By Topic

Model Predictive Control Using Segregated Disturbance Feedback

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chen Wang ; Dept. of Mech. Eng., Nat. Univ. of Singapore & Singapore-MIT Alliance, Singapore, Singapore ; Chong-Jin Ong ; Sim, M.

This paper proposes a new control parametrization under the model predictive control (MPC) framework for constrained linear discrete-time systems with bounded disturbances. The proposed parametrization takes the form of a special piecewise affine disturbance feedback in an effort to reduce conservatism. It is a generalization of linear disturbance feedback parametrization, introduced in the recent literature. Numerical computations and stability properties of the resulting MPC problem using the proposed parametrization are discussed. When the disturbance set and the problem data satisfy mild assumptions, the associated finite-horizon optimization can be computed efficiently and exactly. The advantage of the proposed parametrization over linear disturbance feedback is illustrated via numerical examples.

Published in:

Automatic Control, IEEE Transactions on  (Volume:55 ,  Issue: 4 )