CONTROL 2014: UKACC INTERNATIONAL CONFERENCE ON CONTROL (CONTROL 2014)

# PROGRAM FOR THURSDAY, JULY 10TH

Days:
previous day
next day
all days
08:30-09:30 Session 5: Plenary - Richard Parry Jones
 08:30 Research Challenges for Control Systems Engineering in Road and RailSPEAKER: Richard Parry-Jones
10:00-12:00 Session 6A: IMechE Mini Symposium - Unmanned Aircraft Systems
 10:00 Advanced UAS technologies for aerial manipulation and landing on mobile platformsSPEAKER: Antidio Viguria Jiménez 10:24 Bio-Inspired Flight Control Systems for UASSPEAKER: Ian Cowling 10:48 VTOL flying wing control systems developmentSPEAKER: Mark Shaw With Arthur Henry 11:12 Civil UAV applications Using Crowd-sourced Imagery AnalysisSPEAKER: Darren AnsellABSTRACT. This presentation describes the cross-disciplinary ‘Aerosee’ project, involving UCLan’s School of Computing, Engineering and Physical Sciences and Media Innovation Studio, along with commercial firm eMigs. Aerosee is unique in uniting Unmanned Aerial Vehicle technology with the ‘power of the crowd’. It capitalises on some of the key attributes of the burgeoning UAV technology field, and examine how the devices can be used for socially-motivated purposes, such as by the emergency services. The presentation will discuss the first trial which explored how the use of UAV (or ‘Drone’) technology, together with ‘crowd-sourced’ imagery analysis can reduce the time taken to locate and rescue a person in distress. 11:36 Research and Test Environment for Unmanned Aircraft SystemsSPEAKER: Matthew Coombes
10:00-12:00 Session 6B: IET Mini Symposium - Monitoring and control of poorly-defined and uncertain systems
 10:00 The smart grid – essential contribution to efficiency or Achilles heel of a future electricity system?SPEAKER: Roger KempABSTRACT. The principles behind Britain’s electricity system have been largely unchanged for a century. Steam turbines were demonstrated by Sir Charles Parsons in 1884 and, in Edwardian times, Charles Merz established an ac grid in Newcastle on Tyne distributing power at 5.5 kV, three-phase. The present grid, although hundreds of times larger, uses basically the same technology. The Climate Change Act 2008 and the Industrial Emissions Directive 2010/75/EU have triggered a paradigm shift in this system. So far, we have seen only small changes but plans for phasing-out coal-fired power stations and the widespread adoption of renewable energy will radically change the way electricity is generated and used. Instead of the flow of energy being from a few dozen large centralised power stations via high voltage grids to millions of consumers, we will have generation that includes thousands of wind turbines and millions of solar panels, feeding into the 11 kV and 400 V distribution networks. In addition, we can expect to see millions of electric vehicles, each having a high-capacity battery charger, and a wholesale switch from gas central heating to electrically-powered heat pumps which could double the peak load on the grid. Since the 1950s, electricity generation policy has been “predict and provide”. Consumers do not have to think about when they switch on the kettle; there will always be enough electricity. However to continue this policy when much of the generating capacity uses renewable energy, that is only available when the sun shines or the wind blows, and when loads such as electric vehicle chargers could equally well be supplied at 3 am, rather than when the driver happens to return from work, would be horrendously expensive. Luckily the smart grid can come to the rescue! No-one has really defined what a smart grid is – or to be more accurate, thousands of people have defined it to themselves and made the assumption that their understanding is the same as for everyone else. The common assumption is that it is (or will be) some form of intelligent network that recognises when electricity is in short supply and reduces the demand from loads that can readily be cut off for a period. Other visions of a smart grid include the capability to modulate the output of solar panels or trigger reverse power flows in EV battery chargers (vehicle-to-grid or V2G) to compensate for a sudden loss of supply. Companies are already marketing home energy management computers that allow consumers to control loads remotely via an App on their phones. However there are downsides: if the smart grid sends a message to a million 5 kW battery chargers, sold with a default setting of 10 p/kWh, that electricity prices have dropped from 11 to 9 p/kWh, the surge in demand could destabilise the system – any feedback system with unquantifiable delays risks instability. An erroneous message asking V2G battery chargers over a wide area to provide power could defeat protection systems. Attacks on a smart grid are also a possibility. With household energy bills more than £1000 p.a. there is an incentive to buy black market software that hacks into a smart meter – and less risky than a pair of crocodile clips in the meter terminal box. During the 1939-45 war, both sides sent waves of bombers to damage power stations. In a future conflict the task could be achieved more easily and surreptitiously by the equivalent of the Stutnix virus, used against Iranian centrifuges. Without smart grids we are unlikely to be able to meet our climate change emissions targets. With them we may be introducing instability or opportunities for malicious intervention that causes disruption to power supplies. How do we weigh the risks of a technology for which we do not even have a shared definition? 