用于非高斯系统降维的最小残差熵主元网络

基于最小均方误差的主元分析和主元神经网络是有效的多变量降维统计技术,它们所提取的主元含有系统最大方差.非高斯随机系统的近似模型应当含有系统最大信息熵,但包含最大方差并不一定包含最大信息熵.该文提出一种以最小残差熵为通用指标的非线性主元神经网络模型,并给出了一种基于Parzen窗口密度函数估计的熵近似计算方法和网络学习算法.然后从信息论角度分析了,在高斯随机系统中基于最小残差熵和最小均方差为指标的主元网络学习结果具有一致性.最后以仿真验证该方法的有效性,并与基于最小均方误差的主元分析和主元神经网络方法的计算结果进行对比性分析.     

一种改进的紧格式无模型自适应控制方法

针对一类由一般非线性函数描述的离散时间非线性系统,采用等价动态线性化技术,提出一种改进的紧格式无模型自适应控制(iCF-MFAC)方法.iCF-MFAC方法的自适应控制律包含两项:时变比例控制项和时变积分控制项,与只有一项时变积分项的原CF-MFAC方法相比,iCF-MFAC方法具有更好的通用性和灵活性,并能够提供更好...     

基于Youla参数化的动态随机系统最小误差熵控制

针对具有随机干扰的动态系统,提出一种最小误差熵控制方法。基本思想是应用Youla参数化公式构建具有闭环稳定性的反馈控制策略。其中Renyis熵被作为跟踪误差信息以测度闭环系统的不确定性,Youla参数被优化以使闭环系统误差熵最小,且一个仿真实例也表明了所提算法的有效性。     

Minimum entropy control of closed-loop tracking errors for dynamic stochastic systems

The entropy has been used to characterize the uncertainty of the tracking error for general nonlinear and non-Gaussian stochastic systems. A recursive optimization solution has been developed and the local stability condition of the closed-loop system has been established. The generality of this algorithm has been proved by the special case study of the minimum variance control for linear Gaussian systems.     

UKF算法在星载GPS低轨卫星定轨中的应用

对于扩展卡尔曼滤波在非线性系统中由于线性化过程引入了线性化误差,从而导致滤波器性能下降甚至造成滤波发散的情况,利用Unscented卡尔曼滤波器对非线性系统进行直接滤波,该方法无需对非线性系统进行线性化,避免了线性化误差。并将该算法用于星载GPS低轨卫星定轨中,建立了仿真模型,在初始条件相同的情况下,与EKF算法仿真结果相比较,结果表明在一定观测噪声水平下,UKF定轨结果更准确,定轨精度更高。     

Optimal ensemble control of stochastic time-varying linear systems

We consider the optimal guidance of an ensemble of independent, structurally identical, finite-dimensional stochastic linear systems with variation in system parameters between initial and target states of interest by applying a common control function without the use of feedback. Our exploration of such ensemble control systems is motivated by practical control design problems in which variation in system parameters and stochastic effects must be compensated for when state feedback is unavailable, such as in pulse design for nuclear magnetic resonance spectroscopy and imaging. In this paper, we extend the notion of ensemble control to stochastic linear systems with additive noise and jumps, which we model using white Gaussian noise and Poisson counters, respectively, and investigate the optimal steering problem. In our main result, we prove that the minimum norm solution to a Fredholm integral equation of the first kind provides the optimal control that simultaneously minimizes the mean square error (MSE) and the error in the mean of the terminal state. The optimal controls are generated numerically for several example ensemble control problems, and Monte Carlo simulations are used to illustrate their performance. This work has immediate applications to the control of dynamical systems with parameter dispersion or uncertainty that are subject to additive noise, which are of interest in quantum control, neuroscience, and sensorless robotic manipulation.     

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

The flight dynamics model of air-breathing hypersonic vehicles (AHVs) is highly nonlinear and multivariable coupling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator (LQR) design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping relationship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given probability levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.     

