基于神经网络的导弹制导控制一体化反演设计

针对制导控制一体化(IGC)模型中的不确定性难以进行估计补偿的问题,提出了基于神经网络的IGC反演设计方法。首先,根据弹目相对运动关系以及导弹自动驾驶仪模型建立了三维空间中的IGC模型。其次,针对由目标机动引起的模型不确定性,提出应用高阶滑模微分器(SMD)对导弹导引头获得的弹目相对运动信息进行微分,从而估计出目标加速度的方法,然后考虑导弹自身由于参数摄动以及未建模动态引起的模型不确定性,应用SMD和神经网络模型进行在线逼近补偿,基于反演控制理论设计了带有SMD和神经网络模型的IGC算法,应用李雅普诺夫稳定性理论对所设计的控制算法进行了稳定性证明。最后,进行了导弹六自由度仿真,验证了所设计控制算法的有效性。     

Application of neural networks for the design of flight control algorithms. II Adaptive tuning of neural network control law

In this paper, we consider different approaches for the neural network controller tuning in the flight control system. Two of the most common tuning approaches in the adaptive control theory are applied. The first one uses parameter identification technique and consists in solving a real-time regression problem for the control law. The second approach is based on the Lyapunov direct method, which utilizes a tracking error as an absolute measure of tuning performance. The neural network control law are designed for the three-axis flight control problem and tested on the full nonlinear model of a fighter aircraft. Closed loop simulation results are presented and two adaptation algorithms are compared in the case of abrupt change of aircraft dynamics.     

基于模糊理论的微分对策制导律

在现代战场环境下，导弹制导所需信息经常受到各种干扰，有时甚至是不确定的。而基于精确模型的微分对策制导律的实时实现面临着许多困难。为解决这个问题，尝试将模糊理论与微分策制导律相结合，采用多级模糊控制规则，以导弹怀收音机格半为例，给出了模型的建立方法，并探讨了这种模糊微分对策制导律实现的可行性和优点，为将来发展基于微分对策理论的智能制导律提供了一定的思路。     

过虚拟交班点的能量最优制导律

针对固定目标的导弹过虚拟交班点制导问题，在希尔伯特空间下，基于最优化理论设计了有无终端落角约束两种情况下的全局能量最优制导律。通过对模型进行线性化，将提出的最优制导模型转化为线性二次型最优控制问题，在此基础上利用零控脱靶量（ZEM）概念对系统模型进行降阶，并推导出解析解。设计的制导律可以使导弹准确经过虚拟交班点，并实现期望终端落角。仿真结果表明，与经典制导律对比，该制导律可以显著减少全局控制能量消耗。     

Robust guidance for electro-optical missiles

A robust guidance law is presented which renders zero miss distance (ZMD) against deterministically or randomly maneuvering targets for all missile parametric uncertainties. Since the resulting guidance controller is a phase-lead network, it is mainly suitable for systems characterized by moderate glint levels such as electro-optical missiles. The structured uncertainties in missile dynamics are modeled by interval transfer functions. It is first shown that for the nominal case, when the total missile transfer function is positive real, ZMD can be obtained. When uncertainties are considered, the problem becomes design of a guidance controller which renders a family of transfer functions positive real. A new algorithm for the design of such controllers is proposed. An example illustrating a typical design procedure for a nonlinear real-life missile model is given, showing the simplicity and effectiveness of the proposed robust guidance. The main conclusion of this work is that the newly developed guidance law performs well against highly maneuvering targets and may be a suitable alternative to optimal guidance laws in low-glint systems.     

主动防御协同自适应滑模制导律

飞机相比于攻击导弹,在飞行速度和机动性能等方面存在着劣势.为了提高其生存几率,设计了一种发射防御导弹协同对抗攻击导弹的自适应滑模制导律.该制导律本质是一种视线(Line of Sight,LOS)制导方法,通过对飞机与防御导弹和防御导弹与攻击导弹间的LOS调整以满足特定的几何关系,从而实现对攻击导弹的主动防御.最后,将所设计的制导律与比例导引进行了仿真比较.结果表明,由于与目标间的协同,所设计的制导律在机动性能和拦截性能方面具有明显的优势,且对于三方的飞行速度变化与攻击导弹的机动具有较强的鲁棒性.     

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.     

共面变速拦截对策的一个统一的近似反馈解

 本文运用微分对策理论研究了战术导弹与目标飞机之间的共面变速拦截问题,采用强迫奇异摄动方法求得了统一的近似反馈制导控制规律。选用实际空气动力数据的算例表明这个次最优制导控制规律是有效的。     

Saturated super-twisting sliding mode missile guidance

This paper concentrates on developing a missile terminal guidance law against a highly maneuvering target whose maneuvering acceleration is very close to that of the missile or even exceeds the missile normal acceleration in a finite period of time. A new saturated super-twisting algorithm is proposed and applied to the design of missile guidance law. The proposed algorithm has the advantages of simple structure, easy parameter tuning rules and a full utilization of the limit control input. The designed saturated super-twisting sliding mode guidance law is then employed in a missile guidance system. Simulation and its superior performance against strong maneuvering targets is demonstrated.     

Optimal predictive sliding-mode guidance law for intercepting near-space hypersonic maneuvering target

The design of optimal guidance law for intercepting a near-space hypersonic maneuvering target with bounded inputs is considered. Firstly, a maneuvering model for near-space hypersonic aircraft is given. Then, the aircraft acceleration prediction can be obtained using this model with two neural networks. By using the target acceleration prediction, which is taken into account when calculating the Zero Effort Miss(ZEM), an optimal sliding-mode guidance law is proposed to fulfill the guidance task...     

