Da: preigu, Osnabrück, Germania
EUR 34,00
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Distributed Model Predictive Control with Uncertain Communication | DE | Jannik Hahn | Taschenbuch | Englisch | kassel university press | EAN 9783737612418 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Editore: Kassel University Press
ISBN 10: 3737612412 ISBN 13: 9783737612418
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 34,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This thesis investigates model predictive control for distributed systems. To achieve a common task, all subsystems compute and share local predictions with their neighbors. Due to unreliable communication, shared data might be outdated, and uncertain predictions must be used for planning. The proposed control strategies explicitly account for both network-induced and local uncertainties, considering bounded disturbances in a robust control framework and stochastic models within a chance-constrained MPC setting. In addition, the strategies incorporate predictions of the communication network itself to improve the overall system performance. Both schemes ensure recursive feasibility and closed-loop stability by using suitable terminal sets and constraints. Simulations validate constraint satisfaction and performance improvements. 200 pp. Englisch.
Editore: Kassel University Press
ISBN 10: 3737612412 ISBN 13: 9783737612418
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 34,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This thesis investigates model predictive control for distributed systems. To achieve a common task, all subsystems compute and share local predictions with their neighbors. Due to unreliable communication, shared data might be outdated, and uncertain predictions must be used for planning. The proposed control strategies explicitly account for both network-induced and local uncertainties, considering bounded disturbances in a robust control framework and stochastic models within a chance-constrained MPC setting. In addition, the strategies incorporate predictions of the communication network itself to improve the overall system performance. Both schemes ensure recursive feasibility and closed-loop stability by using suitable terminal sets and constraints. Simulations validate constraint satisfaction and performance improvements.