On-Line Model Identification and Control Using the Kalman Filter.

Abstract

Abstract : For some systems a model determined off-line can be satisfactorily used to design control systems. However, for many systems, the best values for the model parameters will change as the system operation varies. For effective control these parameter changes must be identified on-line and incorporated into the digital control strategy. Equally as important as the identification of changing parameters is the identification of unmeasured disturbances which upset the system operation. Ideally, the identification of model parameters and unmeasured disturbances would be accomplished by measuring only the controlled variable. One method which holds promise is the extended Kalman filter. The Kalman filter, which was developed for rejecting noise from measurements, is applied in this paper to problems of parameters and disturbance identification and state estimation of first- and second-order processes. The primary objective is to evaluate its performance for estimating the unmeasured disturbances encountered so frequently in operating control systems. (Author)