TY - GEN
T1 - State Estimation of LPV Discrete-Time Systems with Application to Output Feedback Stabilization
AU - Chaib-Draa, K.
AU - Zemouche, A.
AU - Rajamani, R.
AU - Wang, Y.
AU - Bedouhene, F.
AU - Karimi, H. R.
AU - Laleg-Kirati, Taous-Meriem
N1 - KAUST Repository Item: Exported on 2020-04-23
PY - 2019
Y1 - 2019
N2 - This paper deals with new finite-time estimation algorithms for Linear Parameter Varying (LPV) discrete-time systems and their application to output feedback stabilization. Two exact finite-time estimation schemes are proposed. The first one provides a direct and explicit estimation algorithm based on the use of delayed outputs, while the second one uses two combined asymptotic observers, connected by a condition of invertibility of a certain time-varying matrix, to recover in a finite-time the solution of the LPV system. Furthermore, a stabilization strategy is proposed as an extension. This strategy, called Two Connected Observers Feedback (2-COF) stabilization method, is based on the use of the two combined observers based estimation algorithm.
AB - This paper deals with new finite-time estimation algorithms for Linear Parameter Varying (LPV) discrete-time systems and their application to output feedback stabilization. Two exact finite-time estimation schemes are proposed. The first one provides a direct and explicit estimation algorithm based on the use of delayed outputs, while the second one uses two combined asymptotic observers, connected by a condition of invertibility of a certain time-varying matrix, to recover in a finite-time the solution of the LPV system. Furthermore, a stabilization strategy is proposed as an extension. This strategy, called Two Connected Observers Feedback (2-COF) stabilization method, is based on the use of the two combined observers based estimation algorithm.
UR - http://hdl.handle.net/10754/662165
UR - https://ieeexplore.ieee.org/document/9030188/
UR - http://www.scopus.com/inward/record.url?scp=85082479459&partnerID=8YFLogxK
U2 - 10.1109/CDC40024.2019.9030188
DO - 10.1109/CDC40024.2019.9030188
M3 - Conference contribution
SN - 9781728113982
SP - 3788
EP - 3792
BT - 2019 IEEE 58th Conference on Decision and Control (CDC)
PB - IEEE
ER -