Augmented UD identification (AUDI) technique is derived from the traditional recursive least squares algorithm and had been developed rapidly during last decade. However, as the identification process evolves, AUDI algorithm falls easily into identification saturation, which means that AUDI algorithm cannot respond to time varying system parameters unless a set of very strong identification signals is utilized or a long identification period is occupied. To overcome such a difficulty, a novel resetting AUDI (RAUDI) strategy is advanced by resetting the augmented information matrix based on MF (Monitor Function) monitoring the conspicuous change of process parameters. The numeric experiment demonstrates that the RAUDI has a good performance in estimation of rapid parameter changes. Augmented UD identification (AUDI) technique is derived from the traditional recursive least squares algorithm and had been developed rapidly during last decade. However, as the identification process evolves, AUDI algorithm falls easily into identification saturation, which means that AUDI algorithm can not respond to time varying system parameters unless a set of very strong identification signals is utilized or a long identification period is occupied. To overcome such a difficulty, a novel resetting AUDI (RAUDI) strategy is advanced by resetting the augmented information matrix based on MF (Monitor Function) monitoring the conspicuous change of process parameters. The numeric experiment demonstrates that the the RAUDI has a good performance in estimation of rapid parameter changes.