High-gain observers are a prevalent and an important topic in state estimation and output feedback control of nonlinear systems. In the absence of measurement noise, this technique robustly estimates the derivatives of the output while achieving fast convergence. Moreover, for a sufficiently fast observer and a globally bounded controller, the high-gain observer is able to recover the system performance achieved under state feedback control.However, in the presence of measurement noise, a tradeoff exists between the measurement noise sensitivity and the speed of state reconstruction. As the observer gain is increased, the bandwidth of the observer is extended. As the bandwidth increases, the high-gain observer asymptotically approaches the behavior of a differentiator,exacerbating the presence of measurement noise.This dissertation addresses the challenging performance issues that arise when implementing high-gain observers in the presence of measurement noise. In particular, we focus on the tradeoff between fast state reconstruction, minimizing the bound on the steady-state estimation error, and rejecting the model uncertainty. The observerdesign and analysis is approached through three major thrust areas: observer structure, tracking performance and filtering.
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