Sampled data systems have come into practical importance for a variety of reasons.
The earliest of these had primarily to do with economy of design. A more recent surge of interest
was due to increase utilization of digital computers as controllers in feedback systems. This thesis
contributes some control design for a class of nonlinear system exhibition linear output. The
solution of several nonlinear control problems required the cancellation of some intrinsic dynamics
(so-called zero dynamics) of the plant under feedback. It results that the so-dened control will
ensure stability in closed-loop if and only if the dynamics to cancel are stable. What if those
dynamics are unstable? Classical control strategies through inversion might solve the problem while
making the closed loop system unstable. This thesis aims to introduce a solution for such a problem.
The main idea behind our work is to stabilize the nonminimum phase system in continuous- time
and undersampling using zero dynamics concept. The overall work in this thesis is divided into
two parts. In Part I, we introduce a feedback control designs for the input-output stabilization
and the Disturbance Decoupling problems of Single Input Single Output nonlinear systems. A
case study is presented, to illustrate an engineering application of results. Part II illustrates the
results obtained based on the Articial Intelligent Systems in power system machines. We note
that even though the use of some of the AI techniques such as Fuzzy Logic and Neural Network
does not require the computation of the model of the application, but it will still suer from some
drawbacks especially regarding the implementation in practical applications. An alternative used
approach is to use control techniques such as PID in the approximated linear model. This design
is very well known to be used, but it does not take into account the non-linearity of the model. In
fact, it seems that control design that is based on nonlinear control provide better performances
