Automatic Control
Important Information:
| The course Automatic Control will be permanently moved from summer to winter semester in 2025. This means no lecture will be held in summer 2025! As a substitute, video lectures will be made available for download. Exams will be available every semester as usual. |
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.