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Richard H. Middleton

Bio: Richard H. Middleton is an academic researcher from University of Newcastle. The author has contributed to research in topics: Control theory & Linear system. The author has an hindex of 48, co-authored 393 publications receiving 12037 citations. Previous affiliations of Richard H. Middleton include Hamilton Institute & University of California.


Papers
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Journal ArticleDOI
30 May 2019
TL;DR: A new closed-loop structure for mechanical systems with dissipation, controlled via energy shaping is proposed, which generalizes the well-established potential energy shaping technique by introducing a kinetic-potential function which has both configuration and momentum states as arguments.
Abstract: In this letter, we propose a new closed-loop structure for mechanical systems with dissipation, controlled via energy shaping. The structure generalizes the well-established potential energy shaping technique by introducing a kinetic-potential function which has both configuration and momentum states as arguments. As a consequence of this generalization, dissipation terms are able to be added to the configuration coordinates of the system, resulting in both exponential stability and input-to-state stability (ISS) properties of the closed-loop. The developments are applied to the tracking problem where it is shown that the scheme is ISS with respect to friction modelling error.

14 citations

Proceedings ArticleDOI
14 Jun 2006
TL;DR: In this paper, the authors explored why modeling the self-sensing magnetic bearing as a linear periodic (LP) system improves the achievable robustness by utilizing lifting to analyze the LP model as a MIMO discrete LTI system.
Abstract: In "Magnetic bearing measurement configurations and associated robustness and performance limitations", Thibeault and Smith demonstrate that self-sensing magnetic bearings are impractical due to fundamental limitations in the achievable closed-loop robustness. Due to experimental data which appeared to contradict these results, Maslen, Montie, and Iwasaki showed that significantly better robustness is achievable in "Robustness limitations in self-sensing magnetic bearings" if the magnetic bearing is modeled as a linear periodic (LP) system rather than the linear time invariant (LTI) system used by Thibeault and Smith. The present paper explores why modeling the self-sensing magnetic bearing as a LP system improves the achievable robustness. This is accomplished by utilizing lifting to analyze the LP model as a MIMO discrete LTI system.

13 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: A novel delay skew data flow control algorithm is proposed for 5G dual connectivity and results are reported, indicating that the delay skew controller can meet the requirements on the delay characteristics of 5G networks.
Abstract: The emerging 5G networks are intended to enable new feedback control applications, run over multiple wireless interfaces. 5G wireless technologies then need to match the state-of-the- art wired network performance, experienced by present commercial feedback control systems. Fibre-optic circuit switched communication is characterized by constant and very low loop delays, uniform and very high sampling rates, very low error rates and an almost unlimited capacity. The non-trivial challenge is then to meet these characteristics with a packet switched wireless 5G network that may be associated with varying latency, time varying sampling rates, significant error rates and a varying air-interface capacity. The paper contributes with a summary and discussion of basic requirements that need to be in place for successful commercial deployment of feedback controllers using such 5G wireless networks. One key requirement is a need to mitigate the problem of delay skew between different transmission paths. A novel delay skew data flow control algorithm is therefore proposed for 5G dual connectivity. The stability of the controller is analyzed and conditions for global L_2-stability are stated. Test bed results are also reported in the paper, indicating that the delay skew controller can meet the requirements on the delay characteristics of 5G networks.

13 citations

Proceedings ArticleDOI
01 Dec 2011
TL;DR: It is shown that it is possible to achieve string stability, under some basic assumptions, with this approach, and some of the benefits of the use of this method such as lowered coordination requirements and simplified communication needs.
Abstract: For vehicle platoons, the leader following control structure is well known for being capable of achieving string stability. In this paper, the linear string case is modified so that each follower tracks the instantaneous velocity of the leader in addition to the position of its predecessor. We show that it is possible to achieve string stability, under some basic assumptions, with this approach. We also discuss some of the benefits of the use of this method such as lowered coordination requirements and simplified communication needs.

13 citations

Proceedings ArticleDOI
21 Jun 1995
TL;DR: In this paper, the authors generalize the results of Middleton and Freudenberg (1993) on nonpathological sampling to non-quare GSHF with high order and/or nonsquare data hold functions.
Abstract: Nonpathological sampling refers to sampling strategies which preserve stabilisability and detectability of an analogue system in the discretised system. This paper generalises the results of Middleton and Freudenberg (1993) on nonpathological sampling. This earlier work was restricted to square, and "memoryless" generalised sampled-data hold functions (GSHF), whereas here, we remove both these assumptions. Necessary and sufficient conditions are also given for nonpathological sampling with a GSHF which is high order and/or nonsquare.

13 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

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
06 Jun 1986-JAMA
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

7,563 citations

Proceedings ArticleDOI
15 Oct 1995
TL;DR: In this article, the authors present a model for dynamic control systems based on Adaptive Control System Design Steps (ACDS) with Adaptive Observers and Parameter Identifiers.
Abstract: 1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.

4,378 citations