Interior-point algorithms for nonlinear model predictive control

Adrian G. Wills; William P. Heath

doi:10.1007/978-3-540-72699-9_16

Interior-point algorithms for nonlinear model predictive control

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Abstract

In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function model predictive control (MPC), which includes standard MPC as a limiting case. However the optimisation problem that arises in nonlinear MPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternative to sequential quadratic programming. The algorithms are appropriate for the convex program that arises at each iteration of such an SCP. © 2007 Springer-Verlag Berlin Heidelberg.

Original language	English
Title of host publication	Lecture Notes in Control and Information Sciences\|Lect. Notes Control Inf. Sci.
Publisher	Springer Nature
Pages	207-216
Number of pages	9
Volume	358
DOIs	https://doi.org/10.1007/978-3-540-72699-9_16
Publication status	Published - 2007

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title = "Interior-point algorithms for nonlinear model predictive control",

abstract = "In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function model predictive control (MPC), which includes standard MPC as a limiting case. However the optimisation problem that arises in nonlinear MPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternative to sequential quadratic programming. The algorithms are appropriate for the convex program that arises at each iteration of such an SCP. {\textcopyright} 2007 Springer-Verlag Berlin Heidelberg.",

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AB - In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function model predictive control (MPC), which includes standard MPC as a limiting case. However the optimisation problem that arises in nonlinear MPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternative to sequential quadratic programming. The algorithms are appropriate for the convex program that arises at each iteration of such an SCP. © 2007 Springer-Verlag Berlin Heidelberg.

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BT - Lecture Notes in Control and Information Sciences|Lect. Notes Control Inf. Sci.

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Interior-point algorithms for nonlinear model predictive control

Abstract

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