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AN OVERVIEW OF THE DEVELOPMENT OF LOW GAIN FEEDBACK AND LOW-AND-HIGH GAIN FEEDBACK

Journal of Systems Science & Complexity 2009年第4期22卷 697-721页

作者： Zongli LIN Department of Electrical and Computer Engineering,University of Virginia,P.O.Box 400473,Ch arlottesville,VA 22904-4743,USA. Department of Electrical and Computer Engineering University of Virginia P.O. Box 400473 Charlottesville VA 22904-4743 USA

Low gain feedback refers to certain families of stabilizing state feedback gains that are pararaeterized in a scalar and go to zero as the scalar decreases to zero. Low gain feedback was initially proposed to achieve semi-global stabilization of linear systems subject to input saturation. It was then combined with high gain feedback in different ways for solving various control.problems. The resulting feedback laws are referred to as low-and-high gain feedback. Since the introduction of low gain feedback in the context of semi-global stabilization of linear systems subject to input saturation, there has been effort to develop alternative methods for low gain design, to characterize key features of low gain feedback, and to explore new applications of the low gain and low-and-high gain feedback. This paper reviews the developments in low gain and low-and-high gain feedback designs.

关键词： Actuator saturation composite nonlinear feedback input saturation low-and-high gain feedback low gain feedback nonlinear control.

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Adaptive Learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion control.of an Industrial Robot

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International Journal of Automation and computing 2011年第1期8卷 54-61页

作者： Fusaomi Nagata Keigo Watanabe Department of Mechanical Engineering Faculty of Engineering Tokyo University of Science Department of Intelligent Mechanical Systems Graduate School of Natural Science and Technology Okayama University

Recently, various control.methods represented by proportional-integral-derivative （PID） control.are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward control.ers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the control.er. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward control.er for PUMA560 manipulator. The feedforward control.er works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control.effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.

关键词： Neural networks large-scale teaching signal sigmoid function adaptive learning servo system PUMA560 manipulator trajectory following control nonlinear control.

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维普期刊数据库

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Discrete-time delayed standard neural network model and its application

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Science in China(Series F) 2006年第2期49卷 137-154页

作者： LIU Meiqin School of Electrical Engineering Zhejiang University Hangzhou 310027 China

A novel neural network model, termed the discrete-time delayed standard neural network model （DDSNNM）, and similar to the nominal model in linear robust control.theory, is suggested to facilitate the stability analysis of discrete-time recurrent neural networks （RNNs） and to ease the synthesis of control.ers for discrete-time nonlinear systems. The model is composed of a discrete-time linear dynamic system and a bounded static delayed （or non-delayed） nonlinear operator. By combining various Lyapunov functionals with the S-procedure, sufficient conditions for the global asymptotic stability and global exponential stability of the DDSNNM are derived, which are formulated as linear or nonlinear matrix inequalities. Most discrete-time delayed or non-delayed RNNs, or discrete-time neural-network-based nonlinear control.systems can be transformed into the DDSNNMs for stability analysis and control.er synthesis in a unified way. Two application examples are given where the DDSNNMs are employed to analyze the stability of the discrete-time cellular neural networks （CNNs） and to synthesize the neuro-control.ers for the discrete-time nonlinear systems, respectively. Through these examples, it is demonstrated that the DDSNNM not only makes the stability analysis of the RNNs much easier, but also provides a new approach to the synthesis of the control.ers for the nonlinear systems.

关键词： delayed standard neural network model (DSNNM) linear matrix inequality (LMI) stability gen-eralized eigenvalue problem (GEVP) discrete-time nonlinear control.

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三维模糊控制系统的仿真研究(英文)

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哈尔滨工业大学学报 1987年第3期 130-131页

作者：李士勇胡恒章哈尔滨工业大学控制工程系

A new quantitative method to regulate the parameters of the control.rule in a fuzzy control.er (2DFC) was presented in. It should be pointed out that both the fuzzy control.er described in [1] and conventional fuzzy control.er are so-called two-dimension fuzzy control.ers which include two fuzzy control.variables (Error and Change in error).

关键词： Fuzzy control.er fuzzy control computer control.system nonlinear control.

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