Stochastic Model Predictive Control of Markov Jump Linear Systems Based on a Two-layer Recurrent Neural Network

被引：0
|
作者：
Yan, Zheng [1 ]
Wang, Jun [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2013年
关键词：
OPTIMIZATION SUBJECT; NONLINEAR-SYSTEMS; STABILITY; MPC;
D O I：
暂无
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 presents a stochastic model predictive control approach to constrained Markov jump linear systems based on neurodynamic optimization. The stochastic model predictive control problem is formulated as a nonlinear convex optimization problem, which is iteratively solved by using a two-layer recurrent neural network in real-time. The applied neural network can globally converge to the exact optimal solution of the optimization problem. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach.
引用
收藏
页码：564 / 569
页数：6
相关论文
共 50 条
  • [21] Two-layer model predictive control of systems with independent dynamics and shared control resources
    La Bella, Alessio
    Bonassi, Fabio
    Farina, Marcello
    Scattolini, Riccardo
    IFAC PAPERSONLINE, 2019, 52 (03): : 96 - 101
  • [22] Model predictive control with quadratic criterion for jump Markov discrete linear systems under constraits
    Dombrovskii, Vladimir V.
    Samorodova, Mariya V.
    VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE, 2016, 34 (01): : 4 - 10
  • [23] A linear approach for sparse coding by a two-layer neural network
    Montalto, Alessandro
    Tessitore, Giovanni
    Prevete, Roberto
    NEUROCOMPUTING, 2015, 149 : 1315 - 1323
  • [24] Dissipative control for Markov jump non-linear stochastic systems based on T-S fuzzy model
    Sheng, Li
    Gao, Ming
    Zhang, Weihai
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2014, 45 (05) : 1213 - 1224
  • [25] Quasi-Linear Recurrent Neural Network based Identification and Predictive Control
    Li, Dazi
    Kang, Tianjiao
    Hue, Jinglu
    Han, Min
    Jin, Qibing
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [26] Stochastic model predictive control for probabilistically constrained Markovian jump linear systems with additive disturbance
    Lu, Jianbo
    Xi, Yugeng
    Li, Dewei
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2019, 29 (15) : 5002 - 5016
  • [27] A Two-Layer Recurrent Neural Network for Nonsmooth Convex Optimization Problems
    Qin, Sitian
    Xue, Xiaoping
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (06) : 1149 - 1160
  • [28] Interpolation based Model Predictive Control Design for Markovian Jump Linear Systems
    Lu, Jianbo
    Xi, Yugeng
    Li, Dewei
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 4413 - 4418
  • [29] Robust, optimal predictive control of jump markov linear systems using particles
    Blackmore, Lars
    Bektassov, Askar
    Ono, Masahiro
    Williams, Brian C.
    HYBRID SYSTEMS: COMPUTATION AND CONTROL, PROCEEDINGS, 2007, 4416 : 104 - +
  • [30] Fast Robust Model Predictive Control for Nonlinear Jump Markov Systems
    Tonne, Jens
    Stursberg, Olaf
    IFAC PAPERSONLINE, 2017, 50 (01): : 9342 - 9348
12345