MA-623 Stochastic Systems

The course is connected to the following study programs

• PhD Programme in Engineering and Science

Course contents

The course will present the following topics in stochastic processes, with additional topics and examples presented at the lecturer's discretion.

• Stochastic processes and their descriptions

• Mathematical description of stochastic systems

• Analysis of linear systems with random inputs

• Prediction and filtering theory

• Prediction for ARMAX systems

• The Kalman filter and the Riccati equation

• Parameter estimation theory for parametric models

• Least squares and maximum likelihood estimators

• Stochastic control methods based on dynamic programmeming

• The LQG problem and the separation theorem

• Minimum variance control

• Adaptive control of stochastic systems

• Self-tuning regulators

• Direct adaptive control schemes

• Stability and convergence analysis

Selected advanced topics

Learning outcomes

This course will introduce students to the basic results of stochastic systems for estimation, identification, stochastic control and adaptive control.

Teaching methods

Lectures and examples classes

Assessment methods and criteria

Oral or Written Examination at the lecturer's discretion. Pass/Fail.