Control Theory of Digitally Networked Dynamic Systems by J. Lunze, L. Grüne (auth.), Jan Lunze (eds.)

By J. Lunze, L. Grüne (auth.), Jan Lunze (eds.)

The ebook offers an advent to networked keep an eye on structures and describes new modeling paradigms, research equipment for event-driven, digitally networked platforms, and layout tools for allotted estimation and regulate. Networked version predictive regulate is constructed as a way to tolerate time delays and packet loss caused via the verbal exchange community. In event-based keep watch over the conventional periodic sampling is changed through state-dependent triggering schemes. Novel equipment for multi-agent platforms make sure entire or clustered synchrony of brokers with exact or with person dynamics.

The ebook contains quite a few references to the latest literature. Many tools are illustrated via numerical examples or experimental results.

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16). Fig. 16 Multi-agent system 26 J. Lunze and L. Gr¨ une Many control problems for multi-agent systems can be formulated as consensus or synchronization problems. , N. There are two important aspects of multi-agent systems with respect to networked control. First, the coupling structure of the overall system is usually not given, but has to be found as a result of the controller design. As research on consensus and synchronization problems have shown, the choice of this structure is more important than the selection of specific controller parameters within this structure.

3) This model shows that the closed-loop system behaves like an impulsive system with Eq. 2) describing the continuous flow and Eq. 3) the state jumps. ) are prescribed by the network protocol. At these time points a node has access to the network and the networkinduced error of this node is set to zero. The mapping φk depends upon the + network protocol. It shows which element of the vectors ey (t+ k ) or eu (tk ) are set to zero. For a survey on hybrid dynamic systems cf. [247] and, in particular, on impulsive systems cf.

15. The coordinator communicates with the ”optimizers” Oi at the subsystems in order to influence the result such that the behavior of the subsystems Pi , which include the plant subsystem Σi and the control station Ci , satisfies the global control goal. 3 gives a survey of results on distributed optimization and the design of optimal controllers, where the controller parameter should be found by the subsystems without any complete model of the overall system. • Distributed model predictive control: Chapter 4 describes several algorithms for model predictive control, where the optimization problem is solved by the subsystems with partial information about the overall system state.

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