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A novel hierarchical intelligent controller configuration is proposed using an artificial neural network as a control-mode classifier in the supervisory level and a set of pre-designed controllers in the lower level. Controller outputs are modified nonlinearly by the classifying signals in a structure resembling one artificial neuron with adaptively changed weights. The lower-level local controllers are implemented using neural networks. An illustrative example of this approach is based on the transient stabilization of a single-machine infinite-bus system studied in Flexible AC Transmission Systems (FACTS) research. 相似文献
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Many well studied classes of dynamical systems such as actuator‐constrained linear systems and dynamic artificial neural networks can be written as discrete‐time Luré systems with sector‐bounded and/or slope‐restricted nonlinearities. Two types of observer‐based output feedback control design methods are presented, compared, and analyzed with regard to robustness to model uncertainties and insensitivity to output disturbances. The controller designs are formulated in terms of LMIs that are solvable with standard software. The design equations are illustrated in numerical examples. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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基于神经网络非线性系统辨识和控制的研究 总被引:12,自引:0,他引:12
本文提出了由静态的前馈网络和稳定的滤波器构成的非线性系统的辨识模型,在神经网络固有的逼近误差存在的情况下,从理论上讨论了神经网络应用于辨识控制过程中系统的稳定性问题,最后研究了在非线性系统的轨迹跟踪过程中增加滑动控制来偿神经网络的逼近误差,从而提高系统跟踪性能。 相似文献
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基于神经网络的一类非线性系统自适应H∞控制 总被引:6,自引:0,他引:6
基于神经网络提出一种自适应H∞控制方法。控制器由等效控制器和H∞控制器两部分组成,用神经网络逼近未知非线性函数,H∞控制器用于减弱外部及神经网络逼近误差对跟踪误差的影响。所设计的控制器不仅保证了闭环控制系统的稳定性,而且使外部干扰及神经网络逼近误差对跟踪误差的影响减小到预定的性能指标。 相似文献
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基于神经网络的严反馈块非线性系统的鲁棒控制 总被引:9,自引:0,他引:9
针对非匹配不确定性的严反馈块非线性系统,基于神经网络提出一种鲁棒控制方法.利用Lyapunov稳定性定理推导出RBF神经网络的全调节律,用于处理系统中的非线性参数不确定性,提高了神经网络的在线逼近能力;采用神经网络和鲁棒控制方法,利用已知信息的同时,对控制系数矩阵未知时的设计问题进行处理,避免了控制器可能的奇异问题;引入非线性跟踪微分器,解决了Backstepping设计中的“计算膨胀”问题.运用Lyapunov稳定性定理证明了闭环系统的所有信号均最终一致有界. 相似文献
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利用神经网络和滑模控制,研究带有饱和输入的一类非线性系统。为了便于问题分析,引入饱和约束模型输出与控制输入的差值这个变量,分5种情况讨论,求得神经网络权值的在线调节律,得到保证闭环系统稳定的控制律。利用Lyapunov函数,证明了闭环系统的稳定性;仿真实验说明了算法的有效性。 相似文献
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1引言 在Wiener开创控制论的伊始,就将控制、信息和神经科学作为一个共同的课题。后,控制学科、计算科学和神经生理学趋于分开发展。自从80年代初期以来,神经网络有了长的进步,在人工智能和 相似文献
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Combining statistical process control, artificial neural networks and an expert system for the intelligent analysis and control of a plastic extruder facility is described. Statistical methodology is compared and contrasted to the exploratory neural network technique, which learns to relate and classify dependent production variables based on measurements taken on-line during the process. Integrating the neural network analysis into a composite control system using an expert system is presented. 相似文献
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典型人工神经网络的结构、功能及其在智能系统中的应用 总被引:14,自引:1,他引:13
人工神经网络已在各个领域得到广泛的应用,
尤其是在智能系统中的非线性建模及其控制器的设计、模式分类与模式识别、联想记忆和优
化计算等方面更是得到人们的极大关注.本文从网络在智能系统中建模及控制器设计的具体
训练结构入手,详细介绍了BP网络在系统控制中的典型应用方式,并根据不同网络所具有的
功能,从性能对比的角度对人工神经网络在上述各方面的应用给予综述. 相似文献
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Gabor Karsai Kristinn Andersen George E. Cook R. Joel Barnett 《Journal of Intelligent Manufacturing》1992,3(4):229-235
While welding processes are of great importance in manufacturing, their modeling and control is still subject of research. The highly nonlinear, strongly coupled, and multivariable nature of these processes renders the use of analytical tools practically impossible. In this article a novel approach is presented which employs networks of simple nonlinear units: a neural network. A widely used welding process, the Gas Tungsten Arc Welding is presented and the problem of its modeling and control is exhibited. A very brief introduction to neural networks is followed by presenting the experimental results for modeling the static and dynamic behavior of the process, as well as some practical recommendations regarding the use of the neural network techniques for controlling these processes. 相似文献
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Asymptotic stability of systems operating on a closed hypercube 总被引:1,自引:0,他引:1
Sufficient conditions for the global asymptotic stability of the equilibrium xe = 0 of dynamical systems which are characterized by linear ordinary differential equations with saturation nonlinearities are established. The class of systems considered herein arises in the modeling of control systems and neural networks. 相似文献
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《International journal of systems science》2012,43(16):3003-3021
ABSTRACTThis paper focuses on the decentralised adaptive finite-time connective stabilisation problem for a class of p-normal form large-scale nonlinear systems at the first. By combining the adding a power integrator technique, the neural network technique and the finite-time Lyapunov stability theory, the decentralised adaptive neural finite-time controllers are designed, which can guarantee the large-scale system is finite-time connectively stable. In order to avoid the effect of neural network estimation error on satisfying the finite-time criteria, the combination vectors are composed by the weights and the estimation errors of the neural networks. The maximal upper bounds of the combination vector norms are taken as the adaptive parameters. Because of employing neural networks, the restriction of the unknown nonlinear terms in some literature about finite-time control is relaxed. Two simulation examples are provided to prove the effectiveness and advantage of the proposed control method. 相似文献
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针对造纸工业传统控制中存在精确度低的问题,提出了利用人工神经网络进行预测控制的方法。通过人工神经网络在造纸工业的应用实例介绍,显示出人工神经网络是一种有效的智能控制手段,在自动控制上具有巨大优势。文章最后还对人工神经网络的应用与研究发展前景进行了评价。 相似文献