The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
T.w.s. Chow - One of the best experts on this subject based on the ideXlab platform.
-
2 d System Theory based iterative learning control for linear continuous Systems with time delays
IEEE Transactions on Circuits and Systems I-regular Papers, 2005Co-Authors: T.w.s. ChowAbstract:This paper presents two-dimensional (2-D) System Theory based iterative learning control (ILC) methods for linear continuous multivariable Systems with time delays in state or with time delays in input. Necessary and sufficient conditions are given for convergence of the proposed ILC rules. In this paper, we demonstrate that the 2-D linear continuous-discrete Roesser's model can be applied to describe the ILC process of linear continuous time-delay Systems. Three numerical examples are used to illustrate the effectiveness of the proposed ILC methods.
-
Iterative learning control for linear time-variant discrete Systems based on 2-D System Theory
IEE Proceedings - Control Theory and Applications, 2005Co-Authors: X. D. Li, J.k.l. Ho, T.w.s. ChowAbstract:The two-dimensional (2-D) System Theory iterative learning control (ILC) techniques for linear time-invariant discrete Systems are extended to the cases of linear time-variant discrete Systems. By exploiting the convergent property of 2-D linear time-variant discrete Systems with only one independent variable, a kind of 2-D System Theory ILC approach is presented for linear time-variant discrete Systems. Sufficient conditions are given for convergence of the proposed ILC rules. Two numerical examples are used to validate the ILC procedures.
-
an iterative learning control method for continuous time Systems based on 2 d System Theory
IEEE Transactions on Circuits and Systems I-regular Papers, 1998Co-Authors: T.w.s. Chow, Yong FangAbstract:This work presents a two-dimensional (2-D) System Theory based iterative learning control (ILC) method for linear continuous-time multivariable Systems. We demonstrate that a 2-D continuous-discrete model can be successfully applied to describe both the dynamics of the control System and the behavior of the learning process. We successfully exploited the 2-D continuous-discrete Roesser's linear model by extending the ILC technique from discrete control Systems to continuous control Systems. Three learning rules for ILC are derived. Necessary and sufficient conditions are given for convergence of the proposed learning rules. Compared to the learning rule suggested by Arimoto et al. (1984), our developed learning rules are less restrictive and have wider applications. The third learning rule proposed ensures the reference output trajectory can be accurately tracked after only one learning trial. Three numerical examples are used to illustrate the proposed control procedures.
-
an iterative learning control method for continuous time Systems based on 2 d System Theory
IEEE transactions on circuits and systems. 2 Analog and digital signal processing, 1998Co-Authors: T.w.s. Chow, Yong FangAbstract:This letter presents a two-dimensional (2-D) System Theory based iterative learning control (ILc) method for linear continuous-time multivariable Systems. We demonstrate that a 2-D continuous-discrete model can be successfully applied to describe both the dynamics of the control System and the behavior of the learning process. We successfully exploited the 2-D continuous-discrete Roesser's linear model by extending the ILC technique from discrete control Systems to continuous control Systems. Three learning rules for tLC are derived. Necessary and sufficient conditions are given for convergence of the proposed learning rules. Compared to the learning rule suggested by Arimoto [2], our developed learning rules are less restrictive and have wider applications. The third learning rule proposed in this letter ensures the reference output trajectory can be accurately tracked after only one learning trial. Three numerical examples are used to illustrate the proposed control procedures.
Yong Fang - One of the best experts on this subject based on the ideXlab platform.
-
an iterative learning control method for continuous time Systems based on 2 d System Theory
IEEE Transactions on Circuits and Systems I-regular Papers, 1998Co-Authors: T.w.s. Chow, Yong FangAbstract:This work presents a two-dimensional (2-D) System Theory based iterative learning control (ILC) method for linear continuous-time multivariable Systems. We demonstrate that a 2-D continuous-discrete model can be successfully applied to describe both the dynamics of the control System and the behavior of the learning process. We successfully exploited the 2-D continuous-discrete Roesser's linear model by extending the ILC technique from discrete control Systems to continuous control Systems. Three learning rules for ILC are derived. Necessary and sufficient conditions are given for convergence of the proposed learning rules. Compared to the learning rule suggested by Arimoto et al. (1984), our developed learning rules are less restrictive and have wider applications. The third learning rule proposed ensures the reference output trajectory can be accurately tracked after only one learning trial. Three numerical examples are used to illustrate the proposed control procedures.
-
an iterative learning control method for continuous time Systems based on 2 d System Theory
IEEE transactions on circuits and systems. 2 Analog and digital signal processing, 1998Co-Authors: T.w.s. Chow, Yong FangAbstract:This letter presents a two-dimensional (2-D) System Theory based iterative learning control (ILc) method for linear continuous-time multivariable Systems. We demonstrate that a 2-D continuous-discrete model can be successfully applied to describe both the dynamics of the control System and the behavior of the learning process. We successfully exploited the 2-D continuous-discrete Roesser's linear model by extending the ILC technique from discrete control Systems to continuous control Systems. Three learning rules for tLC are derived. Necessary and sufficient conditions are given for convergence of the proposed learning rules. Compared to the learning rule suggested by Arimoto [2], our developed learning rules are less restrictive and have wider applications. The third learning rule proposed in this letter ensures the reference output trajectory can be accurately tracked after only one learning trial. Three numerical examples are used to illustrate the proposed control procedures.
M B Zaremba - One of the best experts on this subject based on the ideXlab platform.
-
iterative learning control synthesis based on 2 d System Theory
IEEE Transactions on Automatic Control, 1993Co-Authors: J E Kurek, M B ZarembaAbstract:An algorithm is presented for iterative learning of the control input for a linear discrete-time multivariable System. Necessary and sufficient conditions are stated for convergence of the proposed algorithm. The algorithm synthesis and analysis are based on two-dimensional (2-D) System Theory. A numerical example is given. >
David Pouvreau - One of the best experts on this subject based on the ideXlab platform.
-
on the making of a System Theory of life paul a weiss and ludwig von bertalanffy s conceptual connection
The Quarterly Review of Biology, 2007Co-Authors: Manfred Drack, Wilfried Apfalter, David PouvreauAbstract:In this article, we review how two eminent Viennese System thinkers, Paul A Weiss and Ludwig von Bertalanffy, began to develop their own perspectives toward a System Theory of life in the 1920s. Their work is especially rooted in experimental biology as performed at the Biologische Versuchsanstalt, as well as in philosophy, and they converge in basic concepts. We underline the conceptual connections of their thinking, among them the organism as an organized System, hierarchical organization, and primary activity. With their System thinking, both biologists shared a strong desire to overcome what they viewed as a “mechanistic” approach in biology. Their interpretations are relevant to the renaissance of System thinking in biology—“Systems biology.” Unless otherwise noted, all translations are our own.
Xinjie Wang - One of the best experts on this subject based on the ideXlab platform.
-
a robot ultrasonic mapping method based on the gray System Theory
International Conference on Mechatronics and Automation, 2010Co-Authors: Xuming Pei, Jie Liu, Duanqin Zhang, Liangwen Wang, Xinjie WangAbstract:A robot mapping algorithm based on the gray System Theory is described in this paper. The ultrasonic error model is established according to the sound wave transmission character and a gray value is defined to express the sensor data uncertainty. In this algorithm, several continuous sonar data are fused based on the gray System fusion Theory in order to update the gray value of the map grid. A grid neighborhood searching method is proposed to judge the robot accessible position, plan the feasible path for the complete map of the whole environment. The validity and accuracy are proved in the real office environment.
