Solving the Model Predictive Control Problem with Soft Constraints
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.
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
American Control Conference (1993: Jun. 2-4, San Francisco, CA)
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)
Article - Conference proceedings
© 1993 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jun 1993