In this paper, the problem of simultaneous state and parameter estimation is studied for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. It is shown that such a nonlinear algorithm provides a rate of convergence faster than exponential, ie, faster than the classic linear algorithm. Then, the proposed parameter estimation algorithm is included in the structure of a sliding-mode state observer, providing an ultimate bound for the full estimation error and attenuating the effects of the external disturbances. Moreover, the synthesis of the observer is given in terms of linear matrix inequalities. The corresponding proofs of convergence are developed based on the Lyapunov function approach and input-to-state stability theory. Some simulation results illustrate the efficiency of the proposed adaptive sliding-mode observer.
|Number of pages||17|
|Journal||International Journal of Adaptive Control and Signal Processing|
|State||Published - Mar 2018|
Bibliographical noteFunding Information:
CONACYT, Grant/Award Number: 270504; ANR Finite4SoS, Grant/Award Number: ANR 15 CE23 0007; Government of Russian Federation, Grant/Award Number: 074-U01; Ministry of Education and Science of Russian Federation, Grant/Award Number: Project 14.Z50.31.0031
H. Ríos gratefully acknowledges the financial support from CONACYT 270504. This work was also supported in part by HoTSMoCE Inria associate team program, by ANR Finite4SoS (ANR 15 CE23 0007), by the Government of Russian Federation under grant 074-U01, and by the Ministry of Education and Science of Russian Federation (Project 14.Z50.31.0031).
- adaptive observer
- nonlinear systems