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State estimators are used to reconstruct current plant states based on information received from plant sensors and the use of a mathematical model. The typically applied Kalman filter derivatives require knowledge about the noise statistics affecting system states and measurements. These are often unknown and inaccurate parameterization may lead to decreased filter performance or even filter divergence...
A novel observer for state, parameter and process covariance estimation is presented in this paper. The new observer estimates system states using a Square-Root Unscented Kalman Filter (SRUKF) and by employing the Recursive Prediction-Error (RPE) method, unknown parameters and covariances are identified online. Two experimental applications based on an underactuated planar robot are included to demonstrate...
In this paper a comparison of three methods for online parameter estimation is presented. The analyzed algorithms are a well known recursive least squares method (RLS), an Extended Kalman Filter (EKF) in joint state form, and an adaptive Extended Kalman Filter (aEKF). The methods' performances regarding accuracy, respond time and computing time are compared using a commercial industrial testbed, consisting...
Two observers for joint parameter and state estimation are presented in this paper. The observers are based on the Extended Kalman Filter (EKF) or the Square Root Cubature Kalman Filter (SRCuKF) and a Recursive Predictive Error (RPE) method for state and parameter estimation, respectively. Sensitivity models are introduced to compute and minimize a cost functional and then recursively estimate parameter...
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