Nonlinear Model Predictive Control of Mechatronic Systems

Description
The lecture details the Nonlinear Model Predictive Control (NMPC) concept that is an advanced control method offering significant advantages. Specifically, NMPC schemes are covered which are suited for the requirements of mechatronic systems. Many systems are characterized by complex, nonlinear system dynamics while the sampling times of the control algorithms are in the millisecond range.

Objective
Learn how to design and implement Nonlinear Model Predictive Control algorithms for challenging real-time systems. The lecture discusses the algorithmic details of NMPC with a special focus on mechatronic systems. During the exercise sessions an NMPC controller for a combustion engine is developed. The entire process from simulation-based control development to the application at a real-world combustion engine is covered.

Content
1. Introduction
2. Model-based control
3. Fundamentals of optimization
4. Linear MPC
5. Formulation of the optimization problem
6. Nonlinear MPC: numerical solution algorithms for real-time applications
7. Nonlinear MPC: discretization methods
8. Application example: engine control

Skript
Lecture slides will be provided after each lecture.
T. Albin: "Nonlinear Model Predictive Control of Combustion Engines"

Literature
- T. Albin: "Nonlinear Model Predictive Control of Combustion Engines"
- J. Maciejowski: "Predictive Control with Constraints"
- L. Guzzella, C. Onder: "Introduction to Modeling and Control of Internal Combustion Engine Systems"

Requirements
Fundamental control lecture (e.g. Control System 1), Linear Algebra, Matlab

Exam
Oral exam (30 minutes), covers all material