Modul RO4500-KP08

Advanced Control and Estimation (ACE)

Duration:

2 Semester

Turnus of offer:

each semester

Credit points:

Course of studies, specific field and terms:

Master Robotics and Autonomous Systems 2019 (optional subject), Elective, 1st and 2nd semester

Classes and lectures:

Graphical Models in Systems and Control (exercise, 1 SWS)
Graphical Models in Systems and Control (lecture, 2 SWS)
Linear Systems Theory (exercise, 2 SWS)
Linear Systems Theory (lecture, 2 SWS)

Workload:

30 Hours in-classroom exercises
70 Hours private studies
120 Hours in-classroom work
20 Hours exam preparation

Contents of teaching:

Content of teaching for course Linear Systems Theory:
Vector spaces, norms, linear operators
Eigenvalues, eigenvectors, Jordan normal form
Singular value decomposition and operator norms
Linear systems in continuous and discrete time
Modeling of linear systems and linearization
Fundamental solution to linear systems state equations
Laplace transform and z-transform
Content of teaching for course Graphical Models in Systems and Control:
Introduction to Probability Theory, Discretely and Continuously Distributed Random Variables
Fundamentals on Probabilistic Graphical Models
Forney-Style Factor Graphs as a Probabilistic Graphical Model
Message Passing via Sum- and Max-Produkt Algorithms
Gaussian Message Passing
State Estimation (Kalman Filtering and Smoothing including Nonlinear Extensions)
Parameter Estimation via Expectation Maximization
Expectation Propagation
Control on Factor Graphs

Qualification-goals/Competencies:

Educational objectives for course Linear Systems Theory:
Students are familiar with the important basic concepts of linear algebra.
Students have a solid background in the theory of linear systems in continuous and disrete time.
Students are able to model linear systems in mechanical and electrical domain from first principles.
Students are able to solve the state equations and analyze systems in the time and frequency domain.
Students improve their problem solving and mathematical skills.
Students develop their techniques for logical reasoning and and rigorous proofs.
Students are enabled to perform reseaerch in the field of systems and control theory.
Educational objectives for course Graphical Models in Systems and Control:
Students develop and extend their fundamental knowledge on probability theory and the transformation of discretely as well as continuously distributed random variables.
Students can understand simple linear algorithms, such as the Kalman filter, with the help of graphical probabilistic models.
Students can combine elements of probabilistic algorithms to novel ones with the help of graphical probabilistic models.
Students can understand, extend and apply advanced algorithms in signal processing, parameter and state estimation as well as control to relevant problems with the help of graphical probabilistic models.

Grading through:

Written or oral exam as announced by the examiner

Language:

offered only in English

Notes:

Admission requirements for taking the module:
- None

Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester.

Module Exam(s):
- RO4500-L1: Advanced Control and Estimation, An oral examination on the contents of both submodules, 40min, 100% of the module grade.

Letzte Änderung:

7.10.2021

Module guide