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毕业设计（论文）英文资料翻译外文资料翻译系 别： 自动化系 专 业： 自动化 班 级： 191001 姓 名： 宁帅 学 号： 091680 指导老师： 王佩 2014年 4 月 西北工业大学明德学院本科毕业设计论文 附录1 外文原文Aerodynamic parameter identification of flight vehicles based on adaptive genetic algorithmTo overcome the disadvantages of conventional identification algorithms, no nlinear aerodynamic parameter identification of flight vehicles using max-imum likeliho od method, based on simulation da ta, is car ried out , where adaptiveg enetic algorithm is taken as identificat ion algorithm . During the course of identification, zero-meanvalue random measure noises with Guassian distribution are intro duced. The results of identification are rather satisfactory , which shows that adaptive genetic algo rithm has excellent perfo rmance of o pt imization and may be taken as a practical identification algo rithm for aer odynamic param eter identification.Key words: Aer odynamic parameter identification; Flight vehicle ; Adaptive genetic algorithm ; Maxim umlikelihood method.Maximum likelihood method used on Newton-Raphson algorithm and its improved algorithm has widely used in aerodynamic par ameter identif ication of flight vehicles. However, there existsome disavantages when New to n-Raphson algorithmand its improved algo rithm are used as identif cat ion algo rithms. They are sensitive to init ialization of the parameter s to be identif ied and bring numerical diff iculties on o -taining sensit ivity, so result s of parameter identif icat io n may be greatly affected even invalid or unavailable because of divergence of indentif icat ion Genet ic algorithms are optimizat ion and search methods based on Darw ins natural selection and evolution theory. They will make the so lut ion of optimization pr oblem appro ach bet ter and better through simulation on the g enetic principle of nature. Only the objective function values are used when genetic algorithms are adopted for optimization. Moreover , continuity and different iability of the objective function are no t required. Therefore, g enetic algorithms are suitable for dealing with complex optimization problems for their out standing proerties. Due to the prominent performance of genetic algorithms, it is reasonable to combine genetic algorithms with maximum likelihood identification to improve the reliability and accuracy of aerodynamic parameter identification. In general, process noise is little and may be ignored for flight test when climate is fitting . However, measure noise is inevitable. Our put error method is practical to be used in aero dynamic parameter identification with no consider ation for process noise . So output error method is used here which measure noise is introduced and process noise is ignored. Maximum likelihood method of aero dynamic parameter identification has the following principle that aerodynamic parameters ought to be found to make maximum likelihood function be its maximum for given flight test data. In general, flight test of flight vehicles such as missile has rather little process noise and may be ignored. However , measure noise is inevitable. Therefore, output error method with no consideration for process noise has achieved widely pplication in aero dynamic parameter identification of flight vehicles. Taking axis-symmet rical missiles as example, the state equations in aerodynamic parameter identification are composed of 6 DOF body-axis dynamic equations and kinetic equations: Genetic algorithms used in numerical optimization are mainly related to three elementary genetic operations of reproduction by selection, crossover and mutation. Reproduction is a genetic process that some better individuals are reserved from parents with greater probability by selection. Crossover and mutation are genetic processes that genes in chromosomes of parents are exchanged randomly to produce new individuals and mutate randomly with probability