Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Model Predictive Control


M. Morari

Electrical and Computer Engineering, University of Connecticut, 8 May 2014

Co-contributors: Francesco Borrelli(UC Berkeley), Colin Jones (EPFL)

Increased system complexity and more demanding performance requirements have rendered traditional control laws inadequate regardless if simple PID loops are considered or robust feedback controllers designed according to some H2/infinity criterion. Applications ranging from the process industries to the automotive and the communications sector are making increased use of Model Predictive Control (MPC) where a fixed control law is replaced by on-line optimization performed over a receding horizon. The advantage is that MPC can deal with almost any time-varying process and specifications, limited only by the availability of real-time computer power. In the last few years we have seen tremendous progress in this interdisciplinary area where fundamentals of systems theory, computation and optimization interact. For example, methods have emerged to handle hybrid systems, i.e. systems comprising both continuous and discrete components. Also, it is now possible to perform most of the computations off-line thus reducing the control law to a simple look-up table. In this short course we will derive MPC algorithms and establish their theoretical properties. The main emphasis will be on the theory and the computational tools. I will also talk about the applications where MPC was used with great benefit.


Type of Publication:


No Files for download available.
% No recipe for automatically generating a BibTex entry for (10)Course
Permanent link