Inexact Newton-Kantorovich Methods for Constrained Nonlinear Model Predictive Control

被引：7

作者：

Dontchev, Asen L. ^{[1
,2
]}

Huang, Mike ^{[3
]}

Kolmanovsky, Ilya V. ^{[4
]}

Nicotra, Marco M. ^{[4
]}

机构：

[1] Amer Math Soc, Providence, RI 02904 USA

[2] Univ Michigan, Ann Arbor, MI 48109 USA

[3] Toyota Motor North Amer, Res & Dev, Ann Arbor, MI 48105 USA

[4] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2019年 / 64卷 / 09期

基金：

澳大利亚研究理事会; 美国国家科学基金会; 奥地利科学基金会;

关键词：

Constrained systems; control engineering computing; inexact Newton-Kantorovich method; linear convergence; Newton method; nonlinear dynamical systems; nonlinear model predictive control; optimal control; quadratic programming; strong regularity; TIME ITERATION SCHEME; OPTIMIZATION; STABILITY;

D O I：

10.1109/TAC.2018.2884402

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we consider Newton-Kantorovich type methods for solving control-constrained optimal control problems that appear in model predictive control. Conditions for convergence are established for an inexact version of the Newton-Kantorovich method applied to variational inequalities. Based on these results, two groups of algorithms are proposed to solve the optimality system. The first group includes exact and inexact Newton and Newton-Kantorovich implementations of the sequential quadratic programming. In the second group, exact and inexact Newton and Newton-Kantorovich methods are developed for solving a nonsmooth normal map equation equivalent to the optimality system. Numerical simulations featuring examples from the aerospace and automotive domain are presented, which show that inexact Newton-Kantorovich type methods can achieve significant reduction of the computational time.

引用

页码：3602 / 3615

页数：14

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