Syllabus 2008/2009 FRTN15

Syllabus academic year 2008/2009
(Created 2008-07-17.)

PREDICTIVE CONTROL

FRTN15

Higher education credits: 7,5. Grading scale: TH. Level: A (Second level). Language of instruction: The course will be given in English on demand. FRTN15 overlap following cours/es: FRT050 och FRT050. Optional for: C4, C4sst, D4, D4sst, E4, E4ra, F4, F4rs, I4, Pi4. Course coordinator: Professor Rolf Johansson, Rolf.Johansson@control.lth.se och Professor Karl-Erik Årzén, karl-erik.arzen@control.lth.se, Inst f reglerteknik. Recommended prerequisits: FRT010 Automatic Control, Basic Course, FRT020 Computer Controlled Systems. The course might be cancelled if the numer of applicants is less than 10. Assessment: Written exam (5 hours), project, three laboratory exercises, two hand-in problems. In the case of less than five registered students, the second and third exam may be given on oral form. Home page: http://www.control.lth.se/course/FRTN15/.

Aim
The aim of the course is to provide advanced knowledge and skills about design of control systems that include predictive, adaptive and learning algorithms for control of time-variable and partially unknown processes with disturbances, including stability and interaction between control and identification.

Knowledge and understanding
For a passing grade the student must

be able to define the basic concepts for systems with muliple input and output signals
be able to translate between different multivariable system descriptions, in particular transient responses, transfer function matrices, and state-space descriptions
be able to compute the properties of interconnected systems from the properties of the subparts
be able to show how limitations in process knowledge put limitations on the achievable control performance
understand possibilities and limitations in the use of adaptation and learning

Skills and abilities
For a passing grade the student must

be able to formulate control specifications for predictive control
be able to translate control specifications for model-based control problems
be able to draw conclusions from the results of predictive control about model plausibility and specifications

Judgement and approach
For a passing grade the student must

understand relations and limitations when simplified models are used to describe complex and multivariable systems
show ability for teamwork and group collaboration during laboratory experiments and projects

Contents
Real-time identification, Recursive identification, Automatic controller tuning, Gain scheduling, Automatic calibration, Discrete-time linear systems, Pole-placement, Model reference system, Disturbance models, Optimal prediction, Optimal model-based predictive control, Adaptive control, Self-tuning control, Stochastic adaptive control, Model reference adaptive control, Stability, Passivity, Robustness, Model-predictive control, Iterative learning control, Iterative controller tuning, Applications and software

Literature
R. Johansson: Predictive and Adaptive Control, Dept of Automatic Control, 2007