Selami Beyhan*
Selami Beyhan
Izmir Demokrasi University, Electrical and Electronics Engineering, Uckuyular Dist., Karabaglar 35140, Izmir, Turkey. Email: [email protected]
*Corresponding Author: Selami Beyhan, Email: [email protected]
This paper introduces a novel hyperparameter design based on extremum seeking (ES) method to enhance the convergence speed of Extended-Kalman Filter (EKF). ES method produces a real-time optimization output based on the second-order gradient of performance function so that the estimation performance of EKF is simultaneously optimized. In addition to the convergence speed, the proposed hyperparameter reduces the effect of initial covariance matrices, and improves the accuracy of estimation for the fast changes without any knowledge about system dynamics. In numerical applications, EKF with and without the proposed hyperparameter were first used to estimate the unknown parameters of a linear time-varying system. Second, on a real-time collected data, they were applied for the joint estimation of velocity and payload mass of a real-time nonlinear servo-system where the performance improvement is provided almost 30%. Performance measurements are given in terms of the root-mean squared-error (RMSE) of estimation.
Extremum seeking, hyperparameter, EKF, velocity and payload estimation
Selami Beyhan (2023). A Novel Extremum Seeking Hyperparameter Design for Joint State and Parameter Estimation of Nonlinear Systems. Journal of Artificial Intelligence and Systems, 5, 139–146. https://doi.org/10.33969/AIS.2023050110.
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