EKF Moisture Estimation in Slider-Crank Organic Waste Presses: Angle-Synchronized vs. Revolution-Averaged Motor Current Analysis

A prior simulation study demonstrated that the Extended Kalman Filter (EKF) applied to induction motor stator current can estimate the moisture content of pressed organic waste with a root-mean-square error (RMSE) of 3.18%, assuming a constant mechanical lever arm. In practice, the slider-crank mechanism selected for the AENG-OWP-15 household organic waste processor imposes a crank-angle-dependent lever arm reff(θ), introducing rapid sinusoidal oscillations into the motor torque and current signal. This paper extends the baseline EKF design to handle this compound signal, evaluating three architectural approaches: (1) a Naive application of the EKF to the raw angle-modulated signal; (2) Angle-Synchronized sampling restricted to a ±15^∘ window around θ = 90^∘ where the lever arm is maximal; and (3) Revolution-Averaged current processing, which filters out angle dependence entirely over a complete crank revolution. Simulation results for a 60-second press cycle indicate that while the Naive EKF fails