David H. Owens

Bio: David H. Owens is an academic researcher from University of Sheffield. The author has contributed to research in topics: Iterative learning control & Linear system. The author has an hindex of 42, co-authored 414 publications receiving 7794 citations. Previous affiliations of David H. Owens include Zhengzhou University & University of Southampton.

Computer-Aided Control System Design

Abstract: Computer-aided control system design , Computer-aided control system design , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Iterative learning control for discrete-time systems with exponential rate of convergence

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.

Stability Analysis for Linear Repetitive Processes

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

Iterative learning control using optimal feedback and feedforward actions

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.

Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks

Abstract: In this note, we revisit the problem of scheduling stabilizing control tasks on embedded processors. We start from the paradigm that a real-time scheduler could be regarded as a feedback controller that decides which task is executed at any given instant. This controller has for objective guaranteeing that (control unrelated) software tasks meet their deadlines and that stabilizing control tasks asymptotically stabilize the plant. We investigate a simple event-triggered scheduler based on this feedback paradigm and show how it leads to guaranteed performance thus relaxing the more traditional periodic execution requirements.

Essentials of Robust Control

Abstract: 1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

A survey of iterative learning control

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.

Variable structure control of nonlinear multivariable systems: a tutorial

Abstract: The design of variable-structure control (VSC) systems for a class of multivariable, nonlinear, time-varying systems is presented. Using the Utkin-Drazenovic method of equivalent control and generalized Lyapunov stability concepts, the VSC design is described in a unified manner. Complications that arise due to multiple inputs are examined, and several approaches useful in overcoming them are developed. Recent developments are investigated, as is the kinship of VSC and the deterministic approach to the control of uncertain systems. All points are illustrated by numerical examples. The recent literature on VSC applications is surveyed. >

Linear systems

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: