基于CPSO优化的空空导弹<i>μ</i>综合控制器设计

鲁启东; 陈欣; 张民

基于CPSO优化的空空导弹μ综合控制器设计

南京航空航天大学自动化学院, 南京 210016

基金项目: 飞行器自主控制技术教育部工程中心资助项目

详细信息

中图分类号: V249
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出版历程
- 收稿日期: 2012-12-11
- 网络出版日期: 2013-11-30

μ-synthesis robust control for air-to-air missile based on dynamic acceleration constant PSO arithmetic

College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China

摘要

摘要: 粒子群优化算法（PSO，Particle Swarm Optimization）在空空导弹μ综合控制器参数优化中易出现早熟现象而无法获得全局最优解.针对此问题，提出一种动态加速常数的粒子群优化算法（CPSO，Constant Particle Swarm Optimization）.改进算法通过对加速常数的指数形式变化，在寻优前期扩大搜索范围，在后期提高收敛效率，从而避免了寻优过程中的早熟现象.仿真结果表明，改进的CPSO优化算法具有更强的全局搜索能力，设计出的μ综合控制器具有更优的性能，满足给定的性能指标和自动设计指标，节省了大量设计时间，具有工程应用价值.
- 空空导弹 /
- μ综合 /
- 粒子群优化 /
- 控制参数
Abstract: Particle swarm optimization (PSO) algorithm is prone to premature convergence in the control parameter optimization of air-to-air missile μ-synthesis controller, which makes the global optimal solution inaccessible. Aiming at this situation, dynamic acceleration constant particle swarm optimization (CPSO) algorithm was proposed. This improved algorithm, through changing the index form of the acceleration constant, expanded the searching range in the beginning phase of optimization, improved the efficiency of convergence in its latter phase, and finally the premature phenomena was avoided. The simulation results reveal that the improved version of exponential CPSO algorithm is of great significance in engineering applications. It has a better ability in global searching. μ-synthesis controller based on this algorithm maintains optimal performance, which meets the given performance indicators and automatic design specifications and saves much design time.
- air-to-air missile /
- μ-synthesis /
- particle swarm optimization /
- control parameters

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参考文献(14)

[1]	樊会涛, 刘代军.红外近距格斗空-空导弹发展展望[J].红外与激光工程, 2005, 34 (5):564-568 Fan Huitao, Liu Daijun.Development trends of short-range dogfight IR air to air missile[J].Infrared and Laser Engineering, 2005, 34 (5):564-568 (in Chinese)
[2]	郑建华, 杨涤.鲁棒控制理论在倾斜转弯导弹中的应用[M].北京:国防工业出版社, 2001:81-86, 116-117 Zheng Jianhua, Yang Di.The application of robust control theory to bank-to-turn missile[M].Beijing:National Defense Industry Press, 2001:81-86, 116-117 (in Chinese)
[3]	张民, 陈欣, 陆宇平.基于改进PSO算法的导弹控制参数优化[J].南京航空航天大学学报, 2009, 41 (4):445-449 Zhang Min, Chen Xin, Lu Yuping.Missile control parameter optimization based on improved PSO algorithm[J].Journal of Nanjing University of Aeronautics & Astronautics, 2009, 41 (4):445-449 (in Chinese)
[4]	魏秀业, 潘宏侠.粒子群优化及智能故障诊断[M].北京:国防工业出版社, 2010:7-10 Wei Xiuye, Pan Hongxia.Particle swarm optimization and intelligent fault diagnosis[M].Beijing:National Defense Industry Press, 2010:7-10 (in Chinese)
[5]	吴启迪, 康琦, 汪镭, 等.自然计算导论[M].上海:上海科学技术出版社, 2011:2-9 Wu Qidi, Kang Qi, Wang Lei, et al.Natural computing introduction[M].Shanghai:Shanghai Science and Technology Press, 2011:2-9 (in Chinese)
[6]	史忠科.鲁棒控制理论[M].北京:国防工业出版社, 2003:187-253 Shi Zhongke.Robust control theory[M].Beijing:National Defense Industry Press, 2003:187-253 (in Chinese)
[7]	Balas G J, Doyle J C, Glover Keith.μ-analysis and synthesis toolbox for use with MATLAB[J].MUSYN Inc and the MathWorks, 2001:2-230
[8]	张民, 陈欣, 陆宇平.PSO算法用于导弹鲁棒控制器性能权函数优化[J].应用科学学报, 2011, 29 (6):650-654 Zhang Min, Chen Xin, Lu Yuping.Optimization of performance weighted function for missile robust controller using PSO algorithm[J].Journal of Applied Sciences, 2011, 29 (6):650-654 (in Chinese)
[9]	Kennedy J, Eberhart R.Particle swarm optimization[C]//Proc IEEE International Conf.on Neural Networks.Piscataway, NJ:IEEE, 1995:1942-1948
[10]	Kennedy J, Eberhart R.Swarm intelligence[M].Burlington:Morgan Kaufmann Publishers, 2001
[11]	Krohling R A.Gaussian swarm:a novel particle swarm optimization algorithm[C]//IEEE Conference on Cybernetics and Intelligent Systems.Piscataway, NJ:IEEE, 2004:372-376
[12]	Nickabad A, Ebadzadeh M M, Safabakhsh R.A novel particle swarm optimization algorithm with adaptive inertia weight[J].Applied Soft Computing, 2011, 11 (4):3658-3670
[13]	Montes de O M A, StÜ tzle T, Birattari M, et al.Frankenstein-s PSO:A composite particle swarm optimization algorithm[J].IEEE Transactions on Evolutionary Computation, 2009, 13 (5):1120-1132
[14]	Leung S Y S, Tang Yang, Wong W K.A hybrid particle swarm optimization and its application in neural networks[J].Expert Systems with Applications, 2012, 39 (1):395-405