Nonlinear Model Predictive Control using Set Membership approximated models

This paper investigates the design of Nonlinear Model Predictive Control (NMPC) laws using models derived with a Nonlinear Set Membership (NSM) identification method. It is shown that, with the proposed NSM approach, an existing model of the process (e.g. based on physical laws) can be employed together with measured process input/output data to derive a new model, to be used for NMPC design, and to compute a bound of the related model uncertainty. The latter is then employed to evaluate the effects of model uncertainty on the closed loop system performance. The effectiveness of the proposed approach is shown in a vehicle lateral stability control problem.