This article addresses the filtering of position, speed, and acceleration in servo motor applications by confronting practical challenges such as model dependence, complex matrix computations, and inconsistent performance. The proposed filter is systematically derived through the integration of a disturbance observer (DOB) design and a high-order pole–zero cancellation (PZC) technique, yielding the following contributions. First, a double-integral, offset-free position filter reduces the order of the filtering error dynamics by employing nonlinearly parameterized gains. Second, the combination of the DOB-based system with these gains facilitates the design of speed and acceleration filters based on filtered position measurements. Third, the diagonalization of the filtering error dynamics via nonlinear gain parameterization simplifies the tuning process for desired performance. The experimental validation using a 500-W brushless dc motor-based dynamometer demonstrates a 27% improvement in feedback system performance compared to a well-tuned extended state observer (ESO)
