By Topic

A Riccati Based Interior Point Algorithm for the Computation in Constrained Stochastic MPC

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
$33 $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

2 Author(s)
Minyong Shin ; Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA ; James A. Primbs

We propose a fast algorithm for the linear-quadratic control problem with probabilistic constraints that is repeatedly solved in stochastic model predictive control. Under the assumption of affine state feedback and Gaussian noise, the finite horizon control problem is converted to an equivalent deterministic problem using the mean and covariance matrix as the state. A line search interior point method is proposed to solve this optimization problem, where the step direction can be quickly computed via a Riccati difference equation. Numerical examples show that this algorithm has linear complexity in the horizon length.

Published in:

IEEE Transactions on Automatic Control  (Volume:57 ,  Issue: 3 )