基于UKF的低成本SINS/GPS组合导航系统滤波算法

针对MIMU的精度不高,会带来较大的初始对准误差角,如果继续采用传统的小干扰线性方程就会给滤波带来很大误差,甚至发散。针对这个问题,对低成本SINS/GPS组合导航系统建立了基于四元数误差模型的非线性滤波方程,并采用了UKF非线性滤波方法。针对四元数误差模型单纯使用UKF方法无法估计加计零偏和陀螺漂移的问题,提出将UKF和EKF相结合的算法,仿真结果表明,比起扩展卡尔曼滤波以及采用传统小干扰线性方程的卡尔曼滤波,这种方法能够提高姿态误差角特别是方位误差角的估计精度。     

Robust Chattering-Free Finite Time Attitude Tracking Control with Input Saturation

This paper addresses the attitude tracking control problem of a rigid spacecraft in the presence of the modeling uncertainty, external disturbance, and saturated control input by designing two robust attitude tracking controllers. The basic controller is formulated using an integral sliding mode surface which is continuous and provides an asymptotic convergence rate for the closed-loop system. In this case, only the external disturbance with the prior information is considered. Then, to provide a finite time convergence rate and further improve the robustness of the control system under the unknown system uncertainty containing both the modeling uncertainty and external disturbance,a novel integral terminal sliding mode surface(ITSMS) is designed and associated with the continuous adaptive control method. Besides, a command filter is utilized to deal with the immeasurability problem within the proposed ITSMS and an auxiliary system to counteract the input saturation problem. Digital simulations are presented to verify the effectiveness of the proposed controllers.     

控制方向未知的输入受限非线性系统自适应模糊反步控制

针对一类输入受限控制方向未知的非线性系统,提出一种基于Lipschitz条件的自适应模糊反步控制器的设计方法。在控制器的设计过程当中,通过变换系统形式和采用Butterworth低通滤波器解决控制方向未知的问题;采用模糊系统对不确定非线性函数进行在线逼近;利用双曲正切函数和Nussbaum函数对系统输入饱和函数进行处理;将动态面法与反步法相结合解决“计算膨胀”的问题。运用Lyapunov理论分析证明设计的控制律能够使闭环系统所有信号半全局一致有界(semi-globally uniformly ultimately bounded, SGUUB)。该方法的有效性在一类通用的高超声速飞行器的攻角控制仿真中得到了验证。     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

Adaptive Optimization with Periodic Dither Signals

Optimization methods in cyber-physical systems do not involve parameter uncertainties in most existing literature. This paper considers adaptive optimization problems in which searching for optimal solutions and identifying unknown parameters must be performed simultaneously. Due to the dual roles of the input signals on achieving optimization and providing persistent excitation for identification, a fundamental conflict arises. In this paper, a method of adding a small deterministic periodic dither signal to the input is deployed to resolve this conflict and provide sufficient excitation for estimating the unknown parameters. The designing principle of the dither is discussed. Under dithered inputs, the authors show that simultaneous convergence of parameter estimation and optimization can be achieved. Convergence properties and convergence rates of parameter estimation and optimization variable updates are presented under the scenarios of uncertainty-free observations and systems with noisy observation and unmodeled components. The fundamental relationships and tradeoff among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy,and convergence rates are further investigated.     

一类输入受限的不确定非线性系统自适应Backstepping变结构控制

针对一类输入受限的不确定非线性系统,提出了一种自适应Backstepping变结构控制器设计方法。建立了受未知非线性特征约束的执行器故障模型,可以描述系统存在死区、齿隙、饱和、滞回等输入受限情形以及可能发生的执行器失效、卡死等故障情形。设计径向基函数神经网络补偿未建模动态项,引入一阶低通滤波器避免了Backstepping控制中的计算复杂性问题。自适应近似变结构控制能够有效削弱控制信号抖振。理论分析和仿真实验结果证明,提出的自适应鲁棒控制律能够在输入受限的情况下自适应地调节控制输入,使得闭环系统稳定且满足控制性能要求。     

