一类不确定仿射非线性系统的动态输出反馈全局镇定

本文研究了有非匹配不确定性的ＳＩＳＯ及ＭＩＭＯ仿射非线性系统的动态输出反馈镇定问题，在要求标准系统为双曲极小相位及系统不确定部分满足一定条件下，构造出了输出反馈形式的动态补偿器，该动态补偿器使相应闭环系统在Ｌｙａｐｕｎｏｖ意义下全局这稳定、数值仿真结果令人满意。     

非线性MIMO系统线性化解耦的一种新方法(Ⅰ)——连续时间系统

给出用神经网络（ Ｎ Ｎ）α阶积分逆系统实现连续非线性 Ｍ Ｉ Ｍ Ｏ 系统线性化解耦的方法。 Ｎ Ｎα阶积分逆系统由一个静态神经网络加若干积分器构成,将其串联在原系统之前,原系统则解耦成若干个相互无关的 Ｓ Ｉ Ｓ Ｏ 伪线性积分系统。理论分析与仿真结果表明,对于精确模型未知的较一般的非线性 Ｍ Ｉ Ｍ Ｏ 系统,所给出的方法均能实现有效的线性化解耦,且结构简单,易于工程实现。     

非线性MIMO系统线性化解耦的一种新方法：连续时间系统

给出用神经网络（ＮＮ）α阶积分逆系统实现连续非线性ＭＩＭＯ系统线性化解耦的方法。ＮＮα阶积分逆系统由一个静态神经网络加若干积分器构成,将其串联在原系统之前,原系统则解耦成若干个相互无关的ＳＩＳＯ伪线性积分系统。理论分析与仿真结果表明,对于精确模型未知的较一般的非线性ＭＩＭＯ系统,所给出的方法均能实现有效的线性化解耦,且结构简单,易于工程实现。     

SPP外围子系统的设计与实现

并行处理机外围子系统的设计和实现技术直接影响整个系统的性能价格比，本文根据ＳＰＰ体系结构的特点和实际应用需要，在前端服务器与ＳＭ／ＳＳＭ之间设计了专用的Ｉ／Ｏ处理机，使得系统Ｉ／Ｏ设备与ＳＭ／ＳＳＭ之间直接进行高速数据传送，从而大大提高系统的Ｉ／Ｏ性能。在Ｉ／Ｏ处理机的设计中，采用了ｉ８６０＋８２３８０＋ＳＲＡＭ的总体结构，从而实现了处理机访问主存和ＤＭＡ控制器访问ＳＲＡＭ之间的并行。     

浅析Windows95的自举与卸载

本文剖析了Ｗｉｎｄｏｗｓ９５的自举过程，指出Ｗｉｎｄｏｗｓ９５自举时不依于ＤＯＳ的实模式内核文件：ＭＳ－ＤＯＳ扣的ＩＯ．ＳＹＳ和 ＭＳＤＯＳ．ＳＹＳ，或者ＩＢＭＤＯＳ的ＩＢＭＢＩＯ．ＣＯＭ和ＩＢＭＤＯＳ．ＣＯＭ和ＩＢＭＤＯＳ．ＣＯＭ。     

基于OSI的CIMS网络管理信息库的与实现

本文根据ＯＳＩ的管理框架提出了一种面向ＣＩＭＳ网络管理的管理信息库的实现结构，它包括使用含树的概念来表示管理信息，利用ＯＳＩ的ＣＭＩＳ来控制对ＭＩＢ的访问，使用文件系统来存放管理信息，文章还介绍了ＭＩＢ实现结构在Ｕｎｉｘ系统的实现。     

腐蚀电场非线性边值问题的边界配置法

腐蚀电场非线性边值问题的边界配置法周汉斌，曹志远（同济大学工程力学系）李光耀（贵州大学计算机科学系）ＡＢＯＵＮＤＡＲＹＤＩＳＰＯＳＩＴＩＯＮＭＥＴＨＯＤＦＯＲＴＨＥＮＯＮＬＩＮＥＡＲＢＯＵＮＤＡＲＹＶＡＬＵＥＰＲＯＢＬＥＭＩＮＴＨＥＣＯＲＲＯＳＩＯＮ...     

