Abstract
Abstract Distributed model predictive control (DMPC) methods that are based on iterative optimization algorithms normally require a large number of communications between the controllers, especially when the number of subsystems is large. This can easily result in overloading the network which is normally shared between many different users. Therefore methods to reduce the load on the network while still satisfying convergence, feasibility, stability, and a certain level of performance are of a great importance. In this paper a novel event-based optimization algorithm is provided to reduce the number of communications in DMPC methods that are based on dual decomposition.
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