Applications of Advanced Methods

  • Oliver Nelles


This chapter gives an overview of the way in which nonlinear dynamic models can be utilized for control and fault detection. As an illustrative application example, the thermal plant presented in Sect. 23.3 is chosen. Section 24.1 discusses the design of a predictive controller based on a local linear neuro-fuzzy model. Online adaptation of this model yields a nonlinear adaptive controller. It allows an adjustment to time-variant behavior and changing environmental conditions — in this particular example to a changing environment temperature. As pointed out in Sect. 24.2, some precautions must be taken to make adaptive control robust against insufficient excitation. Section 24.3 introduces the topic of model-based fault detection, and Sect. 24.4 briefly addresses the subject of fault diagnosis, i.e., the determination of the fault cause. Finally, a reconfiguration strategy for the controller is discussed, based on the fault diagnosis results obtained. This chapter can only offer a glimpse of the topics addressed. For a more detailed treatment, the reader is referred to the vast literature on nonlinear control and fault diagnosis.


Fault Detection Fault Diagnosis Fuzzy Model Sensor Fault Manipulate Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Oliver Nelles
    • 1
  1. 1.UC Berkeley / TU DarmstadtKronbergGermany

Personalised recommendations