Multirate Estimation for Discrete Processes Under Multirate Noise With Application to the Grinding Process

Abstract

Asynchronous measurement of process output characterizes a series of discrete process cycles in batch production. A multirate Kalman filtering scheme was proposed by (2008, “Estimation Strategy for a Series of Grinding Cycles in Batch Production,” IEEE Trans. Contr. Syst. Technol., 16(3, pp. 556–561)) for estimating immeasurable variables through integration of other sensor signals with postprocess inspection data. In this paper, a new state-space model structure for a series of discrete process cycles is proposed based on a semicontinuous system under the process noise of multiple frequencies due to the within-cycle drift and the cycle-to-cycle variation. An improvement is made to the previous estimation scheme by deriving the propagation of estimation errors between consecutive cycles under the multirate noise. Following a simulation demonstrating the advantage of the proposed change, experiments are conducted on an actual grinding process to validate the estimation scheme.