A New Robust Predictive Control Design based on Laguerre Expansions

摘要

In this paper, we develop a new robust predictive control (RPC) algorithm for linear SISO systems using a reduced complexity model represented in Laguerre orthonormal base. This method is based on a worst-case strategy that consists in solving a min-max optimization problem taking into account the constraints relative to the parameter uncertainties of the resulting model coefficients and to measurement signals. To simplify the optimization problem min-max view to reducing the number of constraints, we introduce LMIs techniques. However, the uncertainty domain of parameters is an ellipsoid updated by applying the Unknown But Bounded Error (UBBE) approaches.