State space based dual-rate self-tuning

A state space based dual-rate self-tuning algorithm is proposed for the case where the output sampling rate is slower than the input update rate. A state space innovation model suitable for dual-rate self-tuning control is derived. The parameters of the innovation model together with the states are estimated by a recursive prediction error estimator. The control strategy has been based on a pole restriction principle. Simulation studies are presented to establish the practical usefulness of the algorithm.