- Introduction to model predictive control (concept, variants, applications, research)
- Fundamentals of discrete-time systems (structures, discretization, stability, state feedback control, controllability, state observation, observability, separation principle, reference tracking, integral control, disturbance rejection, disturbance estimation, illustration for an active suspension system)
- Fundamentals of optimization (nonlinear optimization, convex optimization, optimality conditions, linear programming, quadratic programming)
- Model predictive control without constraints (formulation and solution of the optimization problem for a finite horizon (system model, cost function, analytical solution), application of the solution for a receding horizon, formulation and solution of the optimization problem for an infinite horizon (LQR), comparison)
- Model predictive control with constraints (Types and handling of constraints, formulation and solution of the optimization problem for a finite horizon (constraint models, numerical solution), application of the solution for a receding horizon, extensions like warm starting, multiple horizons, scaling, linear cost and soft constraints)
- Stability and feasibility (stability of model predictive control without constraints, feasibility and stability of model predictive control with constraints)
- Reference tracking and disturbance rejection (reference tracking based on target calculation and the delta input formulation, disturbance rejection based on disturbance estimation, preview control)
- Robust model predictive control (polytopic and norm-bounded uncertainties, linear matrix inequalities, parameter-dependent Lyapunov functions, robust stability and control)
- Illustration of the contents using simulations in MATLAB/Simulink
Model Predictive Control (M, 4.0 LP)
|Module Number||Module Name||CP (Effort)|
|EIT-JEM-515-M-7||Model Predictive Control||4.0 CP (120 h)|
|CP, Effort||4.0 CP = 120 h|
|Position of the semester||1 Sem. in WiSe|
|Level|| Master (Advanced)|
|Area of study||[EIT-JEM] Electro Mobility|
|Reference course of study||[EIT-88.C48-SG#2021] M.Sc. Automation & Control (A&C) |
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Model Predictive Control
|P||42 h||78 h||-||-||PL1||4.0||WiSe|
- About [EIT-JEM-515-K-7]: Title: "Model Predictive Control"; Presence-Time: 42 h; Self-Study: 78 h
Examination achievement PL1
- Form of examination: written exam (Klausur) (90 Min.)
- Examination Frequency: each semester
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
After completing this module you can...
- ... describe the variants and applications of model predictive control.
- ... explain the theoretical background of model predictive control (optimization, stability, feasibility, robustness, reference tracking, disturbance rejection).
- ... analyze the stability and feasibility of model predictive controllers.
- ... design, implement and evaluate model predictive controllers using MATLAB/Simulink.
Requirements for attendance of the module (informal)
Requirements for attendance of the module (formal)None
References to Module / Module Number [EIT-JEM-515-M-7]
|Course of Study||Section||Choice/Obligation|
|[EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering ||[Free Elective Area] Elective Subjects||[W] Elective Module|
|[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) ||[Free Elective Area] Elective Subjects||[W] Elective Module|
|[EIT-88.C48-SG#2021] M.Sc. Automation & Control (A&C) ||[Specialisation] Major "Connected Automation Systems" (CAS)||[P] Compulsory|
|[EIT-88.D55-SG#2021] M.Sc. Embedded Computing Systems (ESY) ||[Free Elective Area] Elective Subjects||[W] Elective Module|
|[EIT-EIT-MSC-TW-MPOOL-7]||Technical Elective Modules Master EIT|
|[EIT-SIAK-DT-ENG-MPOOL]||SIAK Certificate "Digital Transformation" - Modules EIT "Engineering"|