Variable Forgetting Factor-Based Adaptive Kalman Filter With Disturbance Estimation Considering Observation Noise Reduction

This paper addresses the influence reduction of quantization and observation noises in a disturbance observer (DOB) technique. DOB is estimation method that makes control systems robust. However, implementing low-resolution sensors, estimates from are considerably influenced by noises. In this paper, novel design for simultaneous state unknown disturbances, including noise influences, proposed. The proposed divided into two components. first component Kalman filter (KF)-based disturbances. To improve performance through KF-based DOB, forgetting factor-based adaptive KF (FAKF) was employed. second an law factor FAKF. used balancing accuracy reduction. Simulation results involving various types environments demonstrate effectiveness method.