State and sensor faults estimation via a proportional integral observer

Abstract

This paper deals with the problem of fault detection and identification in noisy systems. A proportionnal integral observer with unknown inputs is used to reconstruct state and sensors faults. A mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appear as an unknown input. The noise effect on the state and fault estimation errors is also minimized. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models.

Intelligent Control of Wind Energy Conversion Systems

“…A reference model is a stable linear system without faults given by (Khedher et al, 2009(Khedher et al, , 2010, …”

“…Consider an uncertain nonlinear system that can be described by the following TS fuzzy model with parametric uncertainties and sensor faults (Khedher et al, 2009(Khedher et al, , 2010Tong & Han-Hiong, 2002). The i-th rule of this fuzzy model is given by:…”

“…The defuzzified output of (9) subject to sensor faults and parameter uncertainty is represented as follows (Khedher et al, 2009(Khedher et al, , 2010Tong & HanHiong, 2002):…”

“…First, the local state observers are designed as follows, based on the triplets (A i ,B i ,,C i ). In order to detect and estimate faults, the following fault estimation observer is constructed (Khedher et al, 2009(Khedher et al, , 2010Tong & Han-Hiong, 2002).…”

“…Since in many practical nonlinear control systems, state variables are often unavailable, output feedback or observer-based control is necessary and these aspects have received much interest. (Khedher et al,2009(Khedher et al, , 2010Odgaard et al, 2009;Tong & Han-Hiong, 2002;Tong et al, 2009 ;Wang et al, 2008;Yong-Qi, 2009;Zhang et al, 2009) fuzzy observer designs for TS fuzzy control systems have been studied, and prove that a state feedback controller and observer always result in a stabilizing output feedback controller, provided that the stabilizing property of the control and asymptotic convergence of the observer are guaranteed through the Lyapunov method. However, in the above output feedback fuzzy controllers, the parametric uncertainties for TS fuzzy control systems have not been considered.…”