An LMI approach for output feedback Robust Predictive Control using Orthonormal Basis Functions

This paper is focused on the problem of uncertain process control by using RMPC (robust model predictive control). A relevant class of RMPC algorithms is the one characterized by the use of the LMI framework. This field started in the middle of nineties and since then several works applying LMIs in the context of RMPC have been proposed. Most of them assume a polytopic representation of the process uncertainties and require full-state feedback. On the other hand, several works describing the theory and applicability of OBF (orthonormal basis functions) in identification and control fields can be found in the literature. So, the present paper proposes the use of OBF for uncertain process modelling and for RMPC algorithm synthesis. It is shown that, due to the proposed approach, one obtains an output feedback control law, with set-point tracking. This represents an advantage in actual applications. Moreover, well established state feedback RMPC strategies can be reviewed under the OBF modelling perspective. Simulation results illustrate the proposed methodology.

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