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应用科学学报 ›› 2009, Vol. 27 ›› Issue (3): 326-330.

• 控制与系统 • 上一篇    

基于大脑情感学习的转台逆模型补偿控制

甄子洋， 王道波， 王志胜， 黄国勇   

  1. 南京航空航天大学自动化学院，南京210016
  • 收稿日期:2008-04-19 修回日期:2008-12-31 出版日期:2009-05-28 发布日期:2009-05-28
  • 作者简介:甄子洋，博士生，研究方向：信息融合估计与控制、无人机飞行控制和计算智能，E-mail：zhenziyang@nuaa.edu.cn；王道波，教授，博导，研究方向：无人机飞行控制、机电模拟，E-mail：dbwangpe@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金(No.60874037); 江苏省普通高校研究生科研创新计划(No.CX08B_091Z); 南京航空航天大学博士学位论文创新与创优基金(No.BCXJ08-06) 资助项目


Inverse Model Compensation Control of Turntable Based on Brain Emotional Learning

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2008-04-19 Revised:2008-12-31 Online:2009-05-28 Published:2009-05-28

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1404

被引次数

7

摘要：

     针对传统控制方法难以克服飞行仿真转台伺服系统中存在的摩擦等非线性干扰力矩问题，提出了一种基于大脑情感学习(brain emotional learning, BEL)模型的转台伺服系统复合控制方法. 在传统PI反馈控制器的基础上，设计了基于BEL模型的同结构系统逆模型辨识器和前馈补偿控制器. 通过在线辨识系统逆模型来学习BEL模型的节点权值. 理想转台模型的数值仿真和实际转台伺服系统的实验结果都表明：BEL模型学习能力强，能满足实时控制要求，基于BEL模型的复合控制策略能有效抑制摩擦力矩的影响，提高转台系统的跟踪性能.

关键词: 飞行仿真转台 , 大脑情感学习 , 系统辨识 , 补偿控制

Abstract:

      In view of the difficulty in dealing with nonlinear disturbance moments such as friction in the flight simulator’s servo system, a compound control method using a brain emotional learning (BEL) model is proposed. Based on the traditional PI feedback controller, a BEL model-based identifier and a forward compensation controller of the same structure are designed. The node weights of the BEL model are obtained through inverse model identification of the system. Simulation results of an ideal simulator model and experimental results of a real simulator servo system show that the BEL model has good learning ability and meets the real-time control requirement. Moreover, the compound control strategy based on the BEL model can effectively reduce the influence of friction moment and improve tracking performance of the simulator.

Key words: flight simulator , brain emotional learning , system identification ,   compensation control

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