This paper presents significant new results on the stability and convergence properties of a general class of iterative learning control schemes derived using two-dimensional(2D) systems theory. These results apply fo...
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This paper presents significant new results on the stability and convergence properties of a general class of iterative learning control schemes derived using two-dimensional(2D) systems theory. These results apply for a general learning law which (explicitly) uses information from previous iterations or trials. A key feature of these results is that they are expressed in terms of standard linear systems theoretic properties, such as relative degree and the location of the zeros.
Repetitive, or multipass, processes are uniquely characterized by a series of sweeps, or passes through a set of dynamics defined over the so-called pass length which is finite and constant. The unique systems theoret...
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Repetitive, or multipass, processes are uniquely characterized by a series of sweeps, or passes through a set of dynamics defined over the so-called pass length which is finite and constant. The unique systems theoretic/control problem is that the sequence of outputs, or pass profiles, can contain oscillations which increase in amplitutde in the pass to pass direction. These processes can be modelled as a class of quarter plane causal 2D linear systems and this paper shows that the boundary (or pass initial) conditions alone can destabilize them. Hence they must be 'adequately modelled' in a given application and it is the boundary conditions which essentially distinguish the dynamic behaviour of linear repetitive processes from other classes of 2D linear systems.
作者：
Kochs, HDDieterle, WDittmar, E[?]Prof. Dr.-Ing. Hans-Dieter Kochs (1943)
VDE is head of the Department of Computer Science and Information Processing Systems at the Gerhard Mercator-University of Duisburgl Germany. He received the Dip1.-Ing. degree in Electrical Engineering and the Dr.-Ing. degree from the RWTH Aached Germany in 1972 and 1976 respectively. From 1979 to 1991 he was system engineer and division leader in the areas of R & D of highly-reliable large-scale control and information systems (AEGIDaimler FAG-Kugelfischer). Since 199 1 he has been Professor at the University of Duisburg. His current R & D areas are reliability safety and fault tolerance of technical systems especially automation systems and hybridknowledge based systems including fuzzy-logic and neural networks. (Gerhard-Menxitor-University-GH Duisburg FB 71FG 10 Lotharstr. 1 D-47048 Duisburg T +49203/379-2204 Fax +49203I379-2205)
The paper presents the results of an application-based reliability study of distributed computercontrolsystems with very high reliability demand, e.g. for supervision and control of power plants and energy distribut...
The paper presents the results of an application-based reliability study of distributed computercontrolsystems with very high reliability demand, e.g. for supervision and control of power plants and energy distribution systems. A reliability classification scheme is presented and typical redundant control system structures are evaluated and classified due to their system reliability Special focus is placed on assessing the influence of the communication system on total system reliability.
Many problems in nonlinear control theory (like, for instance, feedback linearization problem) leed to examination of integrability of a Pfaffian system. In a generic case a Pfaffian system is not integrable. Therefor...
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Many problems in nonlinear control theory (like, for instance, feedback linearization problem) leed to examination of integrability of a Pfaffian system. In a generic case a Pfaffian system is not integrable. Therefore, how to approximate non-integrable Pfafian systems by integrable ones and how this approximation can applied in practice appears to be a natural and important problem. In the present paper we establish some measures of non-integrability of Pfaffian system of arbitrary dimension and discuss their relation to approximations of non-integrable Pfaffian systems by integrable ones. Our work is motivated by expected applications to approximate feedback linearization of multi-input nonlinear systems.
Computing the trajectories generated by an arbitrary system or process is extremely important for its analysis, especially for control and stability investigations. This paper analyses the fundamental matrix sequence ...
Computing the trajectories generated by an arbitrary system or process is extremely important for its analysis, especially for control and stability investigations. This paper analyses the fundamental matrix sequence (a discrete counterpart of the transition matrix in the continous case) for a linear unit memory repetitive process. The main result refers to the representation of the repetitive process in terms of the general singular Kurek model.
This paper describes a learning augmented recursive estimation approach for nonlinear dynamical systems having unmodeled nonlinearities. Utilizing a passive spatially-localized learning system, an approximation of the...
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This paper describes a learning augmented recursive estimation approach for nonlinear dynamical systems having unmodeled nonlinearities. Utilizing a passive spatially-localized learning system, an approximation of the unknown nonlinearity is synthesized online, based on state and parameter estimates from a nonlinear recursive estimator (an adaptive form of the extended Kalman filter). The learned model of the nonlinearity is used, in turn, to improve the performance of the recursive estimator. We demonstrate the approach on a second-order, mass-spring-damper system, where the spring stiffness is a nonlinear function of position. Simulation results indicate that, relative to more traditional adaptive estimation schemes, markedly improved estimation performance can be achieved.
This paper describes two computing paradigms known as neural computing and evolutionary computing, and their potential contribution to building intelligent software systems. The paper begins by giving a brief introduc...
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This paper describes two computing paradigms known as neural computing and evolutionary computing, and their potential contribution to building intelligent software systems. The paper begins by giving a brief introduction to the origins of each paradigm. Then two sections introduce the basic principles, and identify the role of each paradigm in intelligent system design. Each section ends with a number of applications that have been or are being investigated. These include connection admission control, modem communication, adaptive model-based control, face and handwriting recognition, frequency assignment, help-desk scheduling, financial time-series prediction, face recognition and evolving agent behavior. A section introduces the idea of using communications theory to design neural networks and the paper concludes with the authors' views on the future of neural and evolutionary computing for intelligent software systems.
Learning of large-scale neural networks suffers from computational cost and the local minima problem. One solution to these difficulties is the use of modular structured networks. Proposed here is the learning of modu...
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Learning of large-scale neural networks suffers from computational cost and the local minima problem. One solution to these difficulties is the use of modular structured networks. Proposed here is the learning of modular networks using structural learning with forgetting. It enables the formation of modules. It also enables automatic utilization of appropriate modules from among the previously learned ones. This not only achieves efficient learning, but also makes the resulting network understandable due to its modular character. In the learning of a Boolean function, the present module acquires information from its subtask module without any supervision. In the parity problem, a previously learned lower-order parity problem is automatically used. The geometrical transformation of figures can be realized by a sequence of elementary transformations. This sequence can also be discovered by the learning of multi-layer modular networks. These examples well demonstrate the effectiveness of modular structured networks constructed by structural learning with forgetting.
Reactive ion etching is an important process in the fabrication of microelectronic devices. This article reports on work in progress towards developing a strategy for controlling sidewall profile during this process. ...
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Reactive ion etching is an important process in the fabrication of microelectronic devices. This article reports on work in progress towards developing a strategy for controlling sidewall profile during this process. In this strategy, a response surf...
The task of guaranteeing that processes meet their deadlines is one of the most complex in the design of hard real-time controlsystems. This paper describes how process scheduling has been integrated into a framework...
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The task of guaranteeing that processes meet their deadlines is one of the most complex in the design of hard real-time controlsystems. This paper describes how process scheduling has been integrated into a framework of tools for the development of such systems. Functional specifications in the form of control system block diagrams are translated by the Framework into real-time source code which may be executed in a parallel processing environment. The automated off-line scheduling tool takes process specifications from the system under development and attempts to map these processes to the processors in such a way that all the system deadlines are met by using a detailed model of the run-time environment.
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