A Comparison of Two Methods of Adaptive Nonlinear Model Predictive Control

被引：1

作者：

Bamimore, A. ^{[1
]}

Akomolafe, D. A. ^{[1
]}

Asubiaro, P. J. ^{[1
]}

Osunleke, A. S. ^{[1
]}

机构：

[1] Obafemi Awolowo Univ, Dept Chem Engn, PSE Lab, Ife, Nigeria

来源：

IFAC PAPERSONLINE | 2024年 / 58卷 / 25期

关键词：

Adaptive model predictive control; linear parameter varying (LPV) model; nonlinear predictive control; Linear time varying (LTV) model;

D O I：

10.1016/j.ifacol.2024.10.243

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Conventional nonlinear model predictive control (NMPC) relies on an accurate process model. However, real-world systems' models are often imperfect due to parametric variations, modelling errors and additive noise, leading to degraded control performance. This study investigates the effectiveness of two adaptive NMPC methods in solving this problem, namely, linear parameter varying (LPV) model predictive control (LPV-MPC) and model predictive control based on successive linearization (SL-MPC). In addition, an innovative approach for identifying LPV models is proposed and applied to three simulation examples. The identified LPV models gave a very strong fit. Also, simulation results demonstrate that both adaptive predictive NMPC (LPV-MPC and SL-MPC) exhibit performance similar to conventional NMPC and superior to Linear MPC. Notably, the two adaptive predictive controllers offer significantly reduced computational time compared to conventional NMPC.

引用

页码：90 / 95

页数：6

共 12 条

[1] Adaptive model predictive control for constrained nonlinear systems [J].