Cart (Loading....) | Create Account
Close category search window
 

Improving model predictive control arithmetic robustness by Monte Carlo simulations

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 $31
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

4 Author(s)
Vouzis, P.D. ; Dept. of Chem. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Collange, S. ; Arnold, M.G. ; Kothare, M.V.

Model predictive control (MPC) is an optimisation-based algorithm which usually requires a numerical method to calculate the solution of the problem. Inherently, numerical methods for optimisation problems are implemented on a finite-precision hardware platform and are subject to the appearance of numerical instabilities of catastrophic cancellation and ill-conditioned matrices. These anomalies are difficult to detect and overcome, and for safety-critical applications, it is essential to have a mechanism that can at least issue a warning when an arithmetic instability occurs. Towards this direction, Monte Carlo arithmetic (MCA) for the floating-point (FP) number system has been used for both detection and mitigation of catastrophic cancellation and ill-conditioned matrices. An alternative to FP is the Logarithmic Number System (LNS) that recently has been proposed for the real-time hardware implementation of embedded MPC. In this study the authors present the adaptation of MCA to LNS for detecting and mitigating catastrophic cancellation, forming the Monte Carlo Logarithmic Number System (MCLNS). An inherent drawback of MCA is the accuracy deterioration which is a direct consequence of the randomisation in the arithmetic operations. Additionally, multiple simulations of the system result in performance deterioration equal to the number of simulations. Using off-line simulations it is possible to determine the necessary hardware requirements to achieve desired accuracy under performance constraints. These trade-offs are studied and analysed for an MPC algorithm, and the hardware implementation cost of MCLNS is quantified by synthesis on a Xilinx Virtex-IV FPGA.

Published in:

Control Theory & Applications, IET  (Volume:6 ,  Issue: 8 )

Date of Publication:

May 17 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.