沿直线的垂直方向匀速直线运动的带电直线的电场

本文利用相对论变换关系计算了沿直线的垂直方向匀速运动的带电直线的电场.     

基于ARX模型的压电智能结构振动预测控制

采用预测控制法对压电智能结构的振动主动控制进行了实验研究，通过实验方法建立了系统的ARX模型，并在此基础上设计了预测控制器对结构进行振动主动控制。实验结果验证了该方法在压电智能结构振动主动控制中的可行性和有效性。     

基于单片机控制的实验线路故障诊断系统

介绍了基于单片机控制技术的强电实验线路故障自动诊断系统的结构及功能,阐述了系统硬件设计、软件设计的基本原理．     

基于改善空间关联性的混沌控制

基于混沌信号的统计特性，提出了一种通过改善混沌信号的空间关联性实现混沌控制的方法.以Hénon离散混沌系统和四阶Chua's电路超混沌连续系统为例进行了数值研究，验证了这种控制方法的有效性.结果表明， 通过改善混沌信号之间的空间关联性， 混沌系统能以较快的速度收敛到它的平衡点或多种周期轨道. 关键词： 混沌控制 空间关联性 Hénon系统 四阶Chua's电路     

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??We study the linear quadratic optimal stochastic control problem which is jointly driven by Brownian motion and L\'{e}vy processes. We prove that the new affine stochastic differential adjoint equation exists an inverse process by applying the profound section theorem. Applying for the Bellman's principle of quasilinearization and a monotone iterative convergence method, we prove the existence and uniqueness of the solution of the backward Riccati differential equation. Finally, we prove that the optimal feedback control exists, and the value function is composed of the initial value of the solution of the related backward Riccati differential equation and the related adjoint equation.     

高速并行调制解调器中差错控制方法

本文简单介绍了适合高速并行调制解调器中差错控制的Reed-Solomon编码，提出了适合DSP软件实现的一整套的RS码的编码、译码实用方法，并对该编、译码法采用数字信号处理器TMS320C5410b进行仿真，对其性能与传统方法进行了对比。     

不确定非线性系统的鲁棒自适应控制器

在ｂａｃｋｓｔｅｐｐｉｎｇ程序中，把非线性自适应控制和鲁棒控制连接起来，为参数化的严格反馈系统在不确定性存在的情况下，建立了一种鲁棒自适应控制方案．非线性自适应控制被用来处理系统的线性参数化部分，而鲁棒控制通过引进非线性阻尼项被用来处理不确定性部分．与现有的方案不同，作者给出了非线性阻尼项的无限种选择，而不是仅仅一种选择．通过使用一种合适的选择，能够设计一个鲁棒自适应控制器．它不仅能够保证对不确定性的鲁棒性，而且能够使输出误差任意小，以及用较小的控制努力取得较好的性能．     

圆感应同步器数显表检测方法研究

介绍了圆感应同步器数显表测角系统的工作原理,以及在静态和动态测角方法。提出了两种提高动态测量精度的方法。实验数据验证了方法的合理性。     

On a recursive method for the estimation of unknown parameters of partially observed chaotic systems

We investigate a recently proposed method for on-line parameter estimation and synchronization in chaotic systems. This novel technique has been shown effective to estimate a single unknown parameter of a primary chaotic system with known functional form that is only partially observed through a scalar time series. It works by periodically updating the parameter of interest in a secondary system, with the same functional form as the primary one but no explicit coupling between their dynamic variables, in order to minimize a suitably defined cost function. In this paper, we review the basics of the method, and investigate its robustness and new extensions. In particular, we study the performance of the novel technique in the presence of noise (either observational, i.e., an additive contamination of the observed time series, or dynamical, i.e., a random perturbation of the system dynamics) and when there is a mismatch between the primary and secondary systems. Numerical results, including comparisons with other techniques, are presented. Finally, we investigate the extension of the original method to perform the estimation of two unknown parameters and illustrate its effectiveness by means of computer simulations.