Model predictive control of non-linear systems over networks with data quantization and packet loss

被引:21
|
作者
Yu, Jimin [1 ,2 ]
Nan, Liangsheng [1 ,2 ]
Tang, Xiaoming [1 ,2 ]
Wang, Ping [1 ,2 ]
机构
[1] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China
[2] Minist Educ, Key Lab Ind Internet Things & Networked Control, Chongqing 400065, Peoples R China
基金
中国国家自然科学基金;
关键词
Non-linear NCSs; Data quantization; Packet loss; T-S model; Model predictive control; EVENT-TRIGGERED CONTROL; LINEAR-SYSTEMS; FEEDBACK-CONTROL; TIME-DELAY; STABILIZATION; DROPOUT;
D O I
10.1016/j.isatra.2015.06.014
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:1 / 9
页数:9
相关论文
共 50 条
  • [21] QUANTIZATION OF A CLASS OF NON-LINEAR SYSTEMS
    HOOD, CG
    HELLMAN, WS
    BULLETIN OF THE AMERICAN PHYSICAL SOCIETY, 1971, 16 (04): : 504 - &
  • [22] Non-linear prediction horizon time-discretization for model predictive control of linear sampled-data systems
    Gondhalekar, Ravi
    Imura, Jun-ichi
    PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS, VOLS 1-4, 2006, : 326 - +
  • [23] Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
    Liu, Chaoyue
    Zhu, Libin
    Belkin, Mikhail
    APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2022, 59 : 85 - 116
  • [24] Model predictive control of constrained non-linear time-delay systems
    Reble, Marcus
    Esfanjani, Reza Mahboobi
    Nikravesh, Seyyed Kamaleddin Y.
    Allgoewer, Frank
    IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION, 2011, 28 (02) : 183 - 201
  • [25] Robust H∞ model predictive control for constrained Lipschitz non-linear systems
    Shokrollahi, Ali
    Shamaghdari, Saeed
    JOURNAL OF PROCESS CONTROL, 2021, 104 : 101 - 111
  • [26] An overview of continuation methods for non-linear model predictive control of water systems
    Baayen, Jorn
    Becker, Bernhard
    van Heeringen, Klaas-Jan
    Miltenburg, Ivo
    Piovesan, Teresa
    Rauw, Julia
    den Toom, Matthijs
    VanderWees, Jesse
    IFAC PAPERSONLINE, 2019, 52 (23): : 73 - 80
  • [27] Constructive robust model predictive control for constrained non-linear systems with disturbances
    He, De-feng
    Ji, Hai-bo
    Yu, Li
    IET CONTROL THEORY AND APPLICATIONS, 2013, 7 (15): : 1869 - 1876
  • [28] Robust model predictive control with sliding mode for constrained non-linear systems
    Fesharaki, Shekoofeh Jafari
    Kamali, Marzieh
    Sheikholeslam, Farid
    Talebi, Heidar Ali
    IET CONTROL THEORY AND APPLICATIONS, 2020, 14 (17): : 2592 - 2599
  • [29] Model Predictive Control of Linear Systems over Networks with State and Input Quantizations
    Tang, Xiao-Ming
    Qu, Hong-Chun
    Xie, Hao-Fei
    Wang, Ping
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2013, 2013