Proportional integral observer design for linear systems with time delay

PIO Output Fault Diagnosis by ARX-Laguerre Model Applied to 2 nd Order Electrical System

“…To reconstitute or estimate the augmented representation (20) with output fault, we propose the development of a proportional-integral observer based on the ARX-Laguerre model defined by (18). In this case, the representation (18) is written in this form:…”

“…This last is developed and amply applied by many researchers in linear case by using a high-gain observer associated to the both terms proportional and integral applied to the output estimation error. In fact, the PIO allows to have an unbiased estimate of the state as well as the detection and estimation of unknown inputs despite the presence of uncertainties or unknown inputs [6], [8], [20]. The PIO requires knowledge of the suitable model for representing the dynamic behavior of the system.…”

Observer design for networked control systems

“…So researches of the state observers design for the time delay systems have become one of important topics in the control theory and engineering applications. Most of the state reconstruction methods were developed for time-delay systems, one can see [12,13] and the references therein. In [12], a robust proportional integral observer design approach has been proposed for systems with state time-delay.…”

“…Most of the state reconstruction methods were developed for time-delay systems, one can see [12,13] and the references therein. In [12], a robust proportional integral observer design approach has been proposed for systems with state time-delay. In [13], the problem of observer-based stabilization of a class of time-delay nonlinear systems written in triangular form has been studied.…”

Pole placement design of proportional-integral observer with stochastic perturbations

“…The observer designs for linear systems are well-known (Chen, 1998;Kalman, 1963) techniques. However, observer design issues on a linear system with stochastically noised disturbances are still under investigation, especially for applications, such as chemical process system (Mohd Ali et al, 2015), nonlinear control (Targui et al, 2011), time delay system (Yao and Radun, 2007), and Lithium-ion battery (Xu et al, 2014), where handling disturbances may appeal to researchers utilizing probability models that deteriorate the performance of state estimation in the observer mechanism. Dynamical systems arising from material, biological, electromagnetic, or vehicular sciences are often subject to stochastic influences (Xu et al, 2008;Chen andHo, 2013, Franceschetti et al, 2010;He et al, 2010).…”