The interconnection of dynamic subsystems that share limited resources are found in many applications, and the control of such systems of subsystems has fueled significant attention from scientists and engineers. For the operation of such systems, model predictive control (MPC) has become a popular technique, arguably for its ability to deal with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller receiving the signals and performing the computations of output signals. However, the distributed structure of such interconnected subsystems is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decomposition to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) with activation constraints, which brings about organizational flexibility and distributed computation. Two general methods are considered for hierarchical control and optimization, namely Benders decomposition and outer approximation. Results are reported from a numerical analysis of the decompositions and a simulated application to energy management, in which a limited source of energy is distributed among batteries of electric vehicles.

This paper presents a bilevel decomposition approach to a model predictive control of resource constrained dynamic systems. The proposed approach yields a distributed solution that converges to the same optimum that would be obtained by a centralized controller. It is also showed that this decomposition enables the use of multi-core or distributed architectures. Computational analyses of experiments of synthetics problems are reported and discussed, showing the capacity of the bilevel approach in combination with parallel computation.

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