基于二维正交化WLCLMS的自干扰对消方法

广义线性结构广泛用于解决共平台系统中同相/正交相位(in-phase/quadrature-phase, IQ)不平衡引起的自干扰镜像信号残留问题。由于在共轭项维度上的拓展, 输入信号间的相关性使得用于实现权值向量自适应更新的广义线性复数最小均方(widely linear complex least mean square, WLCLMS)算法出现明显的收敛速度下降。针对这一问题, 本文提出了一种二维正交化方法, 通过特征值分解实现输入信号在样本延时以及共轭项两个维度上的去相关。同时建立了包括格形预测器、基于梯度的白化器, 以及数字对消器三级结构的自适应二维正交化WLCLMS模型, 实现了时变环境中自干扰信号的实时跟踪。仿真结果表明, 该方法在有效提升收敛速度的同时, 有着稳定的稳态误差性能, 并且针对系统采样率、滤波器阶数、IQ不平衡量的变化具有良好的鲁棒性。     

Unknown Input Observer Design of Switched Systems Based on Iterative Learning

For a class of discrete switched systems with unknown input, an unknown input observer design method is proposed under the premise of changes along time axis but no changes along iteration axis. This method applies the iterative learning control thought to the design of unknown input observer, constructs the unknown input observer by introducing virtual input signal, and uses the error signal generated from the actual system output and the observer output to correct repetitively the virtual input, which gradually approximates the actual unknown input as the iterations increase.Moreover, the convergence of the observer is strictly proved based on contraction mapping theory, as well as the convergence condition is given. The theoretical analysis indicates that designed unknown input observer can accurately estimate the state and unknown input of the system simultaneously.Simulation example further verifies the effectiveness of the proposed algorithm.     

Observer-based fault detection scheme for a class of discrete time-delay systems

In this contribution, robust fault detection problems for discrete time-delay systems with l2-norm bounded unknown inputs are studied. The basic idea of our study is first to introduce a state-memoryless observer-based fault detection filter (FDF) as the residual generator and then to formulate such a FDF design problem as an Hen optimization problem in the sense of increasing the sensitivity of residual to the faults, while simultaneously enhancing the robustness of residual to unknown input as well as plant input. The main results consist of the formulation of such a residual generation optimization problem, solvability conditions and the derivation of an analytic solution. The residual evaluation problem is also considered, which includes the determination of residual evaluation function and threshold. A numerical example is used to demonstrate the proposed fault detection scheme.     

基于UKF-IMM的双红外机动目标跟踪算法

为了有效解决红外机动目标跟踪精度问题,提出基于UKF的交互式多模型IMM红外机动目标跟踪算法.该方法采用Markov过程描述多个目标模型间的切换,同时导出滤波器输入输出均加权的交互式算法.滤波器采用UKF,避免计算扩展卡尔曼滤波EKF所需的Jacobi矩阵,适用于非线性、非高斯的目标系统模型和观测模型,同时UKF可供多个模型共用,便于软、硬件实现.最后,用双红外探测器对S型机动目标进行仿真实验,给出应用该方法的具体步骤,验证了IMM-UKF的稳定性、有效性和精确性.     

闭环系统的虚拟参考反馈校正控制及迭代最小辨识

在线性框架下研究基于输入输出观测数据对未知系统模型中两反馈控制器的设计问题. 采用虚拟参考反馈校正控制直接设计控制器,无需系统建模过程. 在构造代价函数时,同时考虑闭环传函和灵敏度的模型-匹配问题. 针对模型匹配过程,分别设计虚拟输入和扰动;推导用于观测数据预处理的滤波器. 在设计控制器前,利用概率统计法对控制器进行检验以保证闭环系统的稳定性. 针对控制设计中的参数优化,利用数值优化理论分析迭代最小二乘辨识算法所生成的迭代序列与全局最优值间的逼近式,以此式作为评价收敛性和收敛速度的依据. 最后用仿真算例验证方法的有效性.     

Exponential passive filtering for a class of nonlinear jump systems

The exponential passive filtering problem for a class of nonlinear Markov jump systems with uncertainties and time-delays is studied. The uncertain parameters are assumed unknown but norm bounded, and the nonlinearities satisfy the quadratic condition. Based on the passive filtering theory, the sufficient condition for the existence of the mode-dependent passive filter is given by analyzing the reconstructed observer system. By using the appropriate Lyapunov-Krasovskii function and applying linear matrix inequalities, the design scheme of the passive filter is derived and described as an optimization one. The presented exponential passive filter makes the error dynamic systems exponentially stochastically stable for all the admissible uncertainties, time-delays and nonlinearities, has the better abilities of state tracking and satisfies the given passive norm index. Simulation results demonstrate the validity of the proposed approach.     