Fault diagnosis and fault-tolerant control for non-Gaussian nonlinear stochastic systems via entropy optimisation

ABSTRACT

In this paper, the fault diagnosis (FD) and fault tolerant control (FTC) problems are studied for non-linear stochastic systems with non-Gaussian disturbance and fault. Unlike classical FD algorithms, the minimum entropy FD is adopted to minimise the residual entropy and control the shape of the probability density function (PDF) of the residual signal. The observation error system can be proved to be locally and ultimately bounded in the mean square sense. Since entropy can be used to characteriSe the uncertainty of the tracking error for non-Gaussian stochastic systems, the FTC controller is obtained by minimising the performance function with regard to the entropy of the tracking error in this paper. The PDF of the output tracking error is approximated by the B-spline model. An illustrative example is utilised to demonstrate the effectiveness of the FD and FTC algorithm, and satisfactory results have been obtained.     

Minimum rational entropy fault tolerant control for non-Gaussian singular stochastic distribution control systems using T-S fuzzy modelling

In this paper, a new fault diagnosis (FD) and fault tolerant control (FTC) algorithm for a non-Gaussian nonlinear singular stochastic distribution control (SDC) system is studied. The rational square-root fuzzy logic model is used to approximate the output probability density function of non-Gaussian processes and a Takagi-Sugeno (T-S) fuzzy model is employed to transform the non-Gaussian nonlinear SDC system into a fuzzy SDC system. An adaptive fuzzy fault diagnosis observer is constructed to achieve reconstruction of system state and fault. Based on the estimated fault information, the controller is reconfigured by minimising the performance index with regard to the rational entropy subjected to mean constraint. Minimum rational entropy fault tolerant control is introduced to make the output of the past-fault SDC system still have the minimum uncertainty. Simulation results are provided to demonstrate the validity of the FD and minimum rational entropy FTC algorithm.     

基于坐标补偿的自动泊车系统无模型自适应控制

针对自动泊车系统,提出了无模型自适应控制(Model-free adaptive control, MFAC)方案.控制方案的设计仅利用泊车系统的前轮转角输入数据和车身角输出数据,不包含车辆模型信息.因此,针对不同车型的自动泊车系统,该方案均能实现无模型自适应控制.为了改善期望轨迹的坐标跟踪误差,进一步提出基于坐标补偿的无模型自适应控制方案,该方案由控制算法、参数估计算法、参数重置算法和坐标补偿算法构成.针对不同车型不同泊车速度的仿真结果表明,基于坐标补偿的MFAC方案和原型MFAC方案均能较好地完成自动泊车过程,且基于坐标补偿的MFAC方案相比原型MFAC方案和PID控制方案,在轨迹坐标和车身角等方面均具有更小的跟踪误差和更快的响应速度.     

Robust-nonsmooth Kalman Filtering for Stochastic Sandwich Systems with Dead-zone

In this paper, a robust-nonsmooth Kalman filtering approach for stochastic sandwich systems with dead-zone is proposed, which can guarantee the variance of filtering error to be upper bounded. In this approach, the stochastic sandwich system with dead-zone is described by a stochastic nonsmooth state-space function. Then, in order to approximate the nonsmooth sandwich system within a bounded region around the equilibrium point, a linearization approach based on nonsmooth optimization is proposed. For handling the model uncertainty caused by linearization and modeling, the robust-nonsmooth Kalman filtering method is proposed for state estimation of the stochastic sandwich system with dead-zones with model uncertainty. Finally, both simulation and experimental examples are presented for evaluating the performance of the proposed filtering scheme.

