Advanced control for building applications

Welcome to Advanced control for building applications

Description: 

Despite extensive research and successful implementation of advanced control techniques, like MPC, in other fields, the application of such techniques is still limited in practice in building services engineering. One of the reasons seems to be the lack of knowledge among building service engineers about advanced control methods. There is a growing need for multidisciplinary education on advanced control methods in the built environment.

Buildings use a large share of total energy use around 35–40% in many countries. In Denmark, buildings account for 40% of the Danish energy use. Building energy-related activities are responsible for the 19% of GHG emissions worldwide. Therefore, it is motivated to investigate the energy saving potential in the building sector. Advanced building control can considerably reduce building energy use. For instance, numerous studies reported that advanced HVAC control can notably reduce energy use and mitigate GHG emissions with average energy savings of 13% to 28%.

The most popular advanced building control solution among the scientific community is Model Predictive Control (MPC) due its proven ability to handle constraints while optimizing the system performance. MPC on the supervisory level can be designed to find energy-efficient or cost-efficient control settings for the local controllers, taking into account the system level characteristics, interactions and comfort constraints. MPC combines building modelling, measurement, disturbance forecasting as well as information from external sources in the optimization formulation in order to find optimal control settings.

Prerequisites: 

• Basic knowledge of a programming language (MATLAB/Python/R)
• Knowledge of basic control methods, e.g. feedback control loop, PID controllers
• Basic knowledge of building energy modelling and thermodynamics
• Basic knowledge of linear algebra

Learning objectives: 
This course is intended for PHD students in the built environment and building service engineers, at national and international level, who want to:

• Increase their knowledge about the most recent advanced control techniques and their applications in the built environment
• learn the theory and practice of Model Predictive Control and MPC problem classes for building applications
• learn how to formulate an MPC problem for building applications
• Learn how to implement a basic MPC algorithm in a small-scale experimental mock-up

Teaching methods: 
Teaching method comprises of lecture presentations by the teacher, simulation exercises with teacher’ supervision and discussion-based experimental demonstration possibly with competition between groups of student. The structure of the course is as follows:

Day 1 (Theoretical)

• Lecture 1: A glimpse of control theory
  • Classical control
  • Modern control
• Lecture 2: Optimal control design
  • LQR control
  • Full-state estimation: observability and Kalman filter design
  • LQG control
  • MATLAB/Simulink simulation exercise

Day 2 (Simulation)

• Lecture 1: Introduction to Model Predictive Control
  • MPC without constraints
  • MATLAB/Simulink simulation exercise
• Lecture 2: Constrained MPC with general cost function
  • Introduction to CVX
  • MATLAB/Simulink simulation exercise

Day 3 (Laboratory experiment)

• Lecture 1: Introduction to the experimental setup
• Experimental exercise
  • System identification
  • Implementation of control strategies: MPC, Economic MPC, LQG, PI
  • Comparison of different control strategies on the system performance

Form of evaluation:

Criteria for assessment: 

• Attendance in all course days as scheduled is required.
• Report on the simulation and experimental results.
• Presentation of the experimental results provided by each group

Remarks:

Key literature: 

• L. Wang, Model Predictive Control System Design and Implementation Using MATLAB. Springer, 2009
• Predictive control with constraints, Maciejowski
• Statistical Modelling of Physical Systems (An introduction to Grey Box modelling), Henrik Madsen

Organizer: Professor Alireza Afshari

Lecturers: Associate Professor Samira Rahnama

ECTS: 3

Time: 26, 27, 28 May 2026

Place: Aalborg University

City: Copenhagen

Maximal number of participants: 20

Deadline: 05 May 2026

Important information concerning PhD courses: 

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.