of mutat io n respectively on the basis of genetic operat ons of reproduction, crossover and mutation, off spring are produced and some of them inher it excellent characters from parents. Thus, performance of population will be improved gradually until the final optimal results are obtained. When standard genetic algorithm is employed, binary coding is needed on the precondition of determining the design variables and their ranges first , and then genetic operations take actions on the coded individuals to produce off spring . After decoding on binary design variables, off spring will. have intuitive meaning and fitness values will be evaluated. The off spring with higher fitness values may be selected as parents of the next generation. Just as the above processes, values of the design variables corresponding with the maximum fitness values, when termination criterion, are the optimal results for optimization problem. Moreover, premature convergence and stagnation may occur when standard genetic algorithm is used, which may affect the quality and efficiency of Optimization . Adaptive genetic algorithm using fitness values ranking selection, local crossover,adaptive mutation operators and elitist skill which reserves the excellent individuals ever produced. Adaptive genetic algorithm may prevent optimization from premature convergence and stagnation to improve the quality and eff iciency of optimization. Compared with that in binary coding , operator of mutation may play a much greater role in genetic algorithm. Therefore, efficient design for mutation may considerably improve the quality and efficiency of algorithm. One reasonable idea ought to be such a way that the individuals with higher fitness. values work on a small-scale search and the individuals with lower fitness values work on a largescalesearch. The operation of adaptive mutation is proposed to be : if x i is an individual, and one of its components xk, which belongs to ak, bk , is to be mutated, then the mutated component yk, has the following form: Adaptive genetic algorithm is taken as identification algorithm where aero dynamic parameters are taken as design variables. Size of population and upper limit of evolution are taken as 200 and 300 respectively. Probability of crossover and mutation are 0. 9 and 0. 1. To verify the performance of adaptive genetic algorithm applied in aerodynamic parameter ident ification, measure noises with magnitude of 5 percent of peak with respect to every measure are int roduced into the primitive data. Table shows comparisons between estimated and accurate aerodynamic parameters. Most of the relative errors between estimated and accurate parameters are lower than 5 percent . However , some parameters, especially small-quantity parameters, high-nonlinear parameters and damped derivatives, have great departure from accurate ones. A similar conclusion is obtained through identifiable analysis, which shows that the identif ication results are reasonable and the identification algorithm is effective.附录2：中文翻译基于自适应遗传算法的飞行器气动参数辨识为了克服传统识别算法的缺点，采用最大似然方法，基于仿真数据飞行器没有气动参数辨识，进行，其中自适应遗传算法作为识别算法。在识别过程中，零均值随机分布，关于噪声的防范措施与高斯分布的介绍诱导。鉴定的结果是相当令人满意的，这表明自适应遗传算法的优化性能优良，可采取自适应遗传算法，其作为一种实用的识别算法的航空动力参数辨识。牛顿 - 拉夫逊算法及其改进算法使用最大似然法已广泛应用于飞行器气动参数辨识。但是，当新的到牛顿- 拉夫逊算法及其改进算法作为方式识别算法存在一些缺点。他们是被识别并带来获取灵敏度数值困难的参数初始化参数敏感，所以参数辨识结果可能受到很大的影响，甚至无效，因为鉴定遗传算法发散的或不可用的基于算法中的自然选择和进化理论优化和搜索方法。他们会充分利用最优化问题的解决办法，通过模拟自然的遗传原理越快越好。只对目标函数值时使用的是采用优化的遗传算法。此外，连续性和不同习惯的目标函数是不需要的。因此，遗传算法适合于处理其输出系列特复杂优化问题。由于遗传算法的突出性能，这是合理的遗传算法结合最大似然确定以改善空气动力学参数识别的可靠性和准确性。一般来说，过程噪声小，可能被忽略的飞行试验时的气候是拟合。然而，测量噪声是不可避免的。我们把错误的方法是可行的用于空气动力参数识别，没有考虑振动性的过程噪声。因此，输出误差的方法是用在这里的测量噪声的引入，过程噪声被忽略。空气动力参数识别最大似然方法有以下原则，即气动参数应该被发现，使最大似然函数是它的最大给定的飞行试验数据。在一般情况下，飞行器如导弹飞行测试具有相当小的过程噪声，并且可以被忽略。然而，测量噪声是不可避免的。因此，对过程噪声不考虑输出误差的方法在飞行器空气动力参数识别已经取得了广泛的应用的研究。以轴对称的此种导弹为例，在气动参数辨识的状态方程是由6自由度体轴动力学方程和动力学方程：在数值优化中的遗传算法通过选择，交叉和变异主要
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