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Interior-point algorithms for nonlinear model predictive control

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Interior-point algorithms for nonlinear model predictive control. / Wills, A.G.; Heath, W.P.
Lecture Notes in Control and Information Sciences. Vol. 358 1. ed. Springer, 2007. p. 207-216 (Lecture Notes in Control and Information Sciences).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

HarvardHarvard

Wills, AG & Heath, WP 2007, Interior-point algorithms for nonlinear model predictive control. in Lecture Notes in Control and Information Sciences. 1 edn, vol. 358, Lecture Notes in Control and Information Sciences, Springer, pp. 207-216.

APA

Wills, A. G., & Heath, W. P. (2007). Interior-point algorithms for nonlinear model predictive control. In Lecture Notes in Control and Information Sciences (1 ed., Vol. 358, pp. 207-216). (Lecture Notes in Control and Information Sciences). Springer.

CBE

Wills AG, Heath WP. 2007. Interior-point algorithms for nonlinear model predictive control. In Lecture Notes in Control and Information Sciences. 1 ed. Springer. pp. 207-216. (Lecture Notes in Control and Information Sciences).

MLA

Wills, A.G. and W.P. Heath "Interior-point algorithms for nonlinear model predictive control". Lecture Notes in Control and Information Sciences. 1 udg., Lecture Notes in Control and Information Sciences. Springer. 2007, 207-216.

VancouverVancouver

Wills AG, Heath WP. Interior-point algorithms for nonlinear model predictive control. In Lecture Notes in Control and Information Sciences. 1 ed. Vol. 358. Springer. 2007. p. 207-216. (Lecture Notes in Control and Information Sciences).

Author

Wills, A.G. ; Heath, W.P. / Interior-point algorithms for nonlinear model predictive control. Lecture Notes in Control and Information Sciences. Vol. 358 1. ed. Springer, 2007. pp. 207-216 (Lecture Notes in Control and Information Sciences).

RIS

TY - CHAP

T1 - Interior-point algorithms for nonlinear model predictive control

AU - Wills, A.G.

AU - Heath, W.P.

PY - 2007

Y1 - 2007

N2 - 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.

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.

M3 - Pennod

VL - 358

T3 - Lecture Notes in Control and Information Sciences

SP - 207

EP - 216

BT - Lecture Notes in Control and Information Sciences

PB - Springer

ER -