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This note extends a recently proposed algorithm for model identification androbust MPC of asymptotically stable, linear time-invariant systems subject toprocess and measurement disturbances. Independent output predictors fordifferent steps ahead are estimated with Set Membership methods. It is hereshown that the corresponding prediction error bounds are the least conservativein the considered model class. Then, a new multi-rate robust MPC algorithm isdeveloped, employing said multi-step predictors to robustly enforce constraintsand stability against disturbances and model uncertainty, and to reduceconservativeness. A simulation example illustrates the effectiveness of theapproach.

Robust multi-rate predictive control using multi-step prediction models learned from data