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Eric Rogers

Bio: Eric Rogers is an academic researcher from University of Southampton. The author has contributed to research in topics: Iterative learning control & Linear system. The author has an hindex of 42, co-authored 690 publications receiving 8930 citations. Previous affiliations of Eric Rogers include Queen's University Belfast & University of Rostock.


Papers
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Journal ArticleDOI
01 Mar 1996
TL;DR: An algorithm for iterative learning control is proposed based on an optimisation principle used by other authors to derive gradient-type algorithms and has potential benefits which include realisation in terms of Riccati feedback and feedforward components.
Abstract: An algorithm for iterative learning control is proposed based on an optimisation principle used by other authors to derive gradient-type algorithms. The new algorithm is a descent algorithm and has potential benefits which include realisation in terms of Riccati feedback and feedforward components. This realisation also has the advantage of implicitly ensuring automatic step-size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm achieves a geometric rate of convergence for invertible plants. One important feature of the proposed algorithm is the dependence of the speed of convergence on weight parameters appearing in the norms of the signals chosen for the optimisation problem.

386 citations

Book
27 May 1992
TL;DR: In this article, the authors present a rigorous control theory for repetitive processes with linear dynamics and a constant pass length, based on an abstract representation formulated in functional analysis terms by, in effect, regarding the pass profile as a point in a Banach space.
Abstract: Repetitive, or multipass, processes are characterised by a series of sweeps, or passes, through a set of dynamics which in the simplest case is both linear and known. On each pass an output, or pass profile, is produced which acts as a forcing function on, and hence contributes to, the next pas profile. This so-called unit memory property is a special case of the more general situation where it is the previous M passes which contribute to the current one. The integer M is termed the memory length and such processes are simply termed non-unit memory. Industrial examples include long-wall coal cutting and certain metal rolling operations. This interaction between successive pass profiles is the basic source of the unique control problem for these processes. In particular, it is possible to generate oscillations which increase in amplitude from pass to pass. Such behaviour is clearly totally unacceptable and hence appropriatecontrol action is required. The concept of a multipass process was first introduced in the early 1970s as a result of work at the University of Sheffield on the modelling and control of long-wall coal cutting operations. This, in turn, led to systematic attempts at controller design for these and several other industrial examples based, essentially, on appropriately modifying existing standard linear system techniques such as Nyquist diagrams. As the number of examples increased, however, it gradually became clear that this general approach was, at best, valid only under quite restirctive conditions. Hence the need for a rigorous control theory where stability is an obvious essential item of any such theory. Using previously published work as a basis, this monograph presents a rigorous control theory, and associates tests, for repetitive processes with linear dynamics and a constant pass length. This is based on an abstract representation formulated in functional analysis terms by, in effect, regarding the pass profile as a point in a Banach space. All linear dynamics constant pass length examples are special cases of this abstract representation but this work concentrates on so-called differential and discrete non-unit memory linear repetitive processes which are of direct industrial relevance. Three computationally feasible sets of stability tests are developed together with some associated properties. These then lead to some preliminary results on feedback control which are included with the general aim of stimulating further research. A central theme in the work reported here is the use of structural links with other classes of linear dynamic systems. The work reported in this monograph was undertaken during periods when one or both of the authors were on the staff of The University of Sheffield, The Queen's University of Belfast and the The University of Strathclyde. It follows on from the original work of John Edwards at Sheffield to whom we owe a great debt of gratitude as the pioneer of this area. A number of former colleagues have also made very useful suggestions, particularly Derek Collins and Ian Wilson in the early days at Sheffield. Finally, we must thank Miss Yvonne Flemming for typing the final manuscript.Contents

315 citations

Journal ArticleDOI
TL;DR: An algorithm for iterative learning control is developed on the basis of an optimization principle which has been used previously to derive gradient-type algorithms and has numerous benefits which include realization in terms of Riccati feedback and feedforward components.
Abstract: An algorithm for iterative learning control is developed on the basis of an optimization principle which has been used previously to derive gradient-type algorithms. The new algorithm has numerous benefits which include realization in terms of Riccati feedback and feedforward components. This realization also has the advantage of implicitly ensuring automatic step size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm is expressed as a very general norm optimization problem in a Hilbert space setting and hence, in principle, can be used for both continuous and discrete time systems. A basic relationship with almost singular optimal control is outlined. The theoretical results are illustrated by simulation studies which highlight the dependence of the speed of convergence on parameters chosen to represent the norm of the signals appearing in the optimization problem.

308 citations

Book
20 Feb 2007
TL;DR: Stability, controlability, Observability, Poles and Zeros, and Control Law Design for Robustness and Performance are studied.
Abstract: Examples and Representations.- Stability - Theory, Tests and Performance Bounds.- Lyapunov Equations for Discrete Processes.- Lyapunov Equations for Differential Processes.- Robustness.- Controllability, Observability, Poles and Zeros.- Feedback and Optimal Control.- Control Law Design for Robustness and Performance.- Application to Iterative Learning Control.- Conclusions and Further Work.

271 citations


Cited by
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Journal ArticleDOI
01 Apr 1988-Nature
TL;DR: In this paper, a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) is presented.
Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

9,929 citations

Journal ArticleDOI
TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
Abstract: This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

2,645 citations

Journal ArticleDOI
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
Abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

2,391 citations

01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

1,829 citations