|  | HYBRIDGE Project Presentation
	Project detailsProject objective(s)Description of the workTechnical approachMilestones and expected results 1. Project details
	| Contract number | IST-2001-32460 |  
	| Project acronym | HYBRIDGE |  
	| Project name | Distributed Control and Stochastic Analysis of Hybrid Systems 
	Supporting Safety Critical Real-Time Systems Design |  
	| Project period | January 2002 - December 2004 |  
	| Key action | IST-2001-IV.2.1 (iii) |  
	| Action line | Distributed Control |  
	| Total project cost | 4,000 kEuro |  
	| IST European Commision funding | 2,400 kEuro |  Top
 2. Project objective(s)The 21st century finds Europe facing a number of remarkable changes, many of which 
involve large complex real-time systems the management and control of which undergoes 
a natural trend of becoming more and more distributed while at the same time the safety 
criticality of these systems for human society tends to increase. However good the control 
design for these systems will be, humans are the only ones carrying responsibility for 
the operational safety. This implies that control system designs for safety critical 
operations have to be embedded within sound safety management systems such that the 
level of safety stays under control of humans. The objective of HYBRIDGE is to 
develop the methodologies to accomplish this, and to demonstrate their use in 
support of advanced air traffic management design.In addition to direct application to air traffic management, these contributions 
form the nucleus for further research and development into a complex, uncertain 
system theory, and into application of this theory to distributed control of other 
real time complex systems such as communication, computer and power networks.
 
 
   
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 3. Description of the work In order to lay the foundations for a systems theory for safety critical complex
uncertain systems, the challenging developments are organised in clusters of innovative areas:
 
Characterise in mathematical terms the relations between various mathematical models 
	that are in use by the various hybrid systems researchers. Key examples are Automation 
	models, Petri net models, Piecewise Deterministic Markov Processes and Stochastic 
	Differential equations on an hybrid state space and Hierarchical control models.Develop architectures and algorithms for distributed conflict control and error evolution 
	control for safety critical systems which are embedded within safety management.Development of stochastic analysis based accident risk assessment methodology which 
	supports the design of distributed control of complex systems for safety critical operations. The interplay between these areas is shown through the demonstration of these results on 
working examples and risk assessment courses to learn others how these results are used in 
practice. For the realisation of this we will systematically refer to the design of advanced 
air traffic management, since: 
 
	Top It is well known as one of the most complex distributed safety critical systems,  It allows to show how the different HYBRIDGE developments complement each other, and  It supports the urgent need of innovative approaches in advanced ATM developments. 4. Technical approachAn important rationale underlying the technical approach of the HYBRIDGE project has been 
the desire to subdivide the work into well-defined work packages that can be handled in parallel 
by different partners from the consortium in such a way that their specific and often unique 
background is used to the full extent. The working principle has been that the university 
researchers shall put their ingenuity and theoretical system/control background into the 
specific area of a complex operation like air traffic. For leaders of work packages the 
specific selection of their preferred topic is up to them, under the explicit condition 
that their approach should be such that at least one of the non-university partners is 
enthusiastic in providing the necessary support towards connecting the abstract theories 
to relevant air traffic situations. This rationale ensures to a large extent that each work 
package leader is able to achieve an effective execution of the research, within the time 
frame and in an efficient way being able to accept the responsibility for realising the 
measurable objectives of his work package(s). In view of this, the main risk that remains 
is coming from the possibility that the availability of the key researcher of a work package 
leader becomes a problem. In such case it is the responsibility of the Co-ordinator to identify 
an appropriate way to solve this problem.
Following this rationale we arrived at breakdown of the work into ten technical work packages. 
These ten work packages have been clustered around the Core Innovation areas I, II and III 
as follows (see also Figure below):
	   
		 
		  Stochastic hybrid modelling cluster, containing four work packages: 
		   
			 
			  WP1: Identification and modelling of uncertain hybrid systems 
				(Partner UCAM leads)
			 
			  WP2: Stochastic hybrid systems based modelling of accident risk 
				(Partner NLR leads)
			 
			  WP3: Reachability analysis for probabilistic hybrid systems (Partner 
				UniBs leads)
			 
			  WP4: Compositional specification of stochastic hybrid systems 
				(Partner TWEN leads)
			 
		  Distributed Control theory cluster, containing three work packages 
		   
			 
			  WP5: Control of uncertain hybrid systems (Partner UCAM leads)
			 
			  WP6: Decentralized conflict prediction and resolution (Partner 
				NTUA leads)
			 
			  WP7: Error evolution control (Partner AQUI leads)
			 
		  Distributed Control Risk Assessment cluster, also containing three 
			work packages 
		   
			 
			  WP8: Accident risk decomposition (Partner TWEN leads)
			 
			  WP9: Perform risk assessment of distributed control system (Partner 
				NLR leads)
			 
			  WP10: Develop advanced risk assessment course (Partner NLR leads)
			  
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 5. Milestones and expected resultsThe HYBRIDGE project has several concrete milestones:
 
	TopMethods for modelling, analysing and verifying complex uncertain hybrid systems 
		and use these methods to identify models to perform	conflict prediction in air traffic.Methods for controlling uncertain hybrid systems, both centralised and decentralised, 
		and use these methods towards the development of distributed control architectures 
		and algorithms for conflict resolution in air traffic management.Error detection methods in uncertain distributed hybrid systems in particular for 
		the detection of human situational awareness errors	and system reconfiguration needs.A stochastic analysis framework for accident risk modelling and assessment methodology 
		for distributed hybrid control systems and its demonstrate towards advanced air traffic 
		management. |  |