Abstract
In order to get a better performance from the Linear Extended State Observer (LESO), a Linear Active Disturbance Rejection Control (LADRC) strategy is proposed based on the Unscented Kalman Filter (UKF) identification. The UKF, with high accuracy of estimation for nonlinear distribution, is applied to identify the system information so as to provide the design parameter b 0 of the LADRC. In order to avoid the problem that the covariance matrix is non-positive and improve the numerical robustness, singular value decomposition is usedto instead of Cholesky decomposition in the UKF. It improves the ability of the LESO, and hence the performance of LADRC by reducing the uncertainty of the controlled system. Taking into account sensor noise, a tracking-differentiator is introduced to the LESO to get a better control performance. Both the LADRC based on the UKF identification and the traditional LADRC are applied to the propeller-driving system and compared in this paper. The simulation and experiment results both show that the proposed method has better control effect compared with the traditional LADRC, and the estimation ability of the LESO is stronger.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.