Observer/Kalman Filter Identification With Wavelets

Understanding the dynamic characteristics of the target system is a fundamentally important step in designing reliable closed loop control systems. One method for identifying linear models from the target uses the observer/Kalman filter identification/eigensystem realization algorithm (OKID/ERA) combination. This focuses on time domain representations of plant input and output data. The wavelet transform is capable of providing an efficient mixed time-frequency domain representation of time domain data. We have investigated if this would give any benefit for OKID/ERA. We show how to apply the wavelet transform within the OKID creating a new wavelet-OKID/ERA technique and then compare its performance, using common discrete wavelet families, against a standard procedure. Results indicate that the potential attractiveness of the wavelet approach do not translate into a practical reality.