Synchronization of chaos using radial basis functions neural networks

The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.     

The Maximum Likelihood Method of Aerodynamic Parameter Identification

In this paper a method of aerodynamic parameter identification of vehicle, the maximum likelihood method, is introduced. The aerodynamic model of vehicle is identified and the basic equations using maximum likelihood method are established. After that, the simulation data is identified to verify the correctness of the mathematic model and identification method. Last, the practical flight data is identified and analyzed.     

Parameter estimation of LFM signals based on time reversal

In this paper, parameter estimation of linear frequency modulation(LFM) signals containing additive white Gaussian noise is studied. Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR) threshold, a parameter estimation method for LFM signals based on time reversal is proposed. The proposed method avoids SNR loss in the process of estimating the frequency, thus reducing the SNR threshold. The simulat...     

Modelling of a Class of Nonstationary Time Series with Kalman Filter Using Moving Window

In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalmnan filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimation during recursive process by using LSM, the state space equation is constructed, then the Kahnan filter using moving window is made to get the data with reduced level of observation noise. Finally, the precise parameter estimation can be obtained by using the LSM again. The algorithm is carried on recursively. Good results for estimating and forecasting are shown by simulation, examples. The algorithm of Kalman filter using moving window proposed by us is introduced in this paper, which can guarantee the precision and convergence of Kalman filter.     

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.     

Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.     

Basic Relations Type of System Dynamics DELAY_N Input-Output Under the Discrete State and Its Application

DELAY_N is a kind of important function in system dynamic. Studies about the basic relational type between its input and output under the discrete state have the significant influence and instruction in determination system dynamics model parameter, in debugging model, and in analyzing result. The article has obtained the DELAY_N inputoutput relational expressions, has obtained the input average delay time and the delay variance expression, in this foundution, proposed one method of determination delay functiontype.     

Vector tracking loops in GNSS receivers for dynamic weak signals

Vehicle positioning with the global navigation satellite system(GNSS) in urban environments faces two problems which are attenuation and dynamic.For traditional GNSS receivers hardly able to track dynamic weak signals,the coupling between all visible satellite signals is ignored in the absence of navigation state feedback,and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators.The vector delay/frequency locked loop(VDFLL) with navigation state feedback and the joint vector tracking loop(JVTL) with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation(MLE) are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments.A joint vector position tracking loop(JVPTL) directly tracking user position and velocity is proposed to further improve tracking sensitivity.The coherent navigation parameter discriminator of JVPTL,being able to ease the contradiction between thermal noise error and dynamic stress threshold,is based on MLE according to the navigation parameter based linear model of received baseband signals.Simulation results show that JVPTL,which combines the advantages of both VDFLL and JVTL,performs better than both VDFLL and JVTL in dynamic weak signal environments.     

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

Mountain cities are complex asymmetric dynamic network architectures, and the flight of UAVs in this environment is subject to various constraints, while efficiency is a crucial factor in the trajectory planning of police UAVs, which need to maintain high efficiency and safe flight paths between their starting and ending points, but the traditional trajectory planning method cannot meet the requirements of rapid maneuvering of police UAVs. To achieve this, a 3D terrain map is built,an objective ...     

Duffing混沌振子用于微弱信号检测的研究

针对频率已知的微弱正弦信号幅度和相位检测问题,提出了一种采用Duffing混沌振子作为检测系统的方法。该方法将被测信号作为系统的参数,利用系统参数在临界值附近微小的变化会改变系统输出状态的特性,通过识别系统输出的相轨迹判断输出状态是否改变,根据状态改变所需满足的条件估计信号的参数。给出了实现该方法的主要步骤,并将其运用于高频地波雷达海洋回波信号的检测,与FFT方法所得结果相比,回波多普勒谱的信噪比可提高5 dB,表明了该方法具有更好的检测性能。     

基于Backstepping的飞行仿真转台自适应摩擦补偿控制

论述了基于backstepping的飞行仿真转台自适应摩擦补偿控制。在实验的基础上,提出了飞行仿真转台的动态模型,其中包括动态LuGre摩擦模型。针对此动态模型推导出包括自适应摩擦补偿的backstepping鲁棒控制器。仿真实验证明:闭环系统的输出可以达到渐进跟踪给定的位置参考信号和速度参考信号,克服了转台在使用传统的PID控制器时,其位置波形存在平顶,速度波形存在畸变的缺点。     

