A robust multi-model predictive controller for distributed parameter systems

In this work a robust nonlinear model predictive controller for nonlinear convection–diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given.Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.

► A multi-model approach for MPC of distributed parameter systems has been developed.
► Dynamic Model approximations are built by POD and projection methods.
► Loop stability is ensured by model selection based on allowable process-model mismatch.
► The performance of MMPC is demonstrated on the control of an unstable tubular reactor.