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Active fault‐tolerant control of sampled‐data nonlinear distributed parameter systems
Author(s) -
Ghantasala Sathyendra,
ElFarra Nael H.
Publication year - 2011
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1799
Subject(s) - control theory (sociology) , control reconfiguration , actuator , fault detection and isolation , nonlinear system , observer (physics) , sampling (signal processing) , fault tolerance , controller (irrigation) , computer science , fault (geology) , control engineering , engineering , filter (signal processing) , control (management) , artificial intelligence , distributed computing , physics , quantum mechanics , seismology , agronomy , computer vision , biology , embedded system , geology
SUMMARY This work presents an integrated fault detection and fault‐tolerant control architecture for spatially distributed systems described by highly dissipative systems of nonlinear partial differential equations with actuator faults and sampled measurements. The architecture consists of a family of nonlinear feedback controllers, observer‐based fault detection filters that account for the discrete measurement sampling, and a switching law that reconfigures the control actuators following fault detection. An approximate finite‐dimensional model that captures the dominant dynamics of the infinite‐dimensional system is embedded in the control system to provide the controller and fault detection filter with estimates of the measured output between sampling instances. The model state is then updated using the actual measurements whenever they become available from the sensors. By analyzing the behavior of the estimation error between sampling times and exploiting the stability properties of the compensated model, a sufficient condition for the stability of the sampled‐data nonlinear closed‐loop system is derived in terms of the sampling rate, the model accuracy, the controller design parameters, and the spatial placement of the control actuators. This characterization is used as the basis for deriving appropriate rules for fault detection and actuator reconfiguration. Singular perturbation techniques are used to analyze the implementation of the developed architecture on the infinite‐dimensional system. The results are demonstrated through an application to the problem of stabilizing the zero solution of the Kuramoto–Sivashinsky equation. Copyright © 2011 John Wiley & Sons, Ltd.