基于T-S模型的约束鲁棒保性能飞行控制律设计

对一类具有范数有界参数不确定Takagi-Sugeno (T-S)模糊模型系统,采用状态反馈并行分配补偿结构,综合考虑变量约束和多个性能指标,研究了约束鲁棒最优保性能飞行控制律设计问题。改进输入、输出及状态约束线性矩阵不等式,提出了带约束模糊最优保性能控制定理。在一般抗干扰控制基础上引入不确定性矩阵,证明了模糊鲁棒最优抗干扰定理。综合上述结论,推导出带约束模糊最优保性能抗干扰定理。最后,建立了飞机不确定T-S模糊模型,分别设计了满足上述3个定理的飞行控制律,并进行了仿真与对比分析,结果验证了该设计方法的有效性。     

GP-RBF网络应用于飞行器外测数据野值实时剔除

测量数据集合中严重偏离大部分数据所呈现趋势的小部分数据点被称为野值点。野值的实时剔除对提高目标实时跟踪精度有十分重要的意义。采用了一种新的含有广义参数的径向基函数神经网络(GP-RBF网络)对航天靶场外测数据进行野值实时剔除。该网络可以通过对参数的自适应调整达到实时剔除野值的目的。采用经差商预处理后的飞行器弹道仿真数据，构成GP_RBF网络的训练集和测试集，试验结果表明利用该网络可以快速、有效地解决野值实时剔除问题。     

基于干扰估计的高超声速飞行器鲁棒控制方法

针对高超声速飞行器高度与速度跟踪所面临的参数不确定与外界干扰问题,提出基于干扰估计的鲁棒控制律设计方法。首先建立含参数不确定与外界干扰的高超声速飞行器模型;而后基于动态逆思想,将动力学模型进行线性化,并将由参数不确定与外界干扰对系统造成的总效果看作系统总干扰,从而引入线性扩张状态观测器实现对系统状态及总干扰的估计;最后考虑总干扰作用,提出具有鲁棒性能的动态逆控制律与滑模控制律。通过与传统控制律进行对比仿真,结果表明所设计的两种鲁棒控制器只需在原有方法基础上进行简单改进,即可较大程度提高对系统参数不确定与外界干扰的鲁棒性,具有良好的跟踪性能。     

一类参数不确定非线性系统的故障检测与重构

飞行器在全包络上表现出明显的气动参数不确定性,以某无人机纵向模型为研究对象,提出一种不确定参数在线估计的自适应观测器故障重构方法。首先,将系统状态方程描述为一类带时变参数的仿射非线性结构,在参数增广系统能观性分析基础上,采用增广容积卡尔曼滤波(augmented cubature Kalman filter, ACKF)算法实现气动参数在线估计,以克服鲁棒性死区故障检测方法的保守性,提高检测灵敏度。其次,将所估计参数用于自适应观测器设计,由于Lie导数分析方法保证了对象系统的能观性,故系统不必满足文献方法中的特定规范形式;在此基础上,给出了故障检测自适应阈值和故障参数调节律,并分析了估计误差的收敛性。仿真实验表明了所提方法的有效性。     

考虑响应共变特性的多响应稳健参数设计

针对响应共变特性的稳健参数设计问题,在多任务高斯过程(multi-task Gaussian processes,MTGP)建模框架下,结合质量损失函数和考虑响应不确定性的优化函数构建了一个考虑输出响应不确定性的MTGP(uncertainty of MTGP,UNMTGP)优化模型。首先,利用MTGP模型拟合实验数据,构建考虑响应间共变特性对优化结果影响的多元高斯模型。其次,提出考虑输出响应不确定性的优化目标函数,构建多响应稳健优化模型。最后,结合全局优化方法,获得最优参数设计。此外,结合真实案例,利用质量损失函数的相关评价指标,论证所提方法的有效性。结果表明,所提方法考虑了响应共变特性和输出响应不确定性对优化结果的影响,有效改善了模型的预测质量,提升了输出响应的稳健性。     

结冰对飞机飞行动力学特性影响的仿真研究

采用一种计算飞机结冰后气动系数和气动导数的工程方法,引入几个结冰参数,建立结冰后飞机飞行动力学模型。根据这些参数计算了飞机结冰后阻力系数以及典型气动导数的变化,并计算了结冰前后飞机的飞行包线;同时对结冰前后飞机的纵向、横侧动稳定性以及升降舵、副翼和方向舵阶跃操纵响应进行仿真计算。这种方法除了简单、物理概念明显、耗费少之外,采取将外界结冰条件与飞机本体分开处理的思路,具有广泛的适应性。结冰参数值随时间变化,可为实时监控结冰影响提供数据。     

动态面过失速机动飞行控制律的设计

提出了一种基于B-spline网络和动态面控制方法的过失速机动飞行控制律设计方法。针对飞机气动力和力矩的非线性和不确定性,引入具有学习和记忆功能的B-spline网络,自适应逼近飞机的气动力和力矩,加快参数收敛速度,改善系统的过渡过程性能。动态面飞行控制律消除了Backstepping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。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毕竟有界,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。大迎角过失速机动飞行的数值仿真验证了方法的有效性。