Automatic Control
Introduction to the advanced control theory consisting of
State-Space Control, Digital Control and Predictive
Control. Additionally, the concept of optimization is
introduced and the main aspects of different optimization
problems and optimization solvers are taught.
1. State-Space Control
1.1 Dynamic Systems in State-Space Representation
1.2 Solving State-Space Equations
1.3 Properties of State-Space Equations
1.4 State-Space Control via Pole Placement
1.5 State-Space Control via Optimization (LQ)
1.6 State Observer
1.7 State-Space Control with Observer (LQG)
1.8 Tracking
1.9 Reference Variable and Disturbance Model
2. Digital Control
2.1 Introduction
2.2 Brief Overview: Discrete-Time Systems
2.3 Stability of Discrete-Time Systems
2.4 Deadbeat Control
2.5 Adaptive Control
3. Optimization: Linear in the Parameters
3.1 Introduction
3.2 Linear Problems
3.3 Quadratic Problems
4. Predictive Control
4.1 Introduction to Predictive Control
4.2 Linear Predictive Control
4.3 Constraints in Predictive Control
4.4 Nonlinear Predictive Control
5. Optimization: Nonlinear in the
Parameters
5.1 Search Algorithms
5.2 Gradient Method
5.3 Newton’s Method
5.4 Quasi-Newton-Method
5.5 Conjugate Gradient Method
5.6 Line Search
5.7 Nonlinear Problems with Constraints
5.8 Global Search Methods
5.9 Multi-Objective Optimization
Timeschedule:
Exam:
Written Exam of 2 hours
duration
Exercise:
Roughly every second week,
calculation and simulation tasks are solved.