# 376.086 Optimization-Based Control Methods This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2025S 2024S 2023S

2023S, VU, 3.0h, 4.5EC

## Properties

• Semester hours: 3.0
• Credits: 4.5
• Type: VU Lecture and Exercise
• Format: Presence

## Learning outcomes

After successful completion of the course, students are able to use the concepts of mathematical optimization as well as optimization-based methods for system design, estimation of parameters, trajectory planning, observer design (estimator for non-measurable prozess variables), design of controllers and feedback controllers, and to solve practical problems. Successful completion of this course enables students to identify and mathematically formulate optimization problems in practical control design problems and to select, analyze, implement, simulate, adapt, and apply solution methods appropriate for the respective control task. This course strengthens and deepens engineering approaches, abstract and analytical thinking, independent solution of practical optimization problems, as well as mathematical skills.

## Subject of course

Model predictive control:
Basic idea, optimization problem and its components, variants of model predictive control, stability analysis, suboptimal model predictive control, implementation

Receding horizon estimation:
Basic idea, optimization problem and its components, stability of state observers, state estimation with full information, state estimation using a receding horizon, maximum-a-posteriori state estimation, modifications for parameter estimation

Optimization-based estimation:
parametric linear and nonlinear models, collinearity of parameters, observability Gramian matrix, optimal placement of sensors

## Teaching methods

The contents of this lecture are elaborated and discussed based on lecture notes and exercise notes (both documents freely available). The material is presented on the blackboard and with slides. To deepen, reinforce, and practically apply the material, example problems are discussed and mathematically solved. The software Matlab is used for computer-aided solution of optimization problems. In some cases, the developed solutions are practically implemented and tested on laboratory experiments.

## Mode of examination

Oral

This course consists of lectures and exercises. All events are held in presence. If necessary, a switch to distance learning is possible on short notice.

• Lecture: All lectures are held in presence at the times given in Course dates. The first lecture (including a preview on the organization of the course) starts on 6.3.2023 at 8:00.

• Exercise: All three exercises are held as presence learning events in the computer lab of the institute ACIN (room CA0426). Each exercise consists of two parts. 1) Review and discussion of prepared problems, which are the basis for the presence event and which have to be solved beforehand. 2) Solution of further problems and in parts test on lab experiments.

Each exercise is scheduled for two hours.

All contents of the exercises are part of the final exam. The goal of the exercises is to apply the theoretical concepts, mehtods, and algorithms presented in the lecture to specific examples. The focus lies on the use of numeric software.

## Course dates

DayTimeDateLocationDescription
Mon08:00 - 10:0006.03.2023 - 26.06.2023EI 3A Hörsaal Vorlesung
Fri14:00 - 16:0028.04.2023 Computerlabor E376, CA0426Exercise 1
Fri14:00 - 16:0002.06.2023 Computerlabor E376, CA0426Exercise 2
Fri14:00 - 16:0023.06.2023 Computerlabor E376, CA0426Exercise 3
Optimization-Based Control Methods - Single appointments
DayDateTimeLocationDescription
Mon06.03.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon13.03.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon20.03.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon27.03.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon17.04.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon24.04.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Fri28.04.202314:00 - 16:00 Computerlabor E376, CA0426Exercise 1
Mon08.05.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon15.05.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon22.05.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Fri02.06.202314:00 - 16:00 Computerlabor E376, CA0426Exercise 2
Mon05.06.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon12.06.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Mon19.06.202308:00 - 10:00EI 3A Hörsaal Vorlesung
Fri23.06.202314:00 - 16:00 Computerlabor E376, CA0426Exercise 3
Mon26.06.202308:00 - 10:00EI 3A Hörsaal Vorlesung

## Examination modalities

The performance is evaluated in an oral exam in presence, which can take place at any time Monday to Friday. To arrange a time for the examination, send an e-mail with desired dates, times or time slots, your name, student ID number, and study code to steinboeck@acin.tuwien.ac.at.

Not necessary

## Curricula

Study CodeObligationSemesterPrecon.Info
066 503 Electrical power engineering and sustainable energy systems Mandatory elective
066 515 Automation and Robotic Systems Mandatory elective