     

基于PCNN和遗传算法相结合的新型混凝土桥梁裂缝检测方法

针对混凝土桥梁裂缝对比度低、裂缝图像噪声干扰强等难题,提出了基于脉冲耦合神经网络(PCNN)和遗传算法相结合的混凝土桥梁裂缝检测新算法(GA-PCNN)。该算法首先利用遗传算法优化裂缝PCNN模型参数,然后通过改进的最小对数误差适应度函数区分裂缝与背景,当适应度值大小几乎无变化时,停止分割图像,最后通过连通域去噪算法滤除残余噪声,实现裂缝的自动检测。比较GA-PCNN、PCNN和基于熵和动态阈值算法对裂缝图像的分割效果,并绘制PR曲线和ROC曲线评价分割质量,经计算GA-PCNN算法的PR和ROC曲线下面积为90.6%和91.6%,分别高于PCNN算法10.1%和6.8%、基于熵和动态阈值6.5%和6.7%。试验的结果表明：GA-PCNN新算法分割效果好且去噪能力强,该算法能准确地提取混凝土桥梁裂缝特征。     

基于交叉熵的安全Tri-training算法

半监督学习方法通过少量标记数据和大量未标记数据来提升学习性能.Tri-training是一种经典的基于分歧的半监督学习方法,但在学习过程中可能产生标记噪声问题.为了减少Tri-training中的标记噪声对未标记数据的预测偏差,学习到更好的半监督分类模型,用交叉熵代替错误率以更好地反映模型预估结果和真实分布之间的差距,并结合凸优化方法来达到降低标记噪声的目的,保证模型效果.在此基础上,分别提出了一种基于交叉熵的Tri-training算法、一个安全的Tri-training算法,以及一种基于交叉熵的安全Tri-training算法.在UCI(University of California Irvine)机器学习库等基准数据集上验证了所提方法的有效性,并利用显著性检验从统计学的角度进一步验证了方法的性能.实验结果表明,提出的半监督学习方法在分类性能方面优于传统的Tri-training算法,其中基于交叉熵的安全Tri-training算法拥有更高的分类性能和泛化能力.     

卫星姿态的状态转移控制

本文面向卫星的应用需求,对卫星姿态的运动学和动力学进行了分析与建模.利用反馈线性化,将姿态运动的高阶非线性项包含在姿态控制中,通过局部动态线性化,将动力学系统近似为定常系统.通过幂级数法对系统进行了状态转移过程的求解.采用模型预测的方法获得姿态角和姿态角速度的预期偏差.通过广义逆变换构造关于偏差的最小范数、最小二乘控制器.提出了一种基于状态转移的卫星姿态机动、跟踪与稳定控制的新方法.控制器的参数具有根据系统采样周期和当前状态时变自适应的特点.考虑帆板挠性及多种偏差和噪声影响,仿真验证了方法的可行性和有效性.     

Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

On minimal error entropy stochastic approximation

Information-theoretic concepts are developed and employed to obtain conditions for a minimax error entropy stochastic approximation algorithm to estimate the state of a non-linear discrete time system baaed on noisy linear measurements of the state. Two recursive suboptimal error entropy estimation procedures are presented along with an upper bound formula for the resulting error entropy. A simple example is utilized to compare the optimal and suboptimal error entropy estimators and the minimum mean Square error linear estimator.     

基于数据驱动的控制器综合性能优化

为了提高过程回路的控制性能,提出一种基于数据驱动的控制器综合性能优化方法.首先,通过分析虚拟参考反馈校正方法与内模控制器设计过程的联系,确定数据驱动控制器校正所需参考模型的结构;然后,定义一种结合绝对误差积分和最小方差性能基准的综合性能指标,再基于该指标确定参考模型的未决参数;最后,利用控制系统闭环数据实现对控制器参数...     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

大时滞网络自适应预测PI主动队列管理算法

针对网络中存在的大时滞和网络参数时变问题,提出一种自适应预测PI主动队列管理算法．将Smith预估器与达林算法相结合,既克服了大时滞带来的不利影响,也减少了控制器参数整定数量．利用网络参数与控制参数所具有的确定关系,通过在线估计网络参数来实时调节控制参数,使得控制器能够适应网络参数的变化,同时采用线性化方法分析了系统局部稳定性．仿真结果表明,所提出的算法是可行而有效的。