Course - Linear System Theory - TTK4115
Linear System Theory
About
About the course
Course content
Theory for linear multivariable systems, state space models, discretization, canonical forms and realizations, stability, controllability and observability, state feedback, LQR optimal control, state estimation, the Kalman filter, descriptions of stochastic processes and random signals.
Learning outcome
Knowledge: Detailed knowledge about state space representation of linear time invariant systems in continuous and discrete time. Knowledge about representation and characterization of random signals in linear systems. Detailed knowledge about fundamental concepts like controllability, observability and stability for linear multivariable systems. Substantial knowledge about methods for construction of multivariable controllers for linear systems, and algorithms and methods for state estimation. Use of state estimation for feedback in linear control systems. Knowledge of linearization of nonlinear systems.
Skills: Being able to formulate specifications and dynamic models as a basis for design of linear control systems and state estimators under influence of noise and disturbances. Being able to transform models between continuous and discrete time, and between state space and transfer function matrix representations. Being able to design and tune parameters of controllers with LQR and pole placement. Being able to design and tune parameters of observers and Kalman filters. Being able to apply linear algebra and Matlab for analysis and design of linear control systems. Being able to apply Simulink for rapid prototyping and experimental testing of control systems.
General competence: Understand strengths and limitations of theoretical analysis versus experimental testing. Good system understanding, i.e. how sub-systems consisting of hardware, software, physical systems and humans interact. Have a solid basis for advanced studies in control engineering.
Learning methods and activities
Lectures, a compulsory project, compulsory assignments.
Compulsory assignments
- Exercises
Further on evaluation
Portfolio evaluation is the basis for the final grade in the course. Parts of the portfolio are final written exam (50%) and project assignment (50%). The result for each part is given in percentage units, while evaluation of the entire portfolio (the final grade) is given as a letter. To pass the course, both the exam and project assignment must each be given a passed grade level. If there is a re-sit examination, the examination form may change from written to oral. In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety.
Recommended previous knowledge
Calculus 1, 2, 3 og 4, TTK4105 Control Systems, and Statistics, or similar background.
Course materials
Information will be given when the course starts.
Primary material is from:
- Chi-Tsong Chen. Linear system theory and design. Oxford University Press, New York, 4th international edition, 2014.
- Robert Brown. Introduction to random signals and applied Kalman filtering : with MATLAB exercises. John Wiley & Sons, Inc., Hoboken, NJ, 4th edition, 2012.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| SIE3015 | 7.5 sp |
Subject areas
- Technological subjects
Contact information
Course coordinator
Department with academic responsibility
Examination
Examination
Ordinary examination - Autumn 2021
School exam
The specified room can be changed and the final location will be ready no later than 3 days before the exam. You can find your room location on Studentweb.