Course content

Introduction to multivariable stochastic processes, system identification and parameter estimation in dynamic systems, nonparametric and parametric methods; least squares (LS) methods, instrumental variables (IV) methods, prediction error (PE) methods, recursive variants, the augmented Kalman filter, stability analysis of recursive methods. Stochastic adaptive control based upon the self tuning concept: minimum variance (MV) control, pole placement (PP) control, generalized predictive control (GPC), parameter estimation in closed loop systems.

Learning outcome

The course will give knowledge about tools for system identification and control of linear systems with unknown parameters.

Learning methods and activities

Lectures, compulsory laboratory project, compulsory homework assignments. Portfolio evaluation is the basis for the final grade in the subject. Parts of the portfolio are final exam in writing 80% and project and laboratory work 20%. The result for each part is given in percentage units, while evaluation of the entire portfolio (the final grade) is given as a letter. If there is a re-sit examination, the examination form may change from written to oral.

Compulsory assignments

• Exercises

Recommended previous knowledge

Courses TTK4105 Control Systems and TTK4115 Linear System Theory.

Course materials

To be announced.