DEALING WITH TSOS WITH UNKNOWN AND CONSTANTLY CHANGING STRUCTURE Sample Clauses

DEALING WITH TSOS WITH UNKNOWN AND CONSTANTLY CHANGING STRUCTURE. AND DYNAMICS: THE C4L/L2C PRINCIPLE AND THE LOCAL4GLOBAL SYSTEM It is well-known that existing control design methodologies and the usual practice in control system design are not sufficiently adequate to provide efficient and dependable solutions to the problem of controlling large-scale, complex and highly heterogeneous TSoS. The usual practice to first model the system to be controlled and then apply one of the existing control design methodologies is practically not feasible in the case of TSoS for two main reasons:  Apart from the fact that modelling even a single constituent system of the TSoS, requires quite a tedious effort, the main obstacle in accurately modelling the TSoS behaviour is the lack of suitable methodologies to model the very complex, emergent and constantly changing interactions between the SoS constituent systems. Take for example, the case of a road traffic network: Existing tools are unable to accurately model the emergent, behaviour of drivers at or upstream of congested bottlenecks.  To make things worse, even if an accurate model of the TSoS dynamics were feasible to be developed, such a model would become inaccurate or even useless with the passage of time. System ageing, the constantly changing TSoS structure (TSoS topology or number of TSoS constituent systems may be subject to constant change), faults and incidents, changes in the exogenous factors affecting the TSoS dynamics are only few of the elements that may render the TSoS model partly or totally inaccurate. The Local4Global partners have recently developed, tested and evaluated a novel control design methodology [AGILE1, MBKI], [AGILE2]-[Ko08] that can serve as a starting basis towards overcoming the above mentioned shortcomings of existing control methodologies when dealing with TSoS. The main feature of the developed and demonstrated control methodology is that, even in the case where no model or a poor model for the system is available, it is able - by employing "learning-through-experience" - to rapidly compensate for any model inaccuracies and initial controller deficiencies as well as minor or major variations in the system dynamics so as to provide with an efficient constantly self-tuning (fully-adaptive) control strategy. The key idea of the methodology of [AGILE1, MBKI], [AGILE2-[Ko08] can be thought as a primitive version of the C4L/L2C mechanism proposed within Local4Global: as in the C4L/L2C mechanism, the methodology of [AGILE1, MBKI], [AGILE2-[Ko08] c...
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