  This paper analyzes output feedback control of a class of unknown nonlinear
systems in the presence of measurement noise using multiple high-gain observers
(MHGO). It is well-known that single high-gain observers (HGO) are not able to
provide satisfactory performance when the system output is contaminated by
noise. More specifically, there is a trade-off between the convergence speed of
state estimation and the bound of steady estimation error in HGO when the
output measurement is contaminated by noise. In the presented scheme, the
output feedback controller utilizes the state estimation obtained from an
appropriate combination of information provided by a bank of HGOs. The proposed
strategy is capable of mitigating the destructive effects of measurement noise
and speeding up the convergence process, and it does that because it introduces
an extra design parameter. The performance recovery capabilities of MHGO-based
controllers and the stability of the closed-loop system are discussed.
Simulations are performed on an underwater vehicle system and a mechanical
system to evaluate the performance of the MHGO-based controller. Furthermore, a
detailed comparison between the MHGO-based controller and controllers based on
conventional HGO, HGO with switching gain, and multi-observer approach is
provided, which shows the superiority of the MHG-based controller over the
other methods.
