An unknown input observer provides perfect asymptotic tracking of the state of a system affected by unknown inputs. Such an observer exists (possibly requiring a delay in estimation) if and only if the system satisfies a property known as strong detectability. In this thesis, we consider the problem of selecting (at design-time) a minimum cost subset of sensors from a given set to make a given system strongly detectable. We show this problem is NP-hard even when the system is stable. Furthermore, we show it is not possible to approximate the minimum cost within a factor of log(n) in polynomial-time (unless P=NP). However, we prove if a given system (with a selected set of sensors) is already strongly detectable, finding the smallest set of additional sensors to install to obtain a zero-delay observer can be done in polynomial time. Next we consider the problem of attacking a set of deployed sensors to remove the property of strong detectability. We show finding the smallest number of sensors to remove is NP-hard. Lastly through simulations, we analyze two greedy approaches for approximating the strong detectability sensor selection problem.

In 1952 Alan Turing published his paper "The Chemical Basis of Morphogenesis", which described a model for how naturally occurring patterns, such as the stripes of a zebra and the spots of a leopard, can arise from a spatially homogeneous steady state through diffusion. Turing suggested that the concentration of the substances producing the patterns is determined by the reaction kinetics, how the substances interact, and diffusion.  In this project Turing's model with linear reactions kinetics was studied. The model was first solved using two different numerical methods; the finite difference method (FDM) and the finite element method (FEM) with different boundary conditions. A parameter study was then conducted, investigating the effect on the patterns of changing the parameters of the model. Lastly the controllability of the model and the least energy control was considered. The simulations were found to produce patterns provided the right parameters, as expected. From the investigation of the parameters it could be concluded that the size/tightness of the pattern and similarity of the substance concentration distributions depended on the choice of parameters. As for the controllability, a desired final state could be produced thorough simulations using control of the boundary and the energy cost of producing the pattern increased when decreasing the number of controls.

This thesis examines Turing patterns and how systems control theory can be used to steer them. The mathematical tools used in this thesis consist of linear algebra, partial differential equations (PDE), numerical simulations of PDEs and systems control theory. These topics will all be briefly introduced. Following that will be a section on the numerical study of Turing patters. The bulk of the thesis will focus on network controllability. The subjects explored are an analytical study of controllability, the simulations of specific patterns using controllability and a numerical analysis of the minimal energy control for a Turing system