PUMA
Istituto di Studi sui Sistemi Intelligenti per l'Automazione     
Alessandri A. Sliding-mode estimators for a class of non-linear systems affected by bounded disturbances. In: International Journal of Control, vol. 76 (3) pp. 226 - 236. Taylor & Francis, 2003.
 
 
Abstract
(English)
The problem of state estimation for a class of non-linear systems with Lipschitz non-linearities is addressed using sliding-mode estimators. Stability conditions have been found to guarantee asymptotic convergence to zero of the estimation error in the absence of noise and non-divergence if the state perturbations and measurement noise are bounded. A method is proposed to find a suitable solution to the algebraic Riccati equation on which the design of the estimator is based. The performance of the resulting sliding-mode filter minimizes an upper bound on the asymptotic estimation error. Based on such an approach, a sliding-mode estimator may be designed so as to outperform the extended Kalman filter in practical applications with models affected by uncertainty and strong, possibly unknown non-linearities, as shown by means of simulations.
DOI: 10.1080/0020717031000067448
Subject Control Engineering
Dynamical Systems
Systems & Controls


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