Welcome to Advanced control for building applications

Organizer: Alireza Afshari

Lecturers: Samira Rahnama, Mahmood Khatibi

ECTS: 3.0 (28 hours of work load per ECTS)

Time: 2-4 December 2024

Place: Aalborg University

Zip code: 9220

City: Aalborg

Number of seats: 20

Deadline: 11 October 2024

Description: 

Topic, background and motivation for the course:

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 teachers, simulation exercises with teachers’ 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

• Control of dynamical systems: examples of control problems in building application, types of control: model free (PID) and model-based control, open loop, and closed loop control)
• LTI system: eigen value and vector, stability of the system, controllability
• Pole placement
• A MATLAB simulation example from building application for checking the controllability and stabilize the system with pole placement

Lecture 2: Optimal control design

• LQR control
• Full-state estimation: observability and Kalman filter design
• LQG control
• A MATLAB simulation example from building application for LQR, Kalman filter and LQG control design
• Lecture 3: Model Predictive Control
• General concepts of MPC
• Modelling (White box – Black box- Grey box)
• A MATLAB simulation example for Grey box modelling of a case study from a building application

Day 2 (Simulation)

Lecture 1: An MPC design in MATLAB

• Introduction to CVX toolbox
• Cost functions formulization and constraints definition
• Presentation of a real-life case study (SmartVENT project) (Energy flexibility)
• Simulation exercise and homework (An example similar to SmartVENT project but simpler, in which the white box model is either modelled in IDA ICE or be one of the SIMULINK models in MATLAB )

Day 3 (Laboratory experiment)

Lecture 1: Introduction on the experimental setup

Experimental exercise

• Run the experimental system and be familiar with the it
• Input-output data collection
• Identify the system model and simulate the MPC controller
• Implement the MPC controller on the experimental setup
• Comparison of a PI control and the MPC control on the system performance

Criteria for assessment:

–        Attendance in all course days as scheduled is required.

–        Report on the simulation results.

–        Presentation of the experimental results provided by each group

Key literature:

–        Jan Drgona et al., All you need to know about model predictive control for building, Annual Reviews in Control, 2020

–        Predictive control with constraints. by Maciejowski

–        L. Wang, Model Predictive Control System Design and Implementation Using MATLAB. Springer, 2009

–        Henrik Madsen, Statistical Modelling of Physical Systems (An introduction to Grey Box modelling)

 

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