TY - GEN
T1 - New state observer gain tuning methodology based on the stable margin theory
AU - Ogawa, Kenji
AU - Ohnishi, Kouhei
AU - Ibrahim, Yousef
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/4/26
Y1 - 2017/4/26
N2 - This paper proposes a new observer gain tuning methodology to achieve a robust position control. The state observer is often used to estimate the state of the system. Generally, the observer gain is tuned by an evaluation function and the operator needs to arbitrarily select a weight function to obtain an optimal observer gain. This paper introduces a methodology for observer gain tuning using the stability margin theory. In addition, the state observer is combined with the Disturbance Observer (DOB) to achieve a robust position control. The validity of the proposed methodology was tested and confirmed by simulations and experimental study.
AB - This paper proposes a new observer gain tuning methodology to achieve a robust position control. The state observer is often used to estimate the state of the system. Generally, the observer gain is tuned by an evaluation function and the operator needs to arbitrarily select a weight function to obtain an optimal observer gain. This paper introduces a methodology for observer gain tuning using the stability margin theory. In addition, the state observer is combined with the Disturbance Observer (DOB) to achieve a robust position control. The validity of the proposed methodology was tested and confirmed by simulations and experimental study.
UR - http://www.scopus.com/inward/record.url?scp=85019582645&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019582645&partnerID=8YFLogxK
U2 - 10.1109/ICIT.2017.7915440
DO - 10.1109/ICIT.2017.7915440
M3 - Conference contribution
AN - SCOPUS:85019582645
T3 - Proceedings of the IEEE International Conference on Industrial Technology
SP - 677
EP - 682
BT - 2017 IEEE International Conference on Industrial Technology, ICIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Industrial Technology, ICIT 2017
Y2 - 23 March 2017 through 25 March 2017
ER -