Course Content (Syllabus)
Overview of basic principles in automatic control. Optimal control (problem definition and formulation, performance index selection, optimality conditions, solution techniques in dynamic programming). Optimal state estimation (Kalman filters, extended Kalman filters for nonlinear systems, optimization based estimation). Stochastic control systems (minimum variance controllers, H2 controllers). Robust control systems (uncertainty analysis, Η∞ controllers). Model predictive control (basic principles, linear systems and nonlinear systems, practical implementation issues). System identification (nonparametric and parametric methods), Control of distributed parameter systems (spatial norms and performance indices, model order reduction, H2 and Η∞ controllers, optimal sensor placement). Application of optimal and automatic control to turbo-engines.
Control of structural vibrations, system identification, optimal control, model predictive control
Additional bibliography for study
Strengel, R.F., Optimal control and estimation, Dover, 1994.
Zhou, K., Robust and optimal control, Prentice Hall, 1996.
Kulikov and Thomson (Eds), Dynamic modeling of gas turbines, Simulation, Identification and optimal control, Springer, 2004.