Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRTN15F valid from Spring 2017

Printable view

  • 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.
  • 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
Knowledge and Understanding
  • For a passing grade the doctoral 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
Competences and Skills
  • For a passing grade the doctoral 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 doctoral 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
Types of Instruction
  • Lectures
  • Laboratory exercises
  • Exercises
  • Project
Examination Formats
  • Written exam
  • Written assignments
  • Assessment: Written exam, project, three laboratory exercises, two hand-in problems.
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
  • Johansson, R.: Predictive and Adaptive Control. Department of Automatic Control, LTH, 2010.
Further Information
Course code
  • FRTN15F
Administrative Information
  •  -10-27
  • Professor Thomas Johansson

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published
2017‑01‑17 2017‑06‑02 2016‑10‑31

Printable view