基于PIDNN控制的飞行模拟器人感系统

董伟杰; 刘长华; 宋华

基于PIDNN控制的飞行模拟器人感系统

1.
北京航空航天大学自动化科学与电气工程学院, 北京 100083
2. 中国民航飞行学院飞安基地, 成都 610000

基金项目: 国家自然科学基金资助项目(60572185);中国民航飞行学院自然科学研究基金资助项目

详细信息

作者简介:
董伟杰(1982-),男,河北藁城人,硕士,1982dwj_1@126.com.

中图分类号: TP 273
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- 被引次数: 0
出版历程
- 网络出版日期: 2008-02-29

Human perceive system of simulative aircraft based on PIDNN

1.
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2. Base of Flight Safety Civil Aviation Flight University of China, Chengdu 610000, China

摘要

摘要: 针对飞行模拟器人感系统的高度非线性和易受干扰性,提出一种基于PIDNN(Proportional Integral Differential Neural Network)的控制方案.首先对飞行模拟器人感系统的模型进行分析研究,对它所受到的外界干扰作理论分析,整理出系统的数学模型,再利用PIDNN控制器优良的在线训练、学习和调整功能对该模型进行仿真控制.与传统PID(Proportional Integral Differential)控制器相比,PIDNN结构简单、自适应性强、收敛速度快、不会陷入局部极小.仿真结果表明:PIDNN控制系统响应速度快、稳态精度高、具有良好的动静态特性和鲁棒性,满足实时控制的要求.
- 比例积分微分神经元网络(PIDNN) /
- 飞行模拟器 /
- 人感系统 /
- 实时控制
Abstract: A control scheme based on proportional integral differential neural network (PIDNN) was proposed to control the highly nonlinear and vulnerable simulative aircraft and sensing system. Firstly, the models of the human perceive system for the simulative aircraft was analyzed and studied, the system formula was ratiocinated consequently, and its disturbance from outside environment was analyzed theoretically. Then its superior function of PIDNN controller for its on-line training, learning and regulating ability were used to control the system in simulation environment. Comprising the all-ready used traditional proportional integral differential (PID) controllers, PIDNN has simpler structure, stronger adaptability and faster tracing ability without singularity. The simulation experiment result shows that the PIDNN has properties of quick response and nice steady accuracy, and has a well static and dynamic and robust character, so it can meet the need of real-time control.
- proportional integral differential neural network (PIDNN) /
- simulative aircraft /
- human perceive system /
- real-time control

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参考文献(1)

[1] 李玉涛. 数字人感系统的研制 . 北京: 北京航空航天大学自动化科学与电气工程学院, 200l Li Yutao. Design of digital human perceive system . Beijing: School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, 2001(in Chinese) [2] 冯慧. 侧杆操纵中人感系统对人机耦合的影响分析 . 北京:北京航空航天大学自动化科学与电气工程学院,2004 Feng Hui. Analysis of human machine coupling effect on human perceive system in side pole control . Beijing: School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, 2004(in Chinese) [3] 沈东凯,华清,王占林. 基于神经网络的电动加载系统[J].航空学报,2002, 23(6):255-259 Shen Dongkai, Hua Qing, Wang Zhanlin. Electro load system base on neutral network[J]. Acta Aeronautica Et Astronautica Sinica, 2002, 23(6):255-259(in Chinese) [4] 刘金琨. 智能控制[M]. 北京:电子工业出版社,2002:245-266 Liu Jinkun. Intelligent control[M]. Beijing: Publishing House of Electronics Industry, 2002: 245-266(in Chinese) [5] 徐丽娜. 神经网络控制[M]. 北京:电子工业出版社,2003:69-80 Xu Lina. Neutral network control[M]. Beijing: Publishing House of Electronics Industry, 2003: 69-80(in Chinese) [6] 董新民,王小平. 飞行模拟器电动式纵向操纵人感系统的研究王小平. 中国航空学会第八次飞行器控制与操纵学术交流会论文集. 湖北襄樊: 中国航空学会,1999:92-96 Dong Xinmin, Wang Xiaoping. Study of electro simulative aircraft longitudinal control human perceive system Wang Xiaoping. The 8th Chinese Society of Aeronautics and Astronautics Conference On Aircraft Control and Operation. Xiangfan, Hubei: Chinese Society of Aeronautics and Astronautics, 1999:92-96(in Chinese) [7] Hess R A. Analyzing manipulator and feel system effects in aircraft flight control[J]. IEEE Transactions on Systems, Man, and Cybernetic, 1990,20(4):923-931 [8] 林用满,林土胜. 基于PID神经网络的后非线性盲源分离算法[J]. 数据采集与处理,2006, 21(3):260-265 Lin Yongman, Lin Tusheng. Post-nonlinear blind source separation based on PID neural network[J]. Data Sample and Process, 2006, 21(3):260-265(in Chinese) [9] Shu Huailin, Pi Youguo. PID neural networks for time-delay systems[J]. Computer and Chemical Engineering, 2000, 24(7):859-862 [10] 舒怀林. 神经元控制系统[M]. 北京:国防工业出版社, 2006:156-168 Shu Huailin. PIDNN control system[M]. Beijing: National Defence Industry Press, 2006:156-168(in Chinese)

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