北京航空航天大学学报 ›› 2009, Vol. 35 ›› Issue (11): 1361-1365.

• 论文 • 上一篇    下一篇

无人机舵面负载模拟系统的小脑模型控制

杨 波1, 王俊奎2   

  1. 1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191;
    2. 成都飞机设计研究所, 成都 610041
  • 收稿日期:2008-10-22 出版日期:2009-11-30 发布日期:2010-09-16
  • 作者简介:杨 波(1972-),女,四川广安人,副教授,boyang@buaa.edu.cn.

CMAC neural network for the rudder dynamic load simulator of unmanned aerial vehicles

Yang Bo1, Wang Junkui2   

  1. 1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
    2. Chengdu Aircraft Design & Research Institute, Chengdu 610041, China
  • Received:2008-10-22 Online:2009-11-30 Published:2010-09-16

摘要: 为解决无人机舵面负载模拟系统中非线性和多余力矩扰动问题,利用小脑模型神经网络非线性逼近能力强、结构简单、适于实时控制等特点,采用小脑模型和传统PD(Proportional-Derivative)控制结合的复合控制策略,由小脑模型实现前馈控制,PD控制实现反馈控制,以保证在系统运行各阶段的控制精度.分析讨论了复合控制的不稳定性问题,研究了基于可信度分配和学习率自适应调整的改进型小脑模型的应用情况,提出一种适用于单输入单输出系统的简化小脑模型复合控制设计方法.仿真结果表明该方法有效地解决了小脑模型和PD复合控制的不稳定问题,改善了系统动态加载性能,并具有很好的抗干扰性能.

Abstract: In order to solve the nonlinearity and the surplus torque disturbance in the rudder load simulator of the unmanned aerial vehicles, a hybrid controller was proposed with the cerebellar model articulation controller(CMAC) network and the traditional proportional-derivative(PD) controller. The CMAC can simulate the nonlinear continuous function with high approximation and high speed learning rate which is suitable for the real time control of nonlinear systems. In the hybrid controller, the CMAC performed the feed forward control and the PD controller realized the feedback control to ensure the accuracy in process of the control. The instability of hybrid controller was discussed and analyzed, the application of credit-assigned and adaptive learning rate CMAC hybrid controller was researched and a simple one-input and one-output CMAC hybrid control method was proposed. The simulation results show the method has settled the unstable problem of the hybrid controller and effectively improved the system dynamics with high robustness.

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