非仿射非线性系统的自适应模糊输出反馈控制

针对一类单输入单输出非仿射非线性系统,提出了基于观测器的自适应模糊输出反馈控制。该算法通过构造线性误差观测器估计系统输出误差和系统状态,利用估计状态变量和估计输出误差变量设计自适应控制律,采用模糊逻辑系统在线消除非线性系统中的不确定模型,引入监督控制消除系统逼近误差和外界干扰,并利用Lyapunov方法严格证明了在该控制律作用下闭环系统所有信号一致最终有解,闭环系统的输出能最终跟踪期望输出。仿真结果表明了该方法的有效性。     

基于干扰补偿的拦截弹新型反演姿态控制

针对同时具有未知干扰以及输入饱和与死区特性的大气层内拦截弹姿态控制系统,提出了一种基于干扰补偿的自适应动态面控制器设计方法。该方法通过设计改进的非线性干扰观测器(nonlinear disturbance observer, NDO)对未知干扰进行抑制,利用径向基函数(radial basis function,RBF)神经网络逼近输入饱和引起的非线性项,通过设计参数自适应律在线估计未知死区边界。通过构造合适的Lyapunov函数,证明闭环系统状态一致终结有界。仿真结果表明,所提方法鲁棒性良好,在输入非线性和未知干扰作用下,依然能良好地跟踪指令信号。     

NONPARAMETRIC IDENTIFICATION OF MISO HAMMERSTEIN SYSTEM FROM STRUCTURED DATA

The problem of nonparametric identification of a multivariate nonlinearity in a D-input Hammerstein system is examined.It is demonstrated that if the input measurements are structured,in the sense that there exists some hidden relation between them,i.e.if they are distributed on some（unknown）d-dimensional space M in R^D,d < D,then the system nonlinearity can be recovered at points on M with the convergence rate O(n^-1/（2+d）) dependent on d.This rate is thus faster than the generic rate O(n^-1/（2+D）) achieved by typical nonparametric algorithms and controlled solely by the number of inputs D.     

一种扩展的直接航迹融合方法研究

在分布式多传感器数据融合系统中，各单传感器类型不同情况下的融合问题是一类普遍的问题。本文从直接航迹融合算法的推导过程提出了一种解决这一类问题的方法，包括融合状态设定方法、基于最大似然估计的方法。     

未知转换概率矩阵下的机动目标跟踪算法

机动目标跟踪过程中的转换概率矩阵往往是未知的,系统状态也将呈现非线性、非高斯、不完全观测的特点。传统的方法如交互多模型、广义伪贝叶斯算法等解决该类型问题的效果并不理想。将准贝叶斯法则和辅助粒子滤波算法相结合,提出了一种新的未知转换概率矩阵条件下的机动目标跟踪算法(QB-APF)。仿真结果表明,该算法与其他方法相比具有更高的滤波精度和较好的数值稳定性。     

Multisensor fusion for an experimental airship based on strong tracking filter

1.INTRODUCTION Theinterestinairshipfilledbygasisbooming.It willnotonlyserveasacommunicationrelaystation,butalsobeusedinsurveillance,explorationandmon itoring.Accuratedataacquisitionofposition,velocity andattitudeviaitsmeasurementsystemisindispens abletocontrolitsflyingorkeepingfix point.The measurementsystemshouldbesmallandlight.Ital soshouldhavecertaindynamicsmeasurementability.Usually,DGPSreceiveranddigitalmagnetic compassprovidebasicpositioningandattitudeinfor mationstandalone.DGPS…     

基于自适应未知输入观测器的多故障快速重构

针对一类非线性函数中耦合执行器故障的非线性动态系统, 提出一种基于自适应未知输入观测器的多故障快速重构方法, 通过引入比例项提高故障重构的快速性。首先, 将执行器故障进行解耦处理并构建包含传感器故障的增广系统。然后, 综合H_∞性能指标给出状态估计误差的稳定性证明。接着, 将观测器增益矩阵的求解转化为受线线矩阵不等式约束的非线性优化问题, 并实现执行器故障和传感器故障的多故障重构。最后, 结合单关节柔性机器人算例仿真验证了所提方法的有效性。     

基于PSO-DBSCAN和SCGAN的未知雷达信号处理方法

针对雷达实际侦察过程中会侦收到大量样本库中所没有的未知雷达信号,设计了一种基于粒子群优化的具有噪声的密度聚类算法和半监督条件生成对抗网络的单脉冲未知雷达信号处理方法.通过粒子群优化算法得到具有噪声的密度聚类算法的最优输入参数后,对未知雷达信号进行聚类,在聚类算法输出的簇中采用距离筛选算法筛选出更为可信的样本将其扩展到雷...