一类非线性时滞系统的鲁棒间接自适应控制

针对一类不确定非线性时滞系统,基于变结构控制原理,利用多层神经网络逼近的能力,提出具有投影算法的间接自适应控制方案。该方案通过监督控制器保证闭环系统所有信号有界,并引入综合误差的自适应补偿项来消除建模误差的影响。理论分析证明跟踪误差收敛到零,仿真结果表明该方法有的效性。     

基于Delaunay三角剖分的半径补偿新算法

合理的半径补偿算法能有效提高逆向工程的最终精度.在分析了现有半径补偿算法及其相应优缺点的基础上,针对三角网格法,通过Delaunay三角剖分思想的引入,提出了一种基于Delaunay三角剖分的半径补偿新算法,并对其中三角剖分的优化准则、边界点的处理等关键技术进行了详细的阐述,最后以增压器叶轮为例,实现了叶轮叶面测量数据的半径补偿.     

多普勒频率补偿算法在GPS信号捕获中的应用

在GPS卫星高速运动和地面GPS接收机的移动中,由于卫星和接收机之间的径向速度引起了载波多普勒频移、C/A码的多普勒频移和数据码的多普勒频移,多普勒频移会对GPS接收机的信号捕获产生影响,使其准确度下降。针对上述情况,在常用的相干积分结合非相干积分的GPS信号捕获方案中引入一种多普勒频率补偿算法,并对此算法进行相应的仿真。结果表明该设计方案是可行的,引入的补偿算法能够有效消除多普勒频移对GPS信号捕获的影响,能够实现对GPS卫星信号的多普勒和码相的准确捕获。     

多模型自适应控制的空分系统整体能耗优化

运用多模型自适应控制技术,针对空分系统的整体能耗优化,就空分系统模型集的建立、控制器的设计、切换算法等方面进行了分析。空分系统作为一个实时变化的有机整体,在整体运行过程中存在的操作环境改变对系统的影响,当系统发生变化及波动时,根据相应切换算法切换到可以满足生产变化的模型,采用适合的控制器,以达到实时监控、安全生产的效果,最大限度地优化了系统运行。     

永磁同步电机的自适应反演滑模变结构控制

针对永磁同步电机提出一种基于反演的PMSM自适应滑模控制方案.设计基于反演的滑模变结构位置控制器,通过RBF神经网络实现系统参数变化和外部负载扰动等引起的不确定上界值的在线辨识,减小滑模控制器的控制量,并引入饱和函数来减弱系统的"抖动"现象.理论分析和仿真结果对比表明,基于RBF神经网络的自适应反演滑模控制对参数变化和外部负载扰动具有很好的鲁棒性,永磁同步电动机获得了很好的跟踪效果.     

A new adaptive backstepping Coulomb friction compensator for servo control systems

A new Coulomb friction compensator is proposed for servo control systems in this paper. The novelty of the new approach lies in its capability of assigning the eigenvalues of the resulting closed loop system while attacking the problem. First, based on the standard backstepping methodology, an implicit Lyapunov function, with part of the components being only symbolically constructed at the very beginning, is utilized. To increase the robustness of the system against disturbance and model inaccuracy, an integral term is employed in the design. Using part of the variable gradient method, we are able to turn the implicit Lyapunov function into an explicit one, which is positive definite, and whose time‐derivative is negative definite. Second, it will be shown that the resulting closed loop error system is a switched linear system with two possible active modes that share the same set of eigenvalues, which is at our disposal. Unlike the common adaptive control design methods, such as the Control Lyapunov Function approach, in which the gains are typically positive but otherwise arbitrary, and are hence difficult to choose and have a lack of connection with the system's performance, our new scheme imposes two further constraints on the gains. It turns out that we can then match these gains with the coefficients of the desired characteristic equation of the closed loop system. In this respect, the gains are linked to the system's overall performance, which is a new and very appealing feature for such a scheme. Finally, a procedure of constructing a common Lyapunov function is provided to prove exponential stability of the aforementioned switched linear system. In addition, using the invariance principle, we will show the convergence of the estimated Coulomb friction coefficient to its real value. Numerical simulations are given to validate the effectiveness of the design and its robustness against friction time‐variations. Compared to existing results, the proposed scheme is much simpler, hence, much more advantageous computationally. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

全自动酶免工作站计算机控制系统设计

介绍了酶免工作站软件控制部分的分析和设计。按软件工程的设计思想,归纳出系统的对象,给出系统实现单文档多视和控制类的详细设计。并将一种改进的自适应遗传算法应用于系统执行效率的优化。此开发系统已投入实际试验,从实际使用的效果看,试验执行效率提高了69.9%,血样检测的准确率也达到了99.6%以上,因此,每台酶免仪每天可以多检测120个左右的血样,如果按照现在医院血液检测的指导价,每台酶免仪每年可为医院多创造150万人民币左右的利润。     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

考虑EV充放电意愿的园区综合能源系统双层优化调度

随着电动汽车的普及度越来越高,工业园区的电动汽车用户日益增多,其充放电行为对园区综合能源系统规划和运行带来极大挑战。本文提出了考虑电动汽车充放电意愿的园区综合能源系统双层优化调度。首先基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的电动汽车充放电模型;然后,以园区综合能源系统总成本最小和电动汽车充电费用最小为目标建立双层优化调度模型,通过KKT条件将内层模型转化为外层模型的约束条件,从而快速稳定的实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比了所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的园区综合能源系统双层优化调度的有效性和可行性,具有一定的经济效益。     

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.