The course aims to introduce students to the
main methods of synthesis of controllers for multivariable linear
continuous-time and discrete-time dynamical systems. The definitions of
sensitivity, complementary sensitivity and control sensitivity function
are extended and their characteristics are analyzed using appropriately
defined performance indices. State estimation for deterministic and
stochastic systems are presented with particular emphasis on the Kalman
complementary sensitivity and control sensitivity function.
Representations of uncertainty. Analysis of robustness and performance.
Linear Quadratic Control
Problem formulation, solution algorithms, properties of robustness.
for deterministic systems. Kalman filter and predictor. Linearized and
extended predictor. Applications to the estimation of uncertain
parameters and diagnostics industry. H2 control.
Model Predictive Control
Problem definition. Open and clsed-loop solution. Stability.