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Napolitano M.R. Silvestri G. Windon D.A. II Casanova J.L. Innocenti M. 《IEEE transactions on aerospace and electronic systems》1998,34(2):456-468
The objective of this document Is to show the capabilities of parallel hardware-based on-line learning neural networks (NNs). This specific application is related to an on-line estimation problem for sensor validation purposes. Neural-network-based microprocessors are starting to be commercially available. However, most of them feature a learning performed with the classic back-propagation algorithm (BPA). To overcome this lack of flexibility a customized motherboard with transputers was implemented for this investigation, The extended BPA (EBPA), a modified and more effective BPA, was used for the on-line learning, These parallel hardware-based neural architectures were used to implement a sensor failure detection, identification, and accommodation scheme in the model of a night control system assumed to be without physical redundancy in the sensory capabilities. The results of this study demonstrate the potential for these neural schemes for implementation in actual flight control systems of modern high performance aircraft, taking advantage of the characteristics of the extended back-propagation along with the parallel computation capabilities of NN customized hardware 相似文献
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基于部件跟踪滤波器的解析余度技术 总被引:2,自引:0,他引:2
研究一种以发动机部件跟踪滤波器(CTF)为基础的解析余度技术, 它将CTF与故障检测、隔离和适应逻辑进行了有效的综合, 以改进发动机数控系统的可靠性。仿真表明, 本文所设计的解析余度技术, 在传感器无故障时, 机载模型能正确跟踪发动机的变化。当传感器发生故障时, 在不损坏机载模型的情况下, 又能及时、有效地进行硬、软故障的检测、隔离与适应。 相似文献
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采用一组卡尔曼滤波器检测发动机传感器故障 总被引:2,自引:0,他引:2
在发动机全功能数字电子控制系统中,提高传感器工作的可靠性是十分重要的,除了不断对传感器本身的性能加以改进提高外,现在广泛地采用了余度技术。近二十年来对解析余度(Analyt ical Redundancy)进行了广泛的研究,解析余度(AR)方法是基于各状态变量之间存在的解析关系,在系统可观条件下,利用无故障的输出测量值去估计(构造)已故障传感器正常工作状态时的输出信息,从而实现对故障的检测、隔离与重构,保证控制系统具有预定的控制性能。 相似文献
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采用序列概率比方法检测航空发动机传感器软故障 总被引:1,自引:0,他引:1
提出基于卡尔曼滤波和序列概率比方法进行某型涡扇发动机控制系统传感器软故障检测新方法.研究了采用修正的序列概率比方法处理滤波残差,检测传感器软故障;并将该方法与残差加权二乘算法WSSR(Weighted Sum of Squared Residual)检测传感器软故障过程进行了对比.仿真结果表明,序列概率比方法较WSSR法所需决策时间短,适合于航空发动机传感器软故障检测. 相似文献
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故障诊断的神经网络多重模型自适应方法 总被引:1,自引:0,他引:1
将神经网络与故障诊断的多重模型自适应方法相结合,提出了故障诊断的神经网络多重模型自适应方法,并对某型航空发动机控制系统传感器故障进行诊断仿真。仿真表明,该方法能够用来解决具有模型不确定性系统的故障诊断问题,同时,对未知的故障模态具有自学习能力 相似文献
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基于自联想神经网络的发动机控制系统传感器故障诊断与重构 总被引:5,自引:0,他引:5
研究自联想神经网络及其在发动机控制系统传感器故障诊断及重构中的应用。自联想神经网络关键在于特征提取和噪声滤波。综合自联想网络的最优估计与故障诊断 ,自动区分估计误差和传感器故障。仿真结果表明这种方法不需要模型 ,能诊断传感器硬、软故障 ,当发动机性能蜕化时也能提供很好的解析余度。 相似文献
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飞机着陆时的容错导航 总被引:2,自引:0,他引:2
研究了飞机在微波着陆环境中的传感器容错系统。该系统的主要目的是检测辅助导航设备和机载传感器的故障,并在这些传感器可能发生故障时提供可靠的飞机状态估计值。自动导航和控制系统利用这些状态估计控制和引导飞机按预定路线降落。 相似文献
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推力矢量飞机自适应控制系统仿真平台研究 总被引:1,自引:0,他引:1
研究了具有自修复功能的推力矢量飞机自适应控制系统的结构功能特点,研究了RHO优化控制算法实现在线控制器设计,利用MSLS辨识算法实现在线飞行参数辨识和等价空间算法、传感器信息融合技术和概率统计理论实现FDI算法。并且根据系统各个部分的算法,采用面向对象技术语言VC 6.0和三维图形语言OpenGL开发了仿真平台,利用仿真平台实时演示了飞机存在舵面故障情况下的飞行控制系统运行仿真,解决了飞机飞行过程中存在舵面损伤和气动参数变化的问题,该仿真平台可以根据需求进行飞机故障加载,具备完备的推力矢量飞机自适应控制系统仿真功能。 相似文献
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Harrison J.V. Tze-Thong Chien 《IEEE transactions on aerospace and electronic systems》1975,(3):349-357
