基于多尺度主成分分析的图像局部方向估计算法

现有的大多数图像方向估计算法都对噪声非常敏感。因此,提出了一种基于主成分分析（PCA）和多尺度梯度金字塔分解的图像局部方向估计算法,其中主成分分析用于找到局部方向的最大似然（ML）估计。所提出的算法对于噪声图像非常鲁棒。在实验中,通过对模拟图像的和真实图像的方向估计,该算法都可以得到较好的估计效果,对噪声的鲁棒性较强,并且计算速度非常快。     

面向对象的ICA变化检测新方法

通过分析高分辨率影像变化检测方法存在的问题,提出了结合面向对象和非抽样小波变换(undecimated discrete wavelet transform,UDWT)的独立成分分析(Independent Component Analysis,ICA)变化检测新算法。利用面向对象处理方法提取的影像对象特征图作为构建ICA子空间估计输入向量的数据,改善了对噪声抑制的效果,同时,提出了自适应权值的影像对象提取算法,进一步优化了面向对象的处理方法;采用非抽样小波变换进行分块有效克服了现有分块方法带来的ICA子空间估计输入向量尺寸缩减、子空间估计不准确的突出问题。定性定量仿真结果表明:与典型的ICA算法和UDWT算法相比,新算法在高分辨影像变化检测的准确性和鲁棒性方面都得到了很大的改善。     

基于定子电流小波变换的感应电机速度估计算法的研究

提出一种基于齿槽谐波检测的转速估计算法。首先对感应电机定子电流进行小波变换,得到谱后再利用脊线检测算法提取瞬时频率,然后根据齿槽谐波频率估计转速。将该算法在感应电机无速度传感器直接转矩控制系统中进行了仿真,仿真结果表明,使用小波变换的估计算法不受电机参数的影响,明显提高了电机的转速估计精度,并且在整个转速范围内有效,从而也解决的了在低速范围内,转速估计精度低的问题。     

基于Kullback-Leibler与PCA的概率密度比值估计

为了更好地解决在机器学习和数据挖掘等领域中经常遇到的两个概率密度函数的比值估计问题,文中提出了一种新的概率密度比值估计算法.该算法基于Kullback-Leibler距离,综合混合高斯模型和主成分分析的概率密度比值估计方法,使用混合概率主成分分析为两个概率密度比值函数建模.在概率密度比值估计的过程中,不是分别估计比值函数的分子和分母,而是对整个比值函数进行混合组成建模.算法避免了分别对分子分母的概率密度估计,降低了估计的误差.实验表明该算法能够获得较好的估计结果.     

自适应混合多目标分布估计进化算法

针对多目标分布估计算法全局收敛性较弱的缺陷,提出了一种自适应混合多目标分布估计进化算法。其基本思想是:在多目标分布估计算法中引入全局收敛性较强的差分进化算法,当函数变化率较大时,用分布估计算法产生新种群;当函数变化率较小即算法可能陷入局部收敛时,用差分进化算法产生新种群。理论分析和数值实验结果表明,这种混合算法不仅具有良好的全局收敛性,而且解的分布性和均匀性较没有考虑目标函数变化率的混合多目标分布估计算法也有了一定程度的提高。     

面向目标检测的高光谱图像压缩技术

提出了一种基于面向目标检测的高光谱图像压缩算法。该算法利用主成分分析对高光谱图像进行降维,引入虚拟维数对高光谱图像的本征维数进行估计,在估计结果基础上确定降维后的主成分数,并采用SPIHT算法对保留的主成分进行有损压缩。同时,虚拟维数可以实现对图像中端元数目的有效估计,继而采用基于无监督正交子空间投影的端元提取算法提取各目标端元,利用算术编码对目标端元的位置进行无损压缩,解码端可以利用获得的端元位置信息对解压缩后的主成分进行目标检测。实验结果表明,该算法在获得较高压缩性能的同时,能够有效检测出图像中的目标信息。     

永磁同步电机无位置传感器控制研究

位置传感器会增加电机控制系统的成本,降低系统的可靠性和性能,在无法安装位置传感器的电机调速场合或在恶劣环境中难以应用.论文针对永磁同步电机给出一种转子位置实时估计方法,利用反电势得到的位置特征点对转子位置进行估计,实现了无位置传感器永磁同步电机的空间矢量控制.实验结果表明,本文提出的估计算法可以实时估计转子位置和转速,采取该算法对无位置传感器永磁同步电机进行矢量控制,算法易于实现,实时性好,电机控制系统调速范围广、运行稳定.     

