基于神经网络自适应动态逆的结冰飞机飞行安全边界保护方法

考虑到飞机带冰飞行的安全问题，对结冰飞机进行安全边界保护成为一种有效的解决手段。基于神经网络自适应动态逆跟踪性能好、鲁棒性强的优点，提出了以安全关键飞行参数限制值作为神经网络自适应动态逆的输入，获取可用舵面偏转角的边界保护方法。建立了飞机本体动力学模型，采用高精度的数值模拟方法获得结冰数据库。设计了神经网络自适应动态逆控制律，通过在动态逆环节引入单隐层神经网络，对不确定性逆误差进行自适应补偿，增强了控制系统的鲁棒性。以俯仰姿态保持模式为例设计了结冰飞行闭环安全边界保护系统。以结冰飞机最小平飞速度的估算值作为飞机最低飞行速度，设计自动油门控制系统，实现对飞行速度的保护。通过仿真验证了设计的控制律具有较强的鲁棒性。对结冰严重程度线性增加情形下飞机状态参数的动态响应进行了分析。仿真结果表明，所设计的结冰边界保护系统，能够实现飞机在容冰飞行过程中对安全关键参数如迎角、飞行速度的实时保护。     

Application of neural networks for synthesizing flight control algorithms. I neural network inverse dynamics method for aircraft flight control

The application of artificial neural networks for aircraft motion control, in particular, for creation of nonlinear algorithms of the aircraft remote control system (RCS) is considered. Aircraft as a control object is represented as a multidimensional nonlinear dynamic system and nonlinear control methods are used to operate this system. The control loop is constructed using the method of inverse dynamics based on the feedback linearization principle. The nonlinear control law is represented as a neural network being learned (adjusted) by recorded or incoming measurements of motion parameters. Synthesis and testing of neural network control algorithms is performed with the fully nonlinear mathematical model of a maneuverable aircraft for three control channels. Simulation results of the closed system are presented.     

战斗机非线性飞行控制技术的研究与发展

战斗机非线性飞行控制技术研究综述。首先介绍战斗机技术发展走向,然后对当前主要战斗机飞行控制设计方案进行分析,并重点介绍神经网络在飞行控制中的应用研究。最后指出,基于神经网络的智能控制方案作为飞行控制的重要研究方向,将为未来先进战斗机飞行控制系统设计提供重要的解决方案。     

基于神经网络的自动着陆飞行鲁棒自适应逆控制

针对飞机自动着陆飞行提出了基于神经网络的鲁棒自适应非线性动态逆控制器设计方案。首先采用非线性动态逆方法设计着陆飞行的基本控制律,再利用多层感知器神经网络设计适当的权值调整规则使其能够自适应地逼近和补偿逆误差。仿真结果表明,所设计的飞行控制系统是有效的,系统能够克服动态逆误差对着陆飞行控制带来的不利影响。     

某型飞机无动力投放模型自由飞控制律设计

针对某型飞机无动力投放模型自由飞试验中,由于受载机螺旋桨滑流影响,模型与载机分离后发生滚转、难以保证水平飞行的问题,根据模型自由飞试验不同阶段的飞行特点开发了专用控制律,具备直接链、投放控制、失速尾旋3种控制模态。论述了控制律的设计过程,以及在模型自由飞试验中的应用情况。试飞结果表明,该控制律很好地解决了模型与载机安全分离和大迎角水平飞行控制问题,提高了模型试验动作精准度。     

基于神经网络的超机动飞机自适应重构控制

 讨论了一种基于神经网络的超机动飞机直接自适应重构控制方法。飞机的基本控制律采用非线性动态逆方法设计,对于模型不准确和舵面故障等因素导致的逆误差采用神经网络进行在线补偿。通过仿真表明,在飞机发生舵面故障时,神经网络通过自适应地补偿逆误差,可以快速在线重构控制律,保持飞机稳定和一定的操纵品质。     

一种动态面神经网络鲁棒飞行控制律设计

提出一种基于RBF(Radial Based Function)神经网络和动态面控制的飞行控制律设计方法。针对飞机气动参数变化引起的非线性和不确定性,通过RBF神经网络在线逼近。动态面飞行控制律消除了反推设计方法中由于对虚拟控制反复求导而导致的复杂性问题。同时,在控制律设计中引入一个鲁棒因子项来补偿外界干扰和神经网络的逼近误差,提高了系统的鲁棒性,使整个系统获得更好的跟踪控制性能。基于Lyapunov稳定性定理证明了闭环系统的所有信号半全局一致终结有界,并且通过适当选择设计参数,跟踪误差可收敛到原点的一个小邻域内。最后,通过飞机俯仰运动飞行的数值仿真验证了该方法的有效性。     

