Parameter estimation for uncertain systems based on fault diagnosis using Takagi–Sugeno model
Résumé
The paper addresses a systematic procedure to deal with the state and parameter uncertainty estimation for nonlinear time-varying systems. This is realized by designing
a robust observer for dynamic nonlinear systems using a Takagy-Sugeno (T-S) multi-model approach with unknown premise variables and nonlinear outputs.
This approach allows to apply the tools of the linear automatic to dynamic nonlinear systems by using the Linear Matrix Inequalities (LMI) optimization.
The observer estimates the model uncertainties, the states and minimizes the effect of external disturbances on the estimation error. The model uncertainties are
included in the model in a polynomial way which allows to consider the model uncertainty estimation as a fault detection problem. The residual sensitivity to faults
while maintaining robustness according to a noise signal is handled by Hinf/H− approach. The performance of the proposed method is illustrated by using the three-tank system.