L. Grüne, F. Allgoewer, R. Findeisen, J. Fischer, D. Groß, U. D. Hanebeck, B. Kern, M. A. Müller, J. Pannek, M. Reble, O. Stursberg, P. Varutti, and K. Worthmann, “Distributed and Networked Model Predictive Control,” in Control Theory of Digitally Networked Dynamic Systems, J. Lunze, Ed. Cham: Springer, 2014, pp. 111–167.



In this chapter, we consider the problem of controlling networked and distributed systems by means of model predictive control (MPC). The basic idea behind MPC is to repeatedly solve an optimal control problem based on a model of the system to be controlled. Every time a new measurement is available, the optimization problem is solved and the corresponding input sequence is applied until a new measurement arrives. As explained in the sequel, the advantages of MPC over other control strategies for networked systems are due to the fact that a model of the system is available at the controller side, which can be used to compensate for random bounded delays. At the same time, for each iteration of the optimization problem an optimal input sequence is calculated. In case of packet dropouts, one can reuse this information to maintain closed-loop stability and performance.



 AUTHOR={L. Grüne and F. Allgoewer and R. Findeisen and J. Fischer and D. Groß and U. D. Hanebeck and B. Kern and M. A. Müller and J. Pannek and M. Reble and O. Stursberg and P. Varutti and K. Worthmann},
 TITLE={{Distributed and Networked Model Predictive Control}},
 BOOKTITLE={Control Theory of Digitally Networked Dynamic Systems},
 COMMENT={noch nicht gemeldet, ISBN: ?, ? Normseiten}}