| 摘要: |
| 将神经网络与传统的PID控制相结合,构成神经网络自学习PID控制,用神经网络在线整定PID控制器的比例、积分及微分三个参数,使被控对象跟踪理想参考模型的输出。该系统具有自学习能力,能适用于非线性、时变的被控对象。将神经网络自学习PID控制方法用于航空发动机全包线控制以及蜕化发动机的控制,进行了数字仿真,验证了该方法的有效性。 |
| 关键词: 航空、航天推进系统 神经网络 PID控制 自学习+ |
| DOI： |
| 分类号:V233.7 |
| 基金项目:国家自然科学基金(50576033);航空科学基金(04C52019)资助项目 |
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| Self-learning PID control based on neural networks for aeroengines |
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YAO Hua,YUAN Yang,BAO Liang-liang,SUN Jian-guo
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1.Coll.of Energy and Power,Nanjing Univ.of Aeronautics and Astronautics,Nanjing 210016,China;2.China Aviation Motor Control System Inst.,Wuxi 214063,China;3.Coll.of Energy and Power,Nanjing Univ.of Aeronautics and Astronautics,Nanjing 210017,China;4.Coll.of Energy and Power,Nanjing Univ.of Aeronautics and Astronautics,Nanjing 210018,China
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| Abstract: |
| A self-learning PID controller based on neural networks and conventional PID control was developed.The parameters of PID controller are tuned on-line with the neural networks to make the output of the controlled plant follow the desired output of a reference model.The resulting control system is capable of self-learning and can be used for controlling nonlinear and time-varying plant.The proposed method is applied to aeroengine control.Digital simulation results show that the self-learning PID control proposed is effective to control nominal and deteriorated aeroengine in full envelope. |
| Key words: Aerospace propulsion system Neural networks PID control Self-learning+ |
