自适应比例因子模糊控制器在异步电动机调速系统中的应用

为了降低转子电阻变化对异步电动机矢量控制系统性能的影响，本文把自适应比例因子模糊控制器应用到了矢量控制系统中。介绍了自适应比例因子模糊控制方法的綦本原理，给出了使用自适应比例因子模糊控制器的异步电动机矢量控制系统的结构框图，对主模糊控制器和辅助模糊控制器进行了设计。仿真实验结果表明，采用这种控制方法的调速系统不仅具有优良的动态性能，而且对转子电阻变化具有很强的鲁棒性。     

Linear deterministic adaptive control: fundamental limitations?

This paper is concerned with the achievable performance of adaptive control algorithms. We show that when the only uncertainty is in the form of fixed parameter errors, then there exists an adaptive feedback law whose performance can be made arbitrarily close to that achievable when the system is a priori known. The result is not intended as a practical strategy. Instead, we use it to make the, perhaps obvious, point that meaningful results on performance of adaptive control algorithms must account for non-ideal factors including, at a minimum, noise, parameter time variations and unstructured uncertainty.     

机载红外搜索跟踪系统被动定位滤波算法研究

首先用扩展卡尔曼滤波算法构建了机载红外搜索跟踪系统被动定位滤波模型．然后针对该滤波算法要求先验的噪声统计及存在系统观测模型线性化误差影响滤波精度的特点．利用虚拟噪声技术，提出了适合于红外搜索跟踪系统被动定位的自适应扩展卡尔曼滤波算法。该算法实时地估计了虚拟噪声的统计特性，减小了线性化误差，提高了非线性滤波的精度。仿真结果表明，在完全相同的初始条件下，自适应扩展卡尔曼滤波对目标距离和速度的估计结果明显优于扩展卡尔曼滤波，此算法具有很高的工程应用价值。     

二维卡尔曼─中值混合自适应滤波

本文提出了能同时抑制加性噪声，脉冲噪声干扰和点扩展函数模糊，又能保护图象边缘及细节信息的新型滤波方法—“二维Kalman－Median混合自适应滤波”，并成功地应用于统计图象恢复，取得了满意的效果。     

改善PZT微位移器线性的一种方法

根据锆钛酸铅压电陶瓷的逆压电效应。设计了以８０３１单片机为核心的压电陶瓷堆微位移器提高了ＰＺＴ的线性，从而满足了在天文自适应系统中所要求的精确微位的应用。     

直接斜率波前复原算法的控制效果分析

建立自适应光学系统功率谱抑制函数的概念，分析了采用直接斜率波前复原算法的自适应当光学系统的控制效果，理论分析与６１单元自适应光学系统上的实验结果表明，直接斜率波前复原算法将导致控制效果下降。     

稀布阵综合脉冲孔径雷达旁瓣对消性能分析

定量分析了阵列孔径、干扰信号带宽、通道幅相误差以及Ａ／Ｄ变换器的量化噪声对大型稀布圆形阵列天线干扰对消效果的影响，介绍了稀布阵综合脉冲孔径雷达（ＳＩＡＲ）采用抽头延迟线以提高干扰对消性能的方法，并给出了数值计算与计算机模拟结果。     

智能同步广播激励器及其标频

使用智能同步广播激励器,并采用CCTV传输的1MHz标频信号,就有可能实现自适应相位跟踪同步广播。这将大大节省中波段的频率资源。     

Robust adaptive control of robotic manipulators without the regressor matrix

A new adaptive controller is presented here for rigid-body robotic manipulators. It is stable and robust with respect to a class of external disturbances. The robustness of the adaptive controller is established without the ‘slow-varying’ assumption and the computationally demanding regressor matrix. The control law consists of a non-adaptive PD control part and an adaptive control part. It uses two adaptive matrices to compensate two uniformly bounded coefficient matrices derived from the original dynamics. A α σ|q?|-modified adaptive law is designed to adjust the adaptive matrices. A Lyapunov-type stability analysis indicates that the closed-loop system is uniformly ultimately bounded. The tracking error and compensation error will eventually converge into a closed region, which can be made arbitrarily small by adjusting the controller parameters. Simulation results are included to demonstrate the performance of the proposed controller.