Course Contents
[i]Lecture:[/i]
Introduction and basics of optimal control, Linear Quadratic Regulator (LQR) in discrete and continuous time, basics of model predictive control (cost functions, constraints, receding horizon), nominal model predictive control for linear systems, robust and stochastic model predictive control, model predictive control of nonlinear systems, combination of machine learning and model predictive control.
[i]Group work:[/i]
In a group project, the students will apply the learned. The group project evolves a review of state of the art for the selected task, the selection of suitable model predictive control approach, and the implementation using python/Matlab. It includes a project report and is concluded by a project presentation.
Literature
[list]
[*]J. Rawlings, D. Mayne, and M. Diehl. Model predictive control: theory, computation, and design. Nob Hill Publishing.
[*]S. Rakovic, and W. Levine. Handbook of Model Predictive Control. Birkhäuser, 2018.
[/list]
Preconditions
Basic concepts of control theory. Fundamentals of linear algebra, differential, and difference equations. Knowledge in Python and/or Matlab.
[i]Lecture:[/i]
Introduction and basics of optimal control, Linear Quadratic Regulator (LQR) in discrete and continuous time, basics of model predictive control (cost functions, constraints, receding horizon), nominal model predictive control for linear systems, robust and stochastic model predictive control, model predictive control of nonlinear systems, combination of machine learning and model predictive control.
[i]Group work:[/i]
In a group project, the students will apply the learned. The group project evolves a review of state of the art for the selected task, the selection of suitable model predictive control approach, and the implementation using python/Matlab. It includes a project report and is concluded by a project presentation.
Literature
[list]
[*]J. Rawlings, D. Mayne, and M. Diehl. Model predictive control: theory, computation, and design. Nob Hill Publishing.
[*]S. Rakovic, and W. Levine. Handbook of Model Predictive Control. Birkhäuser, 2018.
[/list]
Preconditions
Basic concepts of control theory. Fundamentals of linear algebra, differential, and difference equations. Knowledge in Python and/or Matlab.
- Lehrende: Rolf Findeisen
- Lehrende: Hoang Hai Nguyen
- Lehrende: Maik Pfefferkorn
Semester: Inverno 2022/23