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应用科学学报 ›› 2010, Vol. 28 ›› Issue (1): 83-89.

• 控制与系统 • 上一篇    下一篇

改进的多目标遗传算法及其在PID优化设计中的应用

刘楠楠1， 石玉1， 程卫平2， 秦福高3;4   

  1. 1. 南京航空航天大学自动化学院，南京210016
    2. 中国直升机设计研究所飞行控制暨仿真研究室，江西景德镇333001
    3. 常州工学院计算机信息工程学院，江苏常州213002
    4. 河海大学计算机及信息工程学院，南京210098
  • 收稿日期:2009-06-16 修回日期:2009-10-10 出版日期:2010-01-20 发布日期:2010-01-20
  • 作者简介:程卫平，研究员，研究方向：计算机测控，E-mail: cheng_wp2000@tom.com
  • 基金资助:

     国家自然科学基金(No.60501022)资助

Improved Multi-objective Genetic Algorithm with Application to PID Optimization Design

LIU Nan-nan1, SHI Yu1, CHENG Wei-ping2, QIN Fu-gao3;4   

  1. 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
    Nanjing 210016, China
    2. Design Institute of China Aviation, China Helicopter Research and Development Institute,
    Jingdezhen 333001, Jiangxi Province, China
    3. School of Computer and Information Engineering, Changzhou Institute of Technology, Changzhou 213002,
    Jiangsu Province, China
    4. College of Computer and Information Engineering, Hohai University, Nanjing 210098, China
  • Received:2009-06-16 Revised:2009-10-10 Online:2010-01-20 Published:2010-01-20

PDF

1798

被引次数

31

摘要：

该文提出一种多目标遗传算法，采用新的拥挤距离计算方法，改进非支配性的比较方法，并引入双重精英策略，提高了进化效率和解的质量，更有效地保持了解的多样性. 将该算法应用于PID优化设计，使系统可同时兼顾快速性、稳定性和鲁棒性，决策者可根据当前工作需求在所得的Pareto解集中选择最终的满意解. 仿真结果表明提出的设计方法有效.

关键词: 多目标优化, 遗传算法, Pareto最优解, PID控制

Abstract:

We propose a multi-objective optimization genetic algorithm, which uses a new method to calculate crowding distance and improves the comparative method of non-domination. Double elitism-mechanism is introduced to improve efficiency of evolution and solution quality, and more effectively increase diversity of the solution. The algorithm is applied to optimal design of PID. In this way, the system is capable of considering
requirements for quickness, reliability and robustness. A satisfactory solution is selected in Pareto optimum set according to the requirements of the present system. Simulation results indicate effectiveness of the proposed algorithm.

Key words: multi-objective optimization, genetic algorithms, Pareto optimal solution, PID control

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