A model-based approach to fault-tolerant control

Abstract

A model-based controller architecture for Fault-Tolerant Control (FTC) is presented in this paper. The controller architecture is based on a general controller parameterization. The FTC architecture consists of two main parts, a Fault Detection and Isolation (FDI) part and a controller reconfiguration part. The theoretical basis for the architecture is given followed by an investigation of the single parts in the architecture. It is shown that the general controller parameterization is central in connection with both fault diagnosis as well as controller reconfiguration. Especially in relation to the controller reconfiguration part, the application of controller parameterization results in a systematic technique for switching between different controllers. This also allows controller switching using different sets of actuators and sensors.

Active Fault Diagnosis in Sampled-data Systems

“…It is possible to apply the same setup for AFD in sampleddata system as described in Niemann [2006Niemann [ , 2012, Poulsen and Niemann [2008]. However, an LFT structure in the continuous-time system will not in general be preserved when the system is discretized.…”

“…Fault diagnosis for sampled-data systems has only been touched briefly and no detailed analysis had been given. The AFD approach for closed-loop systems described in Niemann [2006] and later used in Niemann [2012], Poulsen and Niemann [2008] depends on the structure in the system. It is assumed that an LFT representation of the system with parametric faults is valid.…”

On infinite horizon active fault diagnosis for a class of non-linear non-Gaussian systems

“…Such a problem was addressed first for stochastic models (Zhang, 1989;Kerestecioglu, 1993) and later on for deterministic uncertain models (Scola et al, 2003;Campbell and Nikoukhah, 2004;Andjelkovic et al, 2008;Niemann, 2012;Ashari et al, 2012a;Scott et al, 2013). The input signal designed to improve the quality of detection can be inconvenient from a control perspective.…”

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

“…The effectiveness of contemporary Fault Detection and Isolation (FDI) systems (Ding, 2008;Korbicz et al, 2004;Korbicz and Kościelny, 2010;Palade et al, 2006;Witczak, 2007) and Fault Tolerant Control (FTC) schemes (Ichalal et al, 2012;Isermann, 2005;Niemann, 2012;Noura et al, 2009;Montes de Oca et al, 2012) mostly depends on the quality of the models obtained in the process of system identification. Artificial Neural Networks (ANNs) are often applied in the process of dynamic non-linear industrial system identification (Gupta et al, 2003;Haykin, 2009;Mrugalski et al, 2008).…”