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College of Engineering & Technology  Alexandria
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Model Predictive Control

  • Electrical & Control Engineering |

Description

The course explains the concept of Model Predictive Control analysis and control. The course covers the fundamentals of MPC configuration and solution of unconstrained and constrained MPC; Generalized Predictive Control (GPC); stability and feasibility analysis of MPC; tuning of MPC; Robust MPC and Nonlinear MPC (NMPC); Industrial MPC and applications.

Program

PhD in Electrical & Control Engineering

Objectives

  • The student should be:  Understand the basic principles of MPC;  Know the solution techniques of MPC;  Understand the control design and analysis and tuning of MPC.  Know the concept of robust MPC and NMPC.  Implement MPC in industrial system.

Textbook

J. m. Maciejowski, Predictive Control with Constraints. Prentice Hall, 2005.  E. F. Camacho and C. Bordons, Model Predictive Control, 2nd, Springer, 2005  J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Design, Nob Hill Pub, 2015  Lalo Magni, Davide Martino Raimondo, Frank Allgöwer, Nonlinear Model Predictive Control: Towards New Challenging Applications. Springer, 2009

Course Content

content serial Description

Markets and Career

  • Generation, transmission, distribution and utilization of electrical power for public and private sectors to secure both continuous and emergency demands.
  • Electrical power feeding for civil and military marine and aviation utilities.
  • Electrical works in construction engineering.
Courses
  • Degree Doctorate
  • Code: EEE8103
  • Credit hrs: 3
  • Prequisites: none
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