基于标准特征多项式的横侧向飞控系统设计

采用基于标准特征多项式的飞控系统设计方法设计了某型飞机横侧向通道倾斜姿态保持和航向保持控制律,并进行了仿真研究。控制律的设计过程和仿真结果表明,该方法可以确切地给出系统的过渡过程时间和超调量,避免了任意选择初始参数值的盲目性和经验性,大大简化了飞控系统设计的工作量。     

弹道导弹飞行时间的BP神经网络控制方法

时间协同攻击是提高弹道导弹突防能力和作战效能的一种重要手段。由于发动机推力偏差等干扰因素造成导弹的飞行时间偏差较大,为精确控制弹道导弹的飞行时间,运用BP神经网络算法,建立了一种适用于较大飞行时间偏差修正的飞行时间调控模型。该模型用视加速度偏差表征推力偏差等干扰的影响,根据视加速度偏差调整导弹飞行中某一时段的程序角来实现对飞行时间的精确控制。由BP神经网络建立满足飞行时间偏差要求下视加速度与飞行程序角速率、转弯结束时间之间的映射关系。导弹实际飞行时,根据实际视加速度,由BP神经网络在线计算出飞行程序角速率及转弯结束时间,使导弹在干扰条件下仍以预定飞行时间到达目标。最后,通过仿真验证了该模型的有效性和可行性。     

Neural networks in nonlinear aircraft flight control

This paper describes an approach for incorporating a neural network with real-time learning capability in a flight control architecture. The architecture is also applicable, in general, for the control of processes described by nonlinear differential equations of motion in which there exists a control for each degree of freedom. The main features are that the defining equations of motion for the process to be controlled are poorly known with respect to their functional forms, and that the functional forms, themselves, may undergo sudden and unexpected variation. It is well known that such systems are difficult to control, particularly when the effect of the control action enters nonlinearly. Numerical results based on 6DOF simulations of a high performance aircraft are presented to illustrate the potential benefits of incorporating neural networks as a part of a flight control system architecture     

Transition control of a tail-sitter unmanned aerial vehicle with L1 neural network adaptive control

The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV) during the transition process. Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions, large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process, which leads to control prec...     

Neural and fuzzy reconstructors for the virtual flight datarecorder

The results are presented of a comparative study evaluating the performance of neural network (NN) and fuzzy logic reconstructors (FLRs) for the development of a virtual flight data recorder (VFDK). Typical flight data recorders (FDRS) on commercial airliners do not record the aircraft control surface deflections. These dynamic parameters are critical in the investigation of an accident or an uncommanded maneuver. The results are shown relative to a VFDR based on a neural network simulator (NNS) along with a neural network reconstructor (NNR) or a FLR The NNS is trained off-line, using available flight data for the particular aircraft, for the purpose of simulating any desired dynamic output recorded in current FDRs. The NNS is then interfaced with the NNR or with the FLR. The output of the two reconstructors are the control surface deflections which minimize a performance index based on the differences between the available data from the FDR and the output from the NNS. The study tested with night data from a B737-300 shows that both schemes, the one with the NNR and the one with the FLR, provide accurate reconstructions of the control surface deflections time histories     

临近空间动能拦截器神经反演姿态控制器设计

为满足临近空间动能拦截器姿态控制快速性、准确性和鲁棒性的要求,设计了一种自适应神经反演姿态控制器。首先,建立了姿控发动机侧喷干扰模型,并推导了包含质心漂移、参数摄动和外界干扰的三通道强耦合模型;其次,设计了自适应神经反演姿态控制器,为提高控制精度,采用径向基函数（RBF）神经网络对各个通道的不确定项进行估计和补偿,并基于最小学习参数的思想,将神经网络学习参数拟合为一个参数,提高了RBF计算效率,保证了估计的实时性。最后,采用伪速率（PSR）脉冲调制器将设计的连续控制律转化为脉冲控制律,实现了拦截器的变推力控制,并克服了脉冲脉宽调制（PWPF）调制器相位滞后问题。数字仿真表明,所设计的控制器收敛速度快,控制精度高,对强扰动具有鲁棒性。     

飞行控制律对体自由度颤振特性影响试验

飞翼飞机易发生刚体短周期模态与机翼低阶弯曲模态耦合所致的体自由度颤振。飞行控制系统对飞机的短周期模态特性影响很大,因此考虑飞行控制系统的闭环体自由度颤振特性值得进一步研究。针对自主设计的颤振模型开发了相应的俯仰姿态保持控制律,综合运用风洞试验和仿真计算开展了相关研究,获得了不同刚体自由边界条件下的开环/闭环体自由度颤振特性,研究了闭环增益对体自由度颤振特性的影响规律,简要分析了影响机理。试验和仿真计算结果共同表明：俯仰姿态保持控制律明显地改变了俯仰模态阻尼的原有走势,闭环后的体自由度颤振特性变化明显。以开环颤振速度为基准,采用较小的比例回路增益K_P或较大的微分回路增益K_D,飞行控制律能增加飞行器俯仰阻尼,提高体自由度颤振速度,反之飞行控制律将导致颤振速度降低。就本文控制律而言,当K_P<0.07或K_D>0.2时俯仰姿态保持控制律能起到抑制体自由度颤振的作用。     

