预估Fuzzy-PID在中央空调控制系统中的应用

电加热常作为中央空调的末端设备置于被调节房间风道入口处,微调加热以提高温度控制精度,为此提出一种电加热温度控制方法,采用预估Fuzzy-PID复合控制算法,通过对加热功率的调节进行闭环控制,实现控制点温度的稳定控制.新的控制算法结合了smith预估控制、Fuzzy和PID控制器的优点.在MATLAB环境中仿真预估Fuzzy-PID控制中央空调末端电加热,结果表明该控制方法能提高温度稳定性并降低能耗,控制精度达到±0.1℃.该控制方法同样适用于其它含有电加热元件的场合.      

环境模拟舱体的建模仿真及控制方法

环境试验时需要精确控制环境模拟舱体内的温度以满足军标要求,同时为了解决环境模拟舱体内加热过程中升温速率与控制精度之间的矛盾,在对其温度变化进行动态特性分析与建模的基础上,提出采用专家控制策略控制环境模拟舱内的温度,并对其控制效果进行了仿真研究.仿真结果与现场运行调试结果均表明:专家控制方法具有无超调、响应速度快、稳态精度高、加温效率明显的优点,是可行和有效的;与试验数据相比,建立的传热模型和控制系统模型具有较高的准确性,符合实际情况.这种建模仿真及控制方法为实际过程中研究控制规律提供了有力的参考和帮助,对研究类似问题具有深远意义.      

专家-模糊PID在低速风洞风速控制系统中的应用

介绍北京航空航天大学D-4低速风洞风速控制系统的结构、控制原理和性能特点.为了解决低速风洞风速控制系统建模困难、控制系统复杂、易受外界影响,且自身参数时变不确定、控制难度大的特点,采用了模糊PID调节算法结合专家判断组成专家-模糊智能控制器的方法控制风速.现场调试运行结果表明:这种方法解决了常规PID不能在线进行参数自整定的问题,不仅具有PID控制器高精度的优点,又具有模糊控制器快速性、稳定性、鲁棒性高的特点,并且具有良好的动、稳态特性,可满足低速风洞风速控制要求.      

An intelligent control method for a large multi-parameter environmental simulation cabin

The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced.Due to the diffculties of control methods and the easily damaged characteristics,control systems for the large multi-parameter environmental simulation cabin are diffcult to be controlled quickly and accurately with a classical PID algorithm.Considering the dynamic state characteristics of the environmental simulation test chamber,a lumped parameter model of the control system is established to accurately control the multiple parameters of the environmental chamber and a fuzzy control algorithm combined with expert-PID decision is introduced into the temperature,pressure,and rotation speed control systems.Both simulations and experimental results have shown that compared with classical PID control,this fuzzy-expert control method can decrease overshoot as well as enhance the capacity of anti-dynamic disturbance with robustness.It can also resolve the contradiction between rapidity and small overshoot,and is suitable for application in a large multi-parameter environmental simulation cabin control system.     

人工智能技术在航空发动机孔探检测中的应用进展

孔探是当前航空发动机检修过程中应用最多的无损检测方法,也是孔探图像的唯一获取途径。近年来,深度学习等人工智能方法开始被应用到航空发动机损伤分类、检测中,为实现航空发动机检修智能化提出了一些现行有效的方法,具有重要的工业应用价值。本文概述了航空发动机孔探检测的发展和优缺点,综述了专家系统和机器学习人工智能方法在发动机孔探图像方面的应用进展,总结了基于孔探图像实现航空发动机孔探检测智能化面临的一些挑战。     

Multi-parameter decoupling and slope tracking control strategy of a large-scale high altitude environment simulation test cabin

A large-scale high altitude environment simulation test cabin was developed to accurately control temperatures and pressures encountered at high altitudes. The system was developed to provide slope-tracking dynamic control of the temperature–pressure two-parameter and overcome the control difficulties inherent to a large inertia lag link with a complex control system which is composed of turbine refrigeration device, vacuum device and liquid nitrogen cooling device. The system includes multi-parameter decoupling of the cabin itself to avoid equipment damage of air refrigeration turbine caused by improper operation. Based on analysis of the dynamic characteristics and modeling for variations in temperature, pressure and rotation speed, an intelligent controller was implemented that includes decoupling and fuzzy arithmetic combined with an expert PID controller to control test parameters by decoupling and slope tracking control strategy. The control system employed centralized management in an open industrial ethernet architecture with an industrial computer at the core. The simulation and field debugging and running results show that this method can solve the problems of a poor anti-interference performance typical for a conventional PID and overshooting that can readily damage equipment. The steady-state characteristics meet the system requirements.