MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
Model predictive control (TSM_PredContr)

Model Predictive Control (MPC) is an optimisation-based approach to control systems and processes. The general mathematical formulation of MPC allows it to be applied to a broad range of systems and considers system constraints intrinsically. The advances in optimisation methods and available computational power have made MPC a valuable alternative to classical control approaches also for fast dynamic systems. Today, MPC applications can be found from the original chemical process control systems to the control of frequency converters with sampling periods down to a few microseconds.
This module focuses on introducing MPC from the theoretical basics to the use of tool kits to support the implementation and generation of working code. As the classical frequency domain control methods are not considered here, this module does not need in-depth knowledge of control systems. A general affinity to mathematics and programming skills are beneficial.

Prerequisites

  • Linear Algebra
  • Differential equations
  • Basic feedback control and dynamic systems
  • Basic programming skills in Matlab or Python or equivalent
  • General affinity to mathematics(!)

Learning Objectives

The student is able to 

  • formulate an optimisation problem and solve it with appropriate tool kits
  • formulate model predictive control problems
  • apply MPC concepts to real world systems and generate executable code which runs on their control systems

Contents of Module

Basic concepts ( 3W)

  • Introduction to state space models in continuous and discrete time
  • Introduction to optimisation (linear quadratic programs) using tool kits like YALMIP
  • Introduction to optimisation with constraints

Basic MPC (3W)

  • MPC problem formulation
  • Receding horizon concepts
  • Limits of MPC

Real-time implementation(3W)

  • From problem to code using tool kits like ACADO

MPC Extensions and examples (5W)

  • Reference tracking
  • Buck converter control (explicit MPC)
  • Nonlinear optimisation and MPC with nonlinear models
  • Energy management (scheduling)

Teaching and Learning Methods

Lectures with homework assignments which are a mix of theoretical exercises and programming assignments.

 

Download full module description

Back