企业MIS系统的新探索──OPEN─MIS系统的构想

本文通过对ＭＩＳ系统不同层次的需求分析，提出了ＯＰＥＮ—ＭＩＳ系统的构想。这一构想是通过开放的方法，以求ＭＩＳ系统能够动态地适应管理需求的变化。本文还通过对ＭＩＳ新技术的初步分析，说明了它的可行性。     

BOIF-CIMS实施中的关键技术及解决

该文介绍了ＢＯＩＦ－ＣＩＭＳ工程开展的背景和情况,给出了基于ＰＤＭ构架的ＣＩＭＳ集成体系结构。对ＢＯＩＦ－ＣＩＭＳ开发和实施中的一些关键技术及解决办法进行了讨论,并着重对ＢＯＩＦ－ＣＩＭＳ系统中多应用系统情况下的信息集成技术及数据流进行了阐述。     

UML在分布式系统中的应用与研究

ＵＭＬ（Ｕｎｉｆｉｅｄ　 Ｍｏｄｅｌｉｎｇ 　Ｌａｎｇｕａｇｅ）是一种标准的、功能强大的建模语言。ＩＳＯ ＲＭ－ＯＤＰ（ＩＳＯ开放分布式处理参考模型）为开放、灵活的分布式系统提供了主框架。文章提出了一种用ＵＭＬ为ＯＤＰ系统建模的方法,它以ＯＤＰ的概念和ＵＭＬ的符号为基础。     

T-S模糊广义系统的逼近性

本文研究T-S模糊广义系统的逼近性,给出了T-S模糊广义系统的逼近性定理.证明其可以以任意的精度逼近一类广泛存在的非线性广义系统.还将MISO(多输入单输出)情况推广到MIMO(多输入多输出)的情况.在逼近性定理的基础上,利用神经网络的方法对非线性广义系统建模,给出了神经网络的结构及学习算法.本文共提出了两种神经网路的训练策略,对各自的优点与不足给出了分析,最后用数值例子验证了算法的有效性.     

Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

In this work, we propose a novel iterative learning control algorithm to deal with a class of nonlinear systems with system output constraint requirements and quantization effects on the system control input. Actuator faults have also been considered, which include multiplicative, additive, and stuck actuator faults. To the best of our knowledge, this is the first reported work in the iterative learning control literature to deal with quantization effects for the control input of nonlinear systems under the effects of actuator faults and system output constraints. Under the proposed scheme, using backstepping design and composite energy function approaches in the analysis, we show that uniform convergence of the state tracking errors can be guaranteed over the iteration domain, and the constraint requirement on the system output will not be violated at all time. In the end, a simulation study on a single‐link robot model is presented to demonstrate the effectiveness of the proposed scheme.     

Robust iterative learning control for systems with norm‐bounded uncertainties

A new robust iterative learning control scheme is presented for state tracking control of nonlinear MIMO systems. The main characteristic of the proposed controller lies in its ability to deal with unstructured uncertainties that are norm‐bounded but not globally or locally Lipschitz continuous as usual. The classical resetting condition of iterative learning control is removed and replaced with more practical alignment condition. The class of systems to be considered is further extended to more general scenarios, in which input distribution uncertainties are included. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed control scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

SIMO和MISO系统绝对稳定性问题的一个猜想和几个推论

首先分析了具有多个非线性特性的 SIMO 和 MISO 系统的绝对稳定性问题, 指出应用已知的频域判据来解决上述问题很难奏效. 然后, 基于所有孤立部分传递函数都正实的充分必要条件给出了上述系统为稳定的一个猜想, 当传递函数的零极点都位于虚轴上时, 由这一猜想得到了一个已知的结论; 当传递函数的零极点都位于实轴上时, 由这一猜想得到了一个新的结论, 本文证明该结论是正确的; 最后, 根据这一猜想, 给出了传递函数极点位于复平面的一个例子, 它涉及到一类系数矩阵为时变正定矩阵的振动方程的稳定性问题, 值得去深入研究.     

