事件触发机制下的工业过程多速率模型预测控制方法

杨翊卓; 代伟

doi:10.12052/gdutxb.220056

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

事件触发机制下的工业过程多速率模型预测控制方法

更新时间：2026-05-27

- 事件触发机制下的工业过程多速率模型预测控制方法
- A Multi-rate Model Predictive Control with Event-Triggered Mechanism for Industrial Processes
- 广东工业大学学报 2022年39卷第5期页码：68-74
- 作者机构：
  
  中国矿业大学信息与控制工程学院,江苏,徐州,221116
- 作者简介：
  
  杨翊卓(1998–)，男，硕士研究生，主要研究方向为复杂工业系统的运行优化与控制
  代伟(1984–)，男，教授，博士，主要研究方向为人工智能驱动的复杂工业过程的建模、运行优化与运行反馈控制，E-mail：weidai@cumt.edu.cn
- 基金信息：
  
  国家自然科学基金资助面上项目(61973306);江苏省自然科学优秀青年基金资助项目(BK20200086);江苏省研究生科研与实践创新计划(KYCX22_2559);中国矿业大学研究生创新计划项目(2022WLJCRCZL100)
- DOI：10.12052/gdutxb.220056
  中图分类号： TP273
- 纸质出版：2022
- 稿件说明：
移动端阅览
杨翊卓, 代伟. 事件触发机制下的工业过程多速率模型预测控制方法[J]. 广东工业大学学报, 2022, 39(5): 68-74.

Yang Yi-zhuo, Dai Wei. A Multi-rate Model Predictive Control with Event-Triggered Mechanism for Industrial Processes[J]. 2022, 39(5): 68-74.
杨翊卓, 代伟. 事件触发机制下的工业过程多速率模型预测控制方法[J]. 广东工业大学学报, 2022, 39(5): 68-74. DOI： 10.12052/gdutxb.220056.

Yang Yi-zhuo, Dai Wei. A Multi-rate Model Predictive Control with Event-Triggered Mechanism for Industrial Processes[J]. 2022, 39(5): 68-74. DOI： 10.12052/gdutxb.220056.

摘要

随着工业互联网的发展，为避免网络拥塞，控制系统正在由时间触发向事件触发控制方向发展。本文针对一类多速率工业过程，将模型预测控制与提升技术和事件触发技术相结合，提出了事件触发机制下的多速率模型预测控制方法。该方法首先采用提升技术解决多速率问题，进而采用模型预测控制(Model Predictive Control

MPC)方法设计控制器以跟踪设定值；在此基础上，设计保证系统稳定的事件触发机制，使控制器仅在违反触发机制时更新。通过对典型工业磨矿过程仿真实验来验证本文所提方法的有效性，结果表明所提方法能够有效减少信道资源的占用和计算负载

为工业互联网环境下工业过程控制器设计提供了新的方法。

Abstract

With the development of the industrial internet

the control system is developing from time-triggered to event-triggered for avoiding network congestion. For a class of multi-rate industrial processes

a multi-rate model predictive control method with event trigger mechanism is proposed by combining model predictive control (MPC) with lifting and event-triggered technologies. The proposed method firstly adopts the lifting technology to solve the multi-rate problem

and then employs MPC algorithm to design controller to achieve the setpoint tracking. Furthermore

an event-triggered mechanism is designed to make the controller update only under the condition of trigger mechanism violation

while ensuring the system stability. Experiments have been carried out on an industrial grinding process

showing the effectiveness of the proposed method. The proposed method can save the communication resource and computational load

thereby providing a new method for the design of process industrial controllers in industrial internet framework.

关键词

Keywords

references

ZHOU J. Intelligent Manufacturing—main direction of “Made in China 2025” [J]. China Mechanical Engineering, 2015, 26(17): 2273-2284.

ZHOU Q, CHEN G D, LU R Q, et al . Disturbance observer based event-triggered control for multi-agent systems with input saturation [J ] . Sci Sin Inform, 2019, 49(011): 1502-1516.

LI Y X, YANG G H. Model-based adaptive event-triggered control of strict-feedback nonlinear systems [J]. IEEE transactions on neural networks and learning systems, 2017, 29(4): 1033-1045.

HAN X, ZHAO X, SUN T, et al . Event-triggered optimal control for discrete-time switched nonlinear systems with constrained control input [J ] . IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2020(99): 1-10.

CHENG J, LIANG L D, PARK J H. