ship longitudinal motion hydrodynamic parameters identification.The main works are as follows:1.The theoretical study of mathematical model,parameters setting mechanism,time complexity and algorithm validity of the firefly algorithm（FA）has been carried out in depth.Since the FA is a stochastic search algorithm,the introduction of random mechanism not only improves the convergence rate and accuracy of the algorithm,but also brings a huge challenge to its theoretical research,which has been rarely studied compared to its pervasive application.In this paper,the basic theory of FA is deeply researched,and the parameters choosing principle is given.2.The influence of random mechanism on the algorithm performance is studied,and two improved FAs are proposed based on Lévy flight and random distribution strategy respectively.Random mechanism is the soul of intelligent optimization algorithm and has significant effect on algorithm performance.Simulation results show that the proper selection of random mechanism can help the algorithm jump out of local optimum effectively and improve the precision and convergence rate.Otherwise,it would lead the algorithm to the stagnation too early and obtaining poor optimization results even unusable.3.The multiple algorithm fusion mechanism of intelligence optimization algorithm is studied,and a hybrid FA with synchronous parallel structure is proposed.FA and differential evolution（DE）are executed in parallel during the iterative process,and the obtained population will be mixed and regrouped again.Experimental results illustrate that FA can provide an accelerated convergence rate and an increased global exploration ability,moreover DE can improve the precision and increase local detection ability.Besides,the mix and regroup mechanism can enhance the communication between populations and increase the diversity of the algorithm to avoid stagnation.4.The influence of the balance between global exploration ability and local exploitation ability on algorithm performance is studied,and a self-adaptive hybrid firefly algorithm（SaHFA）based on self-adaptive parameter control mechanism is proposed.It is proved that the balance between global exploration and local exploitation can significantly improve algorithm performance.SaHFA can adjust the parameters adaptively based on HFA,and implicit the parameters adjustment and selection in the iteration process.Experimental results show that this mechanism can adjust and balance the global exploration and local exploitation ability adaptively during runtime,as well as improving the convergence rate,accuracy and robustness.5.The ship longitudinal motion parameters identification problem is studied by using SaHFA.First of all,the discrete state equation of ship longitudinal motion is established;second,rudder angle disturbance model and wave disturbing force and torque model are built by modeling the rudder and wave disturbance respectively;third,least square error criterion is chosen as fitness function,and the ideas and implementation process of the improved FA are illustrated in detail;Forth,ship longitudinal motion parameters are identified under rudder and wave disturbance respectively,the influence of observation noise on algorithm performance is discussed,too.Finally,the simulation results are evaluated,which show that the improved algorithm can successfully identify eight parameters in ship longitudinal motion at the same time,and the precision is quite high.更多还原