Model-Based Design of Adaptive Embedded Systems by Twan Basten, Roelof Hamberg, Frans Reckers, Jacques Verriet

By Twan Basten, Roelof Hamberg, Frans Reckers, Jacques Verriet

This e-book describes model-based improvement of adaptive embedded platforms, which permit more suitable performance utilizing an identical assets. The options awarded facilitate layout from a better point of abstraction, targeting the matter area instead of at the answer area, thereby expanding improvement potency. types are used to catch procedure requirements and to enforce (manually or instantly) approach performance. The authors exhibit the true influence of adaptivity on engineering of embedded structures through delivering a number of business examples of the versions utilized in the advance of adaptive embedded platforms.

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21]. The basic idea is that one does not only have to react to external variations, but when applicable, known or expected future variations can also be taken into account for the current control actions to take. Classically, this is one of the many flavours of control theory, for example model predictive control (MPC) or model reference adaptive control (MRAC). In both methods, a model of the system is used to better align the control parameters with the required system qualities. Such methods are generally concerned with continuous variables controlling physical phenomena, and are highly optimised for the specific situation at hand.

In other contexts the term independent variables is used. 2. e. their targeted values are goals to be achieved by the system. ; in other contexts the term dependent variables is used. 3. Contextual factors that have an influence on the system’s operation. These factors cover constraints, job parameters, environment conditions, etc. We will refer to these factors as context conditions. A property’s type can depend on the context it is used in. Consider for example the fusing temperature for toner-based printing.

The development of a system involves understanding the relationships of a range of properties and elements of the system, either through heuristics, measurements, hearsay, or explicit models. At least in development, systems engineers structure the knowledge for themselves to make it accessible for reasoning about the system behaviour. If the system has to do this reasoning itself at run-time, the knowledge structuring has to go – one way or another – into the system itself. Furthermore, in order to do incorporate this knowledge in a controllable way, it makes sense to consider it as a separate entity in the conceptual system architecture.

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