有向拓扑条件下针对机动目标的分布式协同制导律设计

研究了在有向拓扑条件下针对机动目标的分布式协同制导律的设计问题。首先，根据飞行器与目标之间追击过程的几何关系建立制导过程系统模型，针对该模型的非线性问题采用动态反馈线性化方法进行处理。将目标未知机动视为干扰，通过扩张状态观测器进行观测，同时将该估计值用于制导律的设计中，通过直接补偿的方式剔除目标机动对飞行器剩余飞行时间的影响。然后，将所设计的制导律代入到制导模型中，利用一致性分析方法将多飞行器协同制导问题转化为一致性问题，利用极点分析的方法对非一致性子空间的收敛性进行分析，得到协同制导律收敛的充要条件。最后，通过仿真分析的方式对所设计的协同制导律以及制导律的参数选取方法进行分析。     

神经网络及模糊控制在飞机防滑刹车系统中的应用

提出了一种新的神经网络 -模糊逻辑刹车系统控制方案。利用神经网络解决刹车系统中存在的非线性识别问题,实现刹车过程的轨迹跟踪;利用模糊控制克服参数变化和环境因素造成的不利影响,同时避免不精确建模造成的误差,最后利用所设计的控制器对某型飞机进行了仿真研究。结果表明 :新的控制方案能极大的改善飞机的刹车性能。     

用自适应小波神经网络辨识动态实验数据

在气动动态实验中，往往飞行器气动模型是非线性的，很难对动态系统进行正确建模，因此无法得到准确的气动参数值。而采用自适应小波神经网络，无需对该动态系统建模，就可准确地辨识出气动动稳定特性，同时，精度较高、收敛速度较快。采用该方法对某导弹模型风洞自由飞实验结果进行了辨识与动稳定性分析，结果表明用自适用小波神经网络辨识安全可靠。     

基于自适应神经模糊推理系统的导弹直/气复合控制系统设计

针对导弹直/气复合控制问题,提出了一种基于自适应神经模糊推理系统的导弹直/气复合控制系统设计方法.首先,建立了直/气复合导弹数学模型.然后,针对常规模糊控制设计严重依赖经验的问题,在模糊控制的基础上引入神经网络,通过样本数据学习建立自适应神经模糊推理系统(ANFIS),并设计了脉冲调宽调频调制器.最后,通过仿真试验验证了所提的控制系统设计方法.仿真结果表明,基于ANFIS的导弹直/气复合控制系统能够快速精确地跟踪加速度指令.     

基于神经网络的鲁棒制导律设计

 基于神经网络理论对寻的导弹鲁棒制导律进行了优化设计。建立了制导系统非线性运动学方程和鲁棒性能函数,并将鲁棒性能函数转化成了微分对策的极小极大化问题。采用伴随 BP技术,将微分对策的两点边值求解问题转化为 2个神经网络的学习问题,训练后的 2个神经网络分别作为对策双方的最优控制器在线使用,避免了直接求解复杂的鲁棒制导律问题,仿真结果表明了该方法有效性。     

Intelligent modeling and identification of aircraft nonlinear flight

In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.     

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。     

Adaptive block dynamic surface control for integrated missile guidance and autopilot

A novel integrated guidance and autopilot design method is proposed for homing missiles based on the adaptive block dynamic surface control approach. The fully integrated guidance and autopilot model is established by combining the nonlinear missile dynamics with the nonlinear dynamics describing the pursuit situation of a missile and a target in the three-dimensional space. The integrated guidance and autopilot design problem is further converted to a state regulation problem of a time-varying nonlinear system with matched and unmatched uncertainties. A new and simple adaptive block dynamic surface control algorithm is proposed to address such a state regulation problem. The stability of the closed-loop system is proven based on the Lyapunov theory. The six degrees of freedom (6DOF) nonlinear numerical simulation results show that the proposed integrated guidance and autopilot algorithm can ensure the accuracy of target interception and the robust stability of the closed-loop system with respect to the uncertainties in the missile dynamics.     

基于多项式函数求解的落角约束制导律

在攻击坦克、舰艇等特定目标时,需要对导弹的终端落角进行约束,进一步提高战斗部的毁伤效能。针对这一问题,设计了基于多项式函数推导的落角约束制导律。首先在纵向对称平面内建立弹目相对运动学的小扰动线性化模型,利用落角和脱靶量的始端和终端约束条件,推导得到了满足落角约束的制导律的解析表达式。对该制导律进行仿真,仿真结果表明该制导律可以使导弹按照期望落角命中目标。     

高超声速飞行器时间协同再入制导

从饱和打击任务需求出发,针对多高超声速飞行器时间协同再入制导问题进行研究,提出时间可控再入制导律和协同再入制导架构,在改善现有制导律实时性、在线约束管理等性能的基础上,重点解决再入飞行时间不可知、不可控问题,最终实现时间协同再入飞行。协同再入制导结构分为两层,其中底层提出了基于神经网络的时间可控再入制导律,以实现再入飞行时间的可知性与可控性为目标;上层根据不同再入阶段特点设计相应的协调函数,生成时间协调信息。该结构适用于集中式或分布式的通讯结构,同时上层协调策略可以根据任务需要进行有针对性的设计与拓展。最后,通过仿真验证了时间可控再入制导律对时间的可控性和协同再入制导结构的有效性。