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7 results on '"Owens, David H."'

2. Iterative Learning Control an Optimization Paradigm

Author

Owens, David H. author., SpringerLink (Online service), Owens, David H. author., and SpringerLink (Online service)

Abstract

This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other electromechanical and/or mechanical systems. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Published
2016

3. On the use of reduced-order models and simulation data in control-system design.

Author

Owens, David H., Chotai, Arun, Owens, David H., and Chotai, Arun

Abstract

This note extends some results on robust control and approximation, to provide a systematic framework for the use of reduced-order models of single-input single-output systems in control systems design where data from a finite number of simulations of the original open-loop system can be used to assess the stability and performance of the implemented closed-loop scheme and substantially reduce conservatism during the design stage without the need for a full closed-loop plant simulation.

Published
1993

4. Existence and Learning of Oscillations in Recurrent Neural Networks

Author

Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., Prätzel-Wolters, Dieter, Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., and Prätzel-Wolters, Dieter

Abstract

In this paper we study a particular class of \(n\)-node recurrent neural networks (RNNs).In the \(3\)-node case we use monotone dynamical systems theory to show,for a well-defined set of parameters, that,generically, every orbit of the RNN is asymptotic to a periodic orbit.Then, within the usual 'learning' context of NeuralNetworks, we investigate whether RNNs of this class can adapt their internal parameters soas to 'learn' and then replicate autonomously certain external periodic signals.Our learning algorithm is similar to identification algorithms in adaptivecontrol theory. The main feature of the adaptation algorithm is that global exponential convergenceof parameters is guaranteed. We also obtain partial convergence results in the \(n\)-node case.

5. Existence and Learning of Oscillations in Recurrent Neural Networks

Author

Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., Prätzel-Wolters, Dieter, Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., and Prätzel-Wolters, Dieter

Abstract

In this paper we study a particular class of \(n\)-node recurrent neural networks (RNNs).In the \(3\)-node case we use monotone dynamical systems theory to show,for a well-defined set of parameters, that,generically, every orbit of the RNN is asymptotic to a periodic orbit.Then, within the usual 'learning' context of NeuralNetworks, we investigate whether RNNs of this class can adapt their internal parameters soas to 'learn' and then replicate autonomously certain external periodic signals.Our learning algorithm is similar to identification algorithms in adaptivecontrol theory. The main feature of the adaptation algorithm is that global exponential convergenceof parameters is guaranteed. We also obtain partial convergence results in the \(n\)-node case.

6. Existence and Learning of Oscillations in Recurrent Neural Networks

Author

Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., Prätzel-Wolters, Dieter, Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., and Prätzel-Wolters, Dieter

Abstract

In this paper we study a particular class of \(n\)-node recurrent neural networks (RNNs).In the \(3\)-node case we use monotone dynamical systems theory to show,for a well-defined set of parameters, that,generically, every orbit of the RNN is asymptotic to a periodic orbit.Then, within the usual 'learning' context of NeuralNetworks, we investigate whether RNNs of this class can adapt their internal parameters soas to 'learn' and then replicate autonomously certain external periodic signals.Our learning algorithm is similar to identification algorithms in adaptivecontrol theory. The main feature of the adaptation algorithm is that global exponential convergenceof parameters is guaranteed. We also obtain partial convergence results in the \(n\)-node case.

7. Existence and Learning of Oscillations in Recurrent Neural Networks

Author

Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., Prätzel-Wolters, Dieter, Townley, Stuart, Ilchmann, Achim, Weiß, Martin G., McClements, Warren, Ruiz, Antonio C., Owens, David H., and Prätzel-Wolters, Dieter

Abstract

In this paper we study a particular class of \(n\)-node recurrent neural networks (RNNs).In the \(3\)-node case we use monotone dynamical systems theory to show,for a well-defined set of parameters, that,generically, every orbit of the RNN is asymptotic to a periodic orbit.Then, within the usual 'learning' context of NeuralNetworks, we investigate whether RNNs of this class can adapt their internal parameters soas to 'learn' and then replicate autonomously certain external periodic signals.Our learning algorithm is similar to identification algorithms in adaptivecontrol theory. The main feature of the adaptation algorithm is that global exponential convergenceof parameters is guaranteed. We also obtain partial convergence results in the \(n\)-node case.

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