Module Handbook

  • Dynamischer Default-Fachbereich geändert auf EIT

Course EIT-JEM-515-K-7

Model Predictive Control (2V+1U, 4.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 4.0 CP
2 V Lecture 28 h 48 h
1 U Lecture hall exercise class 14 h 30 h
(2V+1U) 4.0 CP 42 h 78 h

Basedata

SWS 2V+1U
CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Lecturers
Area of study [EIT-JEM] Electro Mobility
Livecycle-State [NORM] Active

Contents

  • 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

Literature

  • J. M. Maciejowski: Predictive Control with Constraints, Pearson Education, 2002 (EIT 915/140)
  • B. Kouvaritakis, M. Cannon: Model Predictive Control: Classical, Robust and Stochastic, Springer, 2016 (EIT 948/024)
  • E. F. Camacho, C. Bordons: Model Predictive Control, 2nd ed., Springer, 2007 (EIT 938/059)
  • J. B. Rawlings, D. Q. Mayne: Model Predictive Control, Nob Hill Publishing, 2009 (EIT 938/062)
  • G. F. Franklin, J. D. Powell, M. L. Workman: Digital Control of Dynamic Systems, 3rd ed., Addison-Wesley, 1997 (L EIT 119)
  • S. Boyd, L. Vandenberghe: Convex Optimization, Cambridge University Press, 2004 (EIT 177/029)

Materials

Slides (PDF as download), board (hand-written notes), MATLAB/Simulink (examples as download)

Requirements for attendance (informal)

Modules:

Requirements for attendance (formal)

None

References to Course [EIT-JEM-515-K-7]

Module Name Context
[EIT-JEM-515-M-7] Model Predictive Control P: Obligatory 2V+1U, 4.0 LP