Showing papers by "Hassan K. Khalil published in 2006"

Differentiation with High-Gain Observers the Presence of Measurement Noise

Abstract: The error in estimating the derivative(s) of a noisy signal by using a high-gain observer is studied and quantified. The error is bounded in terms of the infinity norms of the noise and a derivative of the signal. The error bound is independent of the frequency or derivatives of the noise. Guidelines are presented for the observer gain design when it is used for on-line differentiation. Analytical and simulation results are presented.

Robust Feedback Linearization using Extended High-Gain Observers

Abstract: We consider a partially feedback linearizable system with stable zero dynamics. The system is uncertain and only the output is measured. Consequently, exact feedback linearization is not applicable. We propose to design an extended high-gain observer to recover unmeasured derivatives of the output and an extra one, which contains information about the uncertainty. The observer can be stabilized via feedback linearization followed by a linear control design, such as pole placement or LQR. After a short peaking period, a partial state vector, which includes the output and its derivatives, will be in a small neighborhood of the state of the observer; therefore, the performance achievable under exact feedback linearization will be recovered.

Multirate Sampled-Data Output Feedback Using High-Gain Observers

Abstract: We consider multirate sampled-data output feedback control of a class of nonlinear systems using high-gain observers where the analog-to-digital sampling rate is faster than the digital-to-analog sampling rate. We show that if a single-rate sampled-data state feedback controller globally stabilizes the origin, then the multirate output feedback controller achieves semiglobal practical stabilization.