Estimation of Time Varying Parameters in Discrete Time Dynamic Systems: A Tool Wear Estimation Example

The recursive least squares, Kalman filter, and basis function methods for the estimation of time varying parameters are described and compared for a particular example problem. A generalization of these methods for estimation of time varying parameters is presented, based on an adaptive Kalman filter algorithm. The adaptive Kalman filter (or adaptive observer) utilizes a state model with unknown coefficients, of the time varying parameters. All the other estimation methods presented for time varying parameters can be obtained as special cases of the proposed method. The method proposed shows excellent performance on the simple example problem considered, but can be difficult to apply.