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A trajectory tracking strategy is presented for processes described by a parabolic nonlinear distributed parameter model. A model-based predictive control approach, combined with an internal model control structure and a state estimation method is extended to this kind of process. On-line requirements such as computational time and constraint satisfaction are outlined and discussed. This method is...
In the first part of this work, we present two different architectures for distributed model predictive control (DMPC) of nonlinear process systems: sequential distributed model predictive control and iterative distributed model predictive control. In the present work, we focus on the application of the theoretical results developed in to a catalytic alkylation of benzene process example, which consists...
The multiphase latent variable model predictive control (MLV-MPC) is developed based on the principal component analysis (PCA) model. The proposed control methodology is capable of trajectory tracking as well as disturbance rejection. The model that is used in the course of MPC is a multiphase PCA model that is constructed based on the available data from the measurements on the process. Different...
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