Module Handbook

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Module EIT-JEM-515-M-7

Model Predictive Control (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
EIT-JEM-515-M-7 Model Predictive Control 4.0 CP (120 h)

Basedata

CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
Görges, Daniel, apl. Prof. Dr.-Ing. (EXT | DEPT: EIT)
Lecturers
Görges, Daniel, apl. Prof. Dr.-Ing. (EXT | DEPT: EIT)
Area of study [EIT-JEM] Electro Mobility
Reference course of study [EIT-88.?-SG#2021] M.Sc. Automation and Control (A&C) [2021]
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part 		Presence-Time /
Self-Study 		SL SL is
required for exa. 		PL CP Sem.
2V+1U EIT-JEM-515-K-7
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.


Contents

From [EIT-JEM-515-K-7] Model Predictive Control:
  • 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

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 (informal)

Modules:

Requirements for attendance (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 [2010] Elective Subjects [W] Elective Module
[EIT-88.?-SG#2021] M.Sc. Electrical and Computer Engineering [2021] Technical Elective Modules [W] Elective Module
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] Elective Subjects [W] Elective Module
[EIT-88.?-SG#2021] M.Sc. Automation and Control (A&C) [2021] Major "Connected Automation Systems" (CAS) [P] Compulsory
[EIT-88.?-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] Elective Subjects [W] Elective Module