Special Issue "Advances in Control Engineering"


A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (31 March 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Paul Stewart
School of Engineering, University of Hull, Cottingham Road, Hull HU6 7RX, UK
Website: http://www2.hull.ac.uk/science/engineering/our%20staff/academic/paulstewart.aspx
E-Mail: p.stewart@hull.ac.uk
Phone: +44 1482 466665
Interests: complex system simulation, design and optimization; engineering applications of artificial intelligence; advanced control systems; power and energy architectures; electrical machines, drives and systems; energy conversion and storage; remote monitoring and sensing; prognostics and diagnostics; low carbon and low emissions operations

Special Issue Information

Dear Colleagues,

The last twenty years have seen a radical step-change in the capability and application of Control Engineering, brought about by advances in computational speed and capacity. Control design for contemporary, complex engineering systems has developed alongside Computer Aided Control System Design, powerful real-time embedded computation, and both off-line and on-line optimization techniques.

This Special Issue will bring together papers, which particularly describe recent advances in Control Engineering in industrial applications and complex engineering systems, describing the application of novel theory across all areas of Automation. Papers which include practical experimental results are particularly encouraged, as are papers which set Control advances in the wider context of, for example, society, economics, energy and environment.

Application topics might include:
Aeronautic/Automotive/Transportation Engineering
Autonomous Vehicles
Biomedical Engineering
Electrical Power Systems(including Renewables and Smart Grid)
Environmental Engineering
Manufacturing and Process Control

Control Theory topics might include:
Artificial Intelligence and Heuristics
Optimisation and Search
Non-linear, Adaptive and Robust Control
Advances in PID
Systems Identification
Model based Control
Fault Detection and Diagnostics

Prof. Dr. Paul Stewart
Guest Editor


Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Published Papers (7 papers)

by , , ,  and
Machines 2014, 2(3), 176-201; doi:10.3390/machines2030176
Received: 31 March 2014; in revised form: 9 June 2014 / Accepted: 1 July 2014 / Published: 17 July 2014
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by ,  and
Machines 2014, 2(3), 158-175; doi:10.3390/machines2030158
Received: 10 February 2014; in revised form: 23 June 2014 / Accepted: 30 June 2014 / Published: 4 July 2014
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by , , ,  and
Machines 2014, 2(2), 134-157; doi:10.3390/machines2020134
Received: 28 February 2014; in revised form: 6 May 2014 / Accepted: 15 May 2014 / Published: 20 May 2014
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by  and
Machines 2014, 2(2), 99-119; doi:10.3390/machines2020099
Received: 19 January 2014; in revised form: 30 March 2014 / Accepted: 15 April 2014 / Published: 7 May 2014
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Machines 2014, 2(1), 13-72; doi:10.3390/machines2010013
Received: 14 October 2013; in revised form: 4 December 2013 / Accepted: 25 December 2013 / Published: 2 January 2014
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Machines 2013, 1(3), 81-97; doi:10.3390/machines1030081
Received: 17 June 2013; in revised form: 27 August 2013 / Accepted: 27 September 2013 / Published: 16 October 2013
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Machines 2013, 1(1), 33-49; doi:10.3390/machines1010033
Received: 11 March 2013; in revised form: 29 April 2013 / Accepted: 20 May 2013 / Published: 29 May 2013
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Type of Paper: Article
Title: pCAMAL: A Cognitive Control Architecture
Author: Darryl N. Davis
Affiliation: Department of Computer Science, University of Hull, Kingston-upon-Hull, UK; E-Mail: D.N.Davis@hull.ac.uk
Abstract: Evolution has presented us with a wide range of efficient ecological control architectures. We conveniently refer to these as minds. The study of these biological control architectures has informed artificial intelligence, cognitive science and related disciplines on a number of levels. The technologies arising from these investigations have given rise to a diverse range of machine intelligences, from swarms to cognitive robotics. Here we present the latest evolution of a continuously developing cognitive control architecture (pCAMAL). The CAMAL (Computational Architectures for Motivation, Affect, and Learning) model is a theoretical construct for investigating artificial minds that draws on formal (BDI) reasoning, probability models, affective computation, reactive machines and self-configurable architectures. The latest implementation (probabilistic CAMAL or pCAMAL) has been developed using both synthetic and real robots. It combines qualitative and quantitative reasoning in a sense, motivator management, goal selection, action feedback cycle to adapt to environments.

Type of Paper: Article
Title: Foundations of Impedance Control and Optimal Regulation for a Class of Nonlinear Mechanical Systems
Authors: Suguru Arimoto 1,* and Masahiro Sekimoto 2
Affiliations: 1 Emeritus Professor, Faculty of Engineering Science, Osaka University, Japan; E-Mail: arimoto@fc.ritsumei.ac.jp
2 Faculty of Engineering, Toyama University, 3190 Gofuku, Toyama, Toyama Prefecture 930-0887, Japan
Abstract: Impedance control is a practical feedback control scheme for a class of mechanical systems whose dynamics are regarded as a linear dynamical system with a specified linear input-output relation. Such a linear dynamical system is said to be passive if and only if its input-output transfer function is positive real. For a class of nonlinear mechanical systems including robotic mechanisms, the passivity is still in effect and further a strict passivity concept can be introduced with the aid of a nonlinear version of Kalman-Yakubovich-Popov relation. Notwithstanding the strictness of passivity, strict positive realness (s. p. r.) has not yet studied so far for any class of nonlinear dynamical systems. This paper aims at introducing an extended notion of s. p. r. property for a class of robotic mechanisms with input-output pairs specified in task space (or operational space). A sufficient condition for a task space PD (position and its differential) feedback to make the closed-loop system s. p. r. in a local sense is given by analyzing what optimality condition the closed-loop system satisfies. The optimality is shown on the basis of solvability of a Hamilton-Jacobi-Bellman equation derived from an optimal regulator problem. It is shown that task-space impedance control with optimality and s. p. r. property is realizable even if the degrees-of-freedom (d. o. f.) of an objective mechanism is redundant relative to the dimension of task space. This suggests a new understanding of Bernstein’s dexterity of human limb motion under some d. o. f. redundancy.

Last update: 15 August 2013

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