变体飞行器控制系统综述

介绍了变体飞行器控制系统和涉及的控制理论问题。分析了变体飞行器的控制系统,指出变体飞行器的控制系统由变形控制层和飞行控制层组成。对变体飞行器的硬件结构和变体飞行器控制方法的研究现状进行了阐述。分析了集中式和分布式两种变形机械结构以及控制系统体系结构,提出采用总线网络连接变形结构的分布式元件。总结了变体飞行器需深入研究的变形控制和飞行控制问题,包括大尺度变体飞行器的飞行控制问题,通信受约束的大数目的驱动器的协调控制问题。     

基于鲁棒伺服LQR的结冰飞机纵向控制律重构方法

由于防/除冰系统总会出现故障或者除冰不彻底,因此仅依靠防/除冰系统实现结冰条件下的安全飞行并非完全可靠,研究结冰后飞机控制律重构对飞机操纵安全和飞行安全极其重要。针对飞机的纵向运动建立了结冰影响模型和纵向动力学模型,采用鲁棒伺服线性二次型调节器(LQR)最优控制设计了飞机结冰后空中飞行纵向控制律,模拟了飞机在俯仰姿态保持模式下遭遇不同严重程度结冰后的动态响应特性,并与常规PID控制进行对比。结果表明,所设计的控制律能够有效改善结冰飞机的飞行性能和飞行品质,准确跟踪给定的俯仰角指令,且抗干扰能力、动态性能以及鲁棒性均优于常规PID控制。为飞机结冰后的重构控制问题和自动飞行控制,提供了新的思路。     

基于QFT的鲁棒飞行控制器设计

首先对定量反馈控制理论及其发展进行了介绍,并指出了其所能解决的问题,以及器的一般求解步骤,按着讲述了鲁棒控制中的灵敏度设计方法,讨论了基于ＱＦＴ的鲁棒灵敏度设计思路,最后以某型号飞机为算例,采用ＱＦＴ方法设计了鲁棒控制律,计算和仿真结果说明了定量反馈控制理论是一种设计鲁棒飞行控制律的有效方法。     

模糊自适应过失速飞行控制

研究了带推力矢量飞机过失速非线性飞机控制问题。根据奇异摄动理论将受控变量分为快变量和慢变量，然后分别对内环和外环进行设计，外环利用滑动面控制进行设计，内环利用模糊逻辑系统的万能特性来抵消近似非线性动态逆所带来的误差，根据李亚谱诺夫稳定性理论了模糊系统权值的自适应调整规律，从而保证闭环系统的稳定性。数字仿真结果表明：在存在较大的模型不确定性以及飞机动力学对状态和控制输入均为非线性的情况下，该控制方案能对指令进行良好的跟踪。     

A methodology for multi-objective design assessment and flight control synthesis tuning

Flight control law design is a multi-variable control problem where various strict requirements from multiple disciplines have to be satisfied. This paper describes quality function deployment and its use in multi-objective control synthesis tuning and design assessment, with application to flight control design. The main feature of this methodology is that the various kinds of design objectives can be taken into account in their most natural form and design alternatives can be assessed most visibly with respect to given requirements. Multi-objective synthesis tuning by min-max parameter optimisation allows interactive compromising in the set of what can be best-possibly achieved with a chosen control law structure.     

Design of safety monitor schemes for a fault tolerant flight control system

For a research aircraft, "conventional" control laws (CLs) are implemented on a "baseline" flight computer (FC) while research CLs are typically housed on a dedicated research computer. Therefore, for an experimental aircraft used to test specific fault tolerant flight control systems, a safety logic scheme is needed to ensure a safe transition from conventional to research CLs (while at nominal conditions) as well as from research CLs at nominal conditions to conditions with "simulated" failures on specific control surfaces. This paper describes the design of such a safety scheme for the NASA Intelligent Flight Control System (IFCS) F-15 Program. The goals of the IFCS F-15 program are to investigate the performance of a set of fault tolerant CLs based on the use of dynamic inversion with neural augmentation. The different transitions are monitored using information relative to flight conditions and controller-related performance criteria. The testing of the scheme is performed with a Simulink-based flight simulation code and interface developed at West Virginia University for the NASA IFCS F-15 aircraft.