The application of two-degree-of-freedom inertial sensors in a minimally redundant strapdown configuration is considered. The potential improvement in reliability which can be achieved by exploiting the failure isolation capability unique to this configuration is evaluated. A unified, statistical approach to the detection and isolation of both hard and soft sensor failures is presented. The effectiveness of this unified approach to FDI in terms of the mean time to detection, the mean time between false alarms, and the accumulated attitude error prior to detection is indicated by simulation results. 相似文献
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Multiple model adaptive estimation (MMAE) is applied to the Variable-In-flight Stability Test Aircraft (VISTA) F-16 flight control system at a low dynamic pressure flight condition (0.4 M at 20000 ft). Single actuator and sensor failures are addressed first, followed by dual actuator and sensor failures. The system is evaluated for complete or “hard” failures, partial or, “soft” failures, and combinations of hard and soft actuator and sensor failures. Residual monitoring is discussed for single and dual failure scenarios. Performance is enhanced by the application of a modified Bayesian form of MMAE, scalar residual monitoring to reduce ambiguities, automatic dithering where advantageous, and purposeful commands 相似文献
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THEDECENTRALIZEDROBUSTFAULTTOLERANTCONTROLFORTHESENSORFAILUREOFTHEFIGHTERHuShousong;JinLong(Deptof.AuioConirol,NanjingUnivers... 相似文献
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《Aerospace Science and Technology》2006,10(1):49-61
This paper presents a neural-aided controller that enhances the fault tolerant capabilities of a high performance fighter aircraft during the landing phase when subjected to severe winds and failures such as stuck control surfaces. The controller architecture uses a neural controller aiding an existing conventional controller. The neural controller uses a feedback error learning mechanism and employs a dynamic Radial Basis Function neural network called Extended Minimal Resource Allocating Network (EMRAN), which uses only on-line learning and does not need a priori training. The conventional controller is designed using a classical design approach to achieve the desired autonomous landing profile with tight touchdown dispersions called herein as the pillbox. This design is carried out for no failure conditions but with the aircraft being subjected to winds. The failure scenarios considered in this study are: (i) Single faults of either aileron or elevator stuck at certain deflections, and (ii) double fault cases where both the aileron and elevator are stuck at different deflections. Simulation studies indicate that the designed conventional controller has only a limited failure handling ability. However, neural controller augmentation considerably improves the ability to handle large faults and meet the strict touchdown dispersion requirements, thus enlarging the fault-tolerance envelope. 相似文献
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贝叶斯假设理论检测发动机传感器故障 总被引:1,自引:0,他引:1
贝叶斯多重假设检验是将被检测传感器的M个可能状态,作相应M个假设Hi,其先验概率分别为P(Hi)(i=1,2,…,M),故障决策就是从给定观测量M,寻求Hj为真,由贝叶斯风险函数Hi(i=1,2,…,M,i≠j)个假设中的最小值确定最可能发生的假设Hl。 相似文献