On Repetitive Learning Control for Periodic Tracking Tasks

In this note, a repetitive learning control (RLC) approach is proposed to deal with periodic tracking tasks for nonlinear dynamical systems with nonparametric uncertainties. We address two fundamental issues associated with the learning control methodology: The existence of the solution, and learning convergence property. Applying the existence theorem of the neutral differential difference equation, and using Lyapunov-Krasovskii functional, the existence of the solution and learning convergence can be proven rigorously. A further extension of the RLC to cascade systems is also explored     

基于一种新模糊模型的非线性系统模糊辨识

提出一种基于新的模糊模型和加权递推最小二乘算法 (WRLSA)的非线性系统模糊辨识方法.新型的具有插值能力的模糊系统可以通过学习从输入输出采样数据中提取MISO系统模糊规则,它继承了Sugeno模型及其变化形式的许多优点.采用相应的模糊隶属函数,使得被辨识的模型可用若干局部线性模型来表示,然后利用WRLSA拟合这些线性模型.给出了详细的模糊辨识算法,为了验证该辨识方法的有效性,还给出了对熟知的Box-Jenkins数据的辨识结果.     

未知相对度非线性系统的一阶D型迭代学习控制

The classical D-type iterative learning control law depends crucially on the relative degree of the controlled system, high order differential iterative learning law must be taken for systems with high order relative degree. It is very difficult to ascertain the relative degree of the controlled system for uncertain nonlinear systems. A first-order D-type iterative learning control design method is presented for a class of nonlinear systems with unknown relative degree based on dummy model in this paper. A dummy model with relative degree 1 is constructed for a class of nonlinear systems with unknown relative degree. A first-order D-type iterative learning control law is designed based on the dummy model, so that the dummy model can track the desired trajectory perfectly, and the controlled system can track the desired trajectory within a certain error. The simulation example demonstrates the feasibility and effectiveness of the presented method.     

Adaptive Iterative Learning Control of Non-uniform Trajectory Tracking for Strict Feedback Nonlinear Time-varying Systems

In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backstepping design technique is used to deal with system dynamics with non-global Lipschitz nonlinearities and the approach proposed in this paper solves the non-uniform trajectory tracking problem. Based on the Lyapunov-like synthesis, the proposed method shows that all signals in the closed-loop system remain bounded over a pre-specified time interval [0, T ]. And perfect non-uniform trajectory tracking of the system output is completed. A typical series is introduced in order to deal with the unknown bound of remainder term. Finally, a simulation example shows the feasibility and effectiveness of the approach.     

Sparse online kernelized actor-critic Learning in reproducing kernel Hilbert space

In this paper, we develop a novel non-parametric online actor-critic reinforcement learning (RL) algorithm to solve optimal regulation problems for a class of continuous-time affine nonlinear dynamical systems. To deal with the value function approximation (VFA) with inherent nonlinear and unknown structure, a reproducing kernel Hilbert space (RKHS)-based kernelized method is designed through online sparsification, where the dictionary size is fixed and consists of updated elements. In addition, the linear independence check condition, i.e., an online criteria, is designed to determine whether the online data should be inserted into the dictionary. The RHKS-based kernelized VFA has a variable structure in accordance with the online data collection, which is different from classical parametric VFA methods with a fixed structure. Furthermore, we develop a sparse online kernelized actor-critic learning RL method to learn the unknown optimal value function and the optimal control policy in an adaptive fashion. The convergence of the presented kernelized actor-critic learning method to the optimum is provided. The boundedness of the closed-loop signals during the online learning phase can be guaranteed. Finally, a simulation example is conducted to demonstrate the effectiveness of the presented kernelized actor-critic learning algorithm.

     

Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

In this paper, both output-feedback iterative learning control (ILC) and repetitive learning control (RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.