Advancement in sensing technology is introducing new sensors that can provide information that was not
available before. This creates many opportunities for the development of new control systems. However,
the measurements provided by these sensors may not follow the classical assumptions from the control
literature. As a result, standard control tools fail to maximize the performance in control systems utilizing
these new sensors. In this work we formulate new assumptions on the measurements applicable to new
sensing capabilities, and develop and analyze control tools that perform better than the standard tools
under these assumptions. Specifically, we make the assumption that the measurements are quantized. This
assumption is applicable, for example, to low resolution sensors, remote sensing using limited bandwidth
communication links, and vision-based control. We also make the assumption that some of the measurements
may be faulty. This assumption is applicable to advanced sensors such as GPS and video surveillance, as
well as to remote sensing using unreliable communication links.
The first tool that we develop is a dynamic quantization scheme that makes a control system stable
to any bounded disturbance using the minimum number of quantization regions. Both full state feedback
and output feedback are considered, as well as nonlinear systems. We further show that our approach
remains stable under modeling errors and delays. The main analysis tool we use for proving these results
is the nonlinear input-to-state stability property. The second tool that we analyze is the Minimum Sum
of Distances estimator that is robust to faulty measurements. We prove that this robustness is maintained
when the measurements are also corrupted by noise, and that the estimate is stable with respect to such
noise. We also develop an algorithm to compute the maximum number of faulty measurements that this
estimator is robust to. The last tool we consider is motivated by vision-based control systems. We use a
nonlinear optimization that is taking place over both the model parameters and the state of the plant in
order to estimate these quantities. Using the example of an automatic landing controller, we demonstrate
the improvement in performance attainable with such a tool