基于主成分分析的UWB信号的TOA估计算法

提出了利用主成分分析法估计信号在超宽带(UWB)系统中的波达时间（TOA）的算法。算法先对带有噪声的接收信号的相关矩阵进行主成分分析，求出信号特征值对应的特征向量，然后把接收信号分别投影到上述的各个信号特征向量上，求投影之和。通过设置复杂度和采样频率较低的阈值，比较了主成分分析法和MEP算法的时延估计精度。仿真结果表明，主成分分析法时延估计误差小于12cm的概率大于90%，而MEP算法的概率小于45%。     

利用二次相关改进的广义互相关时延估计算法

为了提高传统广义互相关时延估计算法的性能,本文将二次相关时延估计算法与广义互相关时延估计算法相结合,提出了一种利用二次相关改进广义互相关的时延估计新算法。该算法先对信号进行二次相关处理,利用相关函数抑制了噪声的干扰,然后在相关函数序列上进行权重处理,进一步提高了算法的抗噪声性能与估算精度。仿真实验证明新算法较广义互相关算法性能有了明显的提升,对闪电辐射源定位资料的分析也验证了该算法的有效性。     

正交空时分组码系统的一种新的盲信道估计算法

基于QR分解的信道盲估计方法是一种性能优良的新算法。将该算法推广到正交空时分组码的信道估计中，结合正交空时分组码的特性提出了一种新的信道盲估计算法。与经典的信道盲估计算法相比，新算法的计算量大为降低。同时,Monte Carlo仿真表明，当信噪比较低时，该算法比子空间法具有更好的性能。     

水泥生料磨机成分控制系统的参数估计方法

提出一种新型参数估计算法,以解决水泥生料磨机控制系统输入信号不充分激励”问题,并推导新算法的递推形式。这些结果可直接用于实际系统的设计,仿真结果及实际应用结果证明了新算法的有效性。     

Stability and operating constraints of adaptive LMS-based feedback control

The filtered-X LMS algorithm has enjoyed widespread usage in both adaptive feedforward and feedback controller architectures. For feedforward controller designs the filtered-X LMS algorithm has been shown to exhibit unstable divergence for plant estimation errors in excess of ±90°. Typical implementations of this algorithm in adaptive feedback controllers such as filtered-U and filtered-E have previously been assumed to conform to these same identification constraints. Here we present two instability mechanisms that can arise in filtered-E control that violate the 90° error assumption: feedback loop instabilities and LMS algorithm divergence. Analysis of the adaptive feedback system indicates that the conventionally interpreted plant estimation error can be arbitrarily small yet induce algorithm divergence; while other cases may have very large estimation errors and feedback loops cause controller instability. These analytical observations are supported by simulations. The implications of the actual plant estimation error, calculated here for the filtered-E controller, are extended to practical constraints placed on applications including filtered-U, on-line system identification, and self-excited system control.     

Robust adaptive uniform exact tracking control for uncertain Euler–Lagrange system

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler–Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.     

