Here, a nonlinear
plant is considered, which is operated by a decentralized
control system. The existing system ignores the interactions between
subsystems, which often results in uncaptured plantwide performance.
The focus of this paper is on the design of a distributed model predictive
control (DMPC) network using successively linearized internal models.
In this method, all existing interactions between the subsystems should
be considered in order to enhance the performance of the current decentralized
DMPC scheme. A coordination layer is added to the existing network,
while minor modifications are applied to the local MPC controllers,
to achieve the performance and stability of a hypothetical centralized
MPC for the entire plant. In this work, an interior-point algorithm
is proposed to coordinate a DMPC network via the price-driven coordination
approach. In addition, the convergence of the algorithm is shown,
and the necessary conditions to ensure the closed-loop stability of
the system are provided for the situation when the algorithm is terminated
prematurely prior to convergence.
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