Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe...Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.展开更多
飞机上冲压空气涡轮(ram air turbine,RAT)的工作环境具有风速、飞行高度和温度上的随机性,需在诸多工况下都保持稳定运行。该研究对来流空气作用下的冲压空气涡轮调速系统进行了控制学建模,基于此模型开展多种工况下的稳定性分析。首先...飞机上冲压空气涡轮(ram air turbine,RAT)的工作环境具有风速、飞行高度和温度上的随机性,需在诸多工况下都保持稳定运行。该研究对来流空气作用下的冲压空气涡轮调速系统进行了控制学建模,基于此模型开展多种工况下的稳定性分析。首先,根据涡轮工作原理,针对某型RAT的调速系统建立了能够反映涡轮真实响应的动力学模型;然后,将调速系统在转速平衡状态附近做线性近似处理,得到能够描述调速系统在负载扰动下稳定性的闭环控制模型,结合动力学模型的仿真结果和控制学模型的理论计算,分析了涡轮在负载冲击下的响应特性;最后,对调速系统进行稳定性评估,系统地研究了调速系统在不同工作环境中的稳定性规律。数值仿真结果表明,所建立的闭环控制模型能准确反映RAT调速系统的稳定性程度,在风速大、飞行高度低的工况中该系统稳定程度最薄弱。展开更多
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.
文摘飞机上冲压空气涡轮(ram air turbine,RAT)的工作环境具有风速、飞行高度和温度上的随机性,需在诸多工况下都保持稳定运行。该研究对来流空气作用下的冲压空气涡轮调速系统进行了控制学建模,基于此模型开展多种工况下的稳定性分析。首先,根据涡轮工作原理,针对某型RAT的调速系统建立了能够反映涡轮真实响应的动力学模型;然后,将调速系统在转速平衡状态附近做线性近似处理,得到能够描述调速系统在负载扰动下稳定性的闭环控制模型,结合动力学模型的仿真结果和控制学模型的理论计算,分析了涡轮在负载冲击下的响应特性;最后,对调速系统进行稳定性评估,系统地研究了调速系统在不同工作环境中的稳定性规律。数值仿真结果表明,所建立的闭环控制模型能准确反映RAT调速系统的稳定性程度,在风速大、飞行高度低的工况中该系统稳定程度最薄弱。
