基于自调整神经元的航空发动机多变量自适应解耦控制  

Multivariable adaptive decoupling control based on auto-tuning neurons for aeroengine

作　　者：朱玉斌[1] 樊思齐[1] 张秀华[2] 李华聪[1] 

机构地区：[1]西北工业大学动力与能源学院,西安710072 [2]西北工业大学电子信息学院,西安710072

出　　处：《航空动力学报》2007年第3期490-494,共5页Journal of Aerospace Power

摘　　要：根据航空发动机性能控制要求,通过分析自调整神经元及最速下降学习方法,研究了基于自调整神经元的航空发动机多变量自适应解耦控制系统.利用自调整神经元的结构简单、各神经元之间没有权值连接及在线学习的优点,在线整定多变量PID控制器的参数.阐明了该方法的结构和原理.并进行了航空发动机多变量自适应解耦控制系统的设计.大量的仿真结果表明,系统具有良好的解耦特性和自适应能力.According to the requirements of aeroengine performance control, a new neural network called auto-tuning neurons and gradient descent learning method are presented. A multivariable decoupling control algorithm based on the auto-tuning neurons is used for aeroengine multivariable control systems in this paper. The main difference between an auto-tuning neuron and a general neuron is that there are adjustable parameters of the activation function used in an auto-tuning neuron. Unlike traditional fully connected neural network, there are no synaptic connections among the independent neurons. The emphasis is focused on the research of the algorithm and the properties of the controller, as well as their application to the aeroengine control by means of computer simulation. Finally the aeroengine multivariable control system is designed. Simulation shows that the system has perfect performance of decoupling and adaptive capabilities.

关 键 词：航空 航天推进系统 航空发动机 自调整神经元 多变量控制 解耦控制 自适应控制 

分 类 号：V233.7[航空宇航科学与技术—航空宇航推进理论与工程]