Abstract
Abstract A distributed model predictive control (DMPC) strategy brings interesting features of topology, flexibility and maintenance to large-scale nonlinear systems. This work introduces two new cooperative distributed nonlinear model predictive control strategies with automatic partitioning. The first approach is based on oriented graphs of the linearized model of the plant and the second one based on incidence matrices of the nonlinear model of the plant. The proposed strategies were applied to a benchmark plant and compared to other control structures, obtaining promising results.
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