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Model predictive control (MPC) is a frequently used control technique. An extension of MPC is distributed MPC (DMPC) that can be used to meet restrictions in computation time and make flexible system reconfiguration possible. This contribution presents a DMPC algorithm, which uses sensitivities that contain information about the influence of a control action on neighboring agents. Three different ways of calculation are presented. The algorithm itself performs local optimization and exchanges sensitivities on agent level until a convergence criterion is met. The method is applied to several examples to demonstrate its performance, including trajectories and time analysis. In particular, it is shown that the computation time on agent level is almost constant for an increasing system size.
Huber et al. (Mon,) studied this question.