Abstract
Sensor-less speed estimation of brushed DC motor is preferred for dynamic control and state monitoring. Ripple-based and model-based methods are widely applied for sensor-less speed estimation. This paper firstly offers a ripple-based technique, analyzes their features and performance, then presents a modified Kalman filter to fuse the ripple-base and model-based results. The two source data are fused through modification of the noise covariance matrices of the conventional Kalman filter. Experimental validates the proposed algorithm. Test shows that the proposed method reduces speed estimation to less 3% and owns roughly 8 times of accuracy comparing with conventional ripple-based and model-based methods. The proposed method is suitable for speed monitoring of industrial applications involving brushed DC motor.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
