Solving the Model Predictive Control Problem with Soft Constraints

Author

James D. Feher
Kelvin T. Erickson, Missouri University of Science and TechnologyFollow

Abstract

This paper will demonstrate how the convexity and quadratic nature of the soft constrained model predictive control problem can be used to solve for its unique minimum in a finite number of steps. A mathematical formulation for this problem will be given that leads to a new convergent minimization algorithm. This algorithm will then be compared to a traditional method of steepest descent type algorithm in an example.

Recommended Citation

J. D. Feher and K. T. Erickson, "Solving the Model Predictive Control Problem with Soft Constraints," Proceedings of the American Control Conference (1993, San Francisco, CA), pp. 377 - 378, Institute of Electrical and Electronics Engineers (IEEE), Jun 1993.

The definitive version is available at https://doi.org/10.23919/ACC.1993.4792878

Meeting Name

American Control Conference (1993: Jun. 2-4, San Francisco, CA)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Algorithms; Constraint Theory; Optimization; Minimization Algorithms; Model Predictive Control; Soft Constraints; Predictive Control Systems

International Standard Book Number (ISBN)

0-7803-0860-3

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1993 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 1993