Variable Upper Bounding Approach for Adaptive-Robust Control in Robot Control

This paper presents a new adaptive-robust control law for robot manipulators with parametric uncertainty. Stability of the uncertain system has been guaranteed using the Lyapunov theory and the control law is derived by means of analytical approach. In this scheme, the manipulator parameters are determined with an estimation law, and both adaptive gain and additional control input are also updated as a function of the estimated value. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, and besides it eliminates the disadvantages of them.     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

无人驾驶车辆稳态漂移的无模型自适应控制

本文引入一种基于无模型自适应控制算法的无人驾驶车辆稳态漂移控制方案.首先,对车辆漂移动态过程进行动力学分析,其次,确定控制系统的控制输入量和控制目标,然后基于无人驾驶车辆稳态漂移控制系统的动态线性化数据模型,设计无模型自适应稳态漂移控制算法、伪雅可比矩阵估计整定算法和伪雅可比矩阵重置算法,从而实现无人驾驶车辆的稳态漂移过程.该无模型自适应控制方法仅使用车辆运行时的输入输出数据,无须进行复杂建模,实现了良好的动态控制结果,具有的良好自适应性.最后,通过数值仿真验证了控制算法的有效性.     

Robust adaptive control of nonlinearly parametrized multivariable systems with unmatched disturbances

A new robust adaptive control scheme is developed for nonlinearly parametrized multivariable systems in the presence of parameter uncertainties and unmatched disturbances. The developed control scheme employs a new integrated framework of a functional bounding technique for handling nonlinearly parametrized system dynamics, an adaptive parameter estimation algorithm for dealing with parameter uncertainties, a nonlinear feedback controller structure for stabilization of interconnected system states, and a robust adaptive control design for accommodating unmatched disturbances. It is proved that such a new robust adaptive control scheme is capable of ensuring the global boundedness and mean convergence of all closed‐loop system signals. A complete simulation study on an air vehicle system with nonlinear parametrization in the presence of an unmatched wind disturbance is conducted, and its results verify the effectiveness of the proposed robust adaptive control scheme.     

Robust adaptive fault tolerant control for a process with actuator faults

This paper studies design and implementation of an enhanced multivariable adaptive control scheme for an uncertain nonlinear process exposed to actuator faults. For adaptive fault compensation, a model reference adaptive control (MRAC) strategy is utilized as main controller. A new adaptation algorithm making possible to improve transient performance of adaptive control is integrated to the controller. With the help of further modifications, some restrictive conditions on multivariable adaptive design are relaxed so that the system requires less plant information. The resulting controller has a simpler structure than the other matrix factorization based controllers. At the final stage of design, a robust adaptive control scheme is obtained with consideration of practical implementation problems such as sensor noises, external disturbances and unmodeled​ system dynamics. It is proved that the controller guarantees closed-loop signal boundedness and asymptotic output tracking. Real-time experiment results acquired from quadruple tank benchmark system are presented in order to exhibit the effectiveness of the proposed scheme.     

灰色预测模糊自适应PID控制的城轨列车智能驾驶系统研究

为提高城轨列车驾驶系统的智能化程度，将灰色预测控制与模糊自适应PID控制技术相结合，应用到城轨列车驾驶系统中。在对城轨列车驾驶系统进行需求分析的基础上，针对其对各种性能指标的要求，提出了基于灰色预测模糊自适应PID控制的城轨列车智能驾驶系统的设计方案。将灰色预测控制与模糊自适应控制应用到经典PID控制中，设计出了系统的智能控制器，有效满足了城轨列车在运行时对跟踪误差、准点率、停车误差、舒适度和节能性等多个性能指标的要求。仿真结果表明：采用灰色预测模糊自适应PID算法的控制系统具有良好的控制精度且系统的智能化程度得到了相应的提升。     

不确定系统的变结构鲁棒自适应控制及其在机械手控制中的应用

本文针对一类不确定非线性系统,通过状态微分同坯变换和反馈控制建立了系统的变结构鲁棒控制设计过程,并在此基础上提出了一种新型的变结构鲁棒自适应控制算法。该算法优点是:1)不需要确切知道系统不确定性,也不需要知道不确定性的界,而是利用自适应规律对系统不确定性范围进行在线估计;2)能够保证系统获得较为满意的动态性能;3)控制规律是连续性的,因而避免了一般变结构系统中的不连续控制所导致的颤振现象。为了说明本文所提算法的正确性,本文还以二连杆机械手为例讨论了其终端夹持不定载荷时的轨迹跟踪问题。