10:40 Challenges for monitoring and controlling biological systemsSPEAKER: Jean-Marie AertsABSTRACT. Thanks to the (r)evolution in sensors and computing power, the possibilities for measuring and processing biological signals in real-time are ever growing. As a consequence, it becomes more and more realistic to apply schemes of model-based monitoring and control to biological systems in practice. However, unlike many mechanical systems, biological systems are very complex, individually different, time-varying and dynamic, making the management of these systems often very challenging. A first challenge is to model these biological systems using compact, accurate and biologically meaningful models that can be applied in monitoring and controlling schemes. It is argued that dynamic data-based mechanistic modelling approaches are potential candidates for model-based management of biological systems. A second challenge is to extract model features that can be used as biomarkers for monitoring the status of individual biological systems. A third challenge is to define proper control targets that take into account the physiological/physical boundary conditions of the considered biological system. Examples of animal and human applications, ranging from cellular up to ecosystem level, will be discussed. 11:20 Poorly-Defined Environmental Systems: Data-Based Mechanistic Modelling, Forecasting and ControlSPEAKER: Peter YoungABSTRACT. Engineering is basically about the way humans apply their knowledge to design, build and maintain objects of various kinds. Although engineering research can take various forms, depending on what branch of engineering we are considering, it normally conforms to the scientific method', following what Popper (1959) has termed a ‘hypothetico-deductive’ procedure. There is, however, an important difference between such standard engineering research and research based on the application of engineering principles and methods to naturally occurring environmental systems (recently termed Geoegineering). Natural systems are not made by humans: they may be shaped and modified by us but we did not create them and so, in contrast to normal engineering constructs, their internal behavioural mechanisms are quite often poorly defined and understood. The climate scientist, for instance, can hypothesize about the causes and mechanisms of global warming' but it is clear from the controversy on this topic, that such hypotheses are not universally accepted. Moreover, while Popper stresses the importance of planned experimentation in hypothetico-deductive reasoning, it is rarely possible to conduct well planned experiments on natural systems in order to remove ambiguity and so clarify our understanding. An alternative, systems-based approach is to observe the normal operational behaviour and try to infer the most important underlying mechanisms from such observational data and our, hopefullly inspired, perceptions of what these dominant mechanisms may be. Such data-based analysis is the domain of inductive' or hypothetico-inductive' reasoning (see e.g. Young, 2013), in which the modelling is based on model structures that are initially identified from the naturally occurring data, thus avoiding undue reliance on prior hypotheses and ensuring that the resulting models are fully identifiable from the available data. This seminar will outline a particular hypothetico-inductive approach to the modelling, forecasting and control of environmental systems and show how this Data-Based Mechanistic (DBM) procedure (see e.g. Young 1998, 2011 and the prior references therein) has been applied in a study of globally averaged climate data. It will show how the behaviour of the large simulation models produced by climate scientists is much simpler than their size and complexity would suggest. In particular, the globally averaged response characteristics of even the largest climate models, the Global Circulation Models (GCMs), are defined almost completely by a relatively small number of dominant modes'; and it is these dominant modes of behaviour that are most important in forecasting and control system design.
10:00-12:00 Session 6C: Special Session - Automotive Control
10:00-12:00 Session 6D: Invited Session - Integrated Operation in Large Scale Industrial Sites, Results from the Energy-Smartops Consortium
Chairs:
12:45-13:45 Session 7: Plenary - Geoff Downton
 12:45 Drilling and ControlSPEAKER: Geoff DowntonABSTRACT. Within a tick of the imperceptibly slow moving geological clock, a borehole is plunged deep into the Earth. How we steer the ensuing transient response of the rocks and the fluids towards a safe and beneficial outcome is at the very heart of the drilling process. Wells are being drilled deeper and faster and in more difficult environments, and so the complexity of the interactions between different domains of physics is increasing. Fortunately, this is a challenge well suited to the interests and tools of the control engineering community. The “factory floor” of a well is clearly very long and very thin—effectively a line along which the mechanical drilling string and the hydraulic drilling fluids must transmit  power, information and control to propagate the borehole to its target resting place. The Earth needs to be persuaded to reveal its structural secrets, and so long arrays of sensors and actuators follow the drill bit into the ground to illuminate, monitor and control the path to be drilled. In comparison to the drilling industry, the downstream processing industries are highly automated. A chemical plant designer has great freedom to create the ideal production environment. Such is not the case for drilling. The impetus for control engineers to take an interest in drilling has been further strengthened by a drive to increase the level of automation for the standard reasons of efficiency, consistency, performance and management of complexity. This drive for controlled drilling presents both challenges and opportunities.
14:00-16:00 Session 8A: InstMC Mini Symposium - Actively Controlled Automotive Safety Systems
14:00-16:00 Session 8B: IChemE Mini Symposium - Industrial Process Control: Latest Challenges and Applications
 14:00 Innovation Still Required to Solve Upstream Oil & Gas Control IssuesSPEAKER: Paul Oram 14:24 Practical problems in implementing small scale MBPC in the chemical industrySPEAKER: Philip Masding 14:48 TBASPEAKER: Dave Lovett 15:12 Making predictive control easier and cheaper to implementSPEAKER: Anthony Rossiter
14:00-16:00 Session 8C: Invited Session - Sustainable Control of Off-shore Wind Turbines
Chairs:
14:00-16:00 Session 8D: Invited Session - Real-time Adaptive Networked Control of Rescue Robots 1