Predictive Control

Course Description

Optimal controller design based on integral criteria. Application of convex and parametric optimization in control systems. Design of linear quadratic controller and linear quadratic tracking for linear discrete time (DLTI) systems. Basic idea of model predictive control (MPC) and receding horizon principle. Constrained linear optimal control with linear and quadratic performance index. Feasibility and stability of MPC. Parametric solution to the MPC problem - explicit MPC. Extensions of MPC and fast MPC.

Learning Outcomes

  1. design controller based on integral criteria
  2. design linear quadratic regulator and linear quadratic tracking for DLTI systems
  3. design model predictive controller for linear and quadratic performance index with linear constraints
  4. analyze and ensure stability of MPC
  5. design explicit solution to the MPC problem

Forms of Teaching

Lectures

3 hours of lectures per week

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Class participation 0 % 5 % 0 % 5 %
Mid Term Exam: Written 0 % 20 % 0 %
Final Exam: Written 0 % 25 %
Final Exam: Oral 50 %
Exam: Written 0 % 45 %
Exam: Oral 50 %

Week by Week Schedule

  1. Optimal controller design based on integral criteria
  2. Convex optimization in control systems
  3. Parametric optimization in control systems
  4. Finite and infinite horizon discrete-time LQR
  5. Linear quadratic tracking
  6. Basic idea of receding horizon control; History of MPC
  7. Constrained linear optimal control (linear and quadratic performance index)
  8. Midterm exam
  9. Feasibility and stability of MPC
  10. Feasibility and stability of MPC
  11. Parametric solution to the MPC problem
  12. Parametric solution to the MPC problem
  13. Extensions of MPC
  14. Explicit MPC; Fast MPC
  15. Final exam

Study Programmes

University graduate
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Literature

Francesco Borrelli, Alberto Bemporad, Manfred Morari (2017.), Predictive Control for Linear and Hybrid Systems, Cambridge University Press
James Blake Rawlings, David Q. Mayne (2009.), Model Predictive Control, Nob Hill Pub, Llc
Stephen Boyd, Stephen P. Boyd, Lieven Vandenberghe (2004.), Convex Optimization, Cambridge University Press
Jan Marian Maciejowski (2002.), Predictive Control, Pearson Education

For students

General

ID 222704
  Summer semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures
8 Laboratory exercises

Grading System

87.5 Excellent
75 Very Good
62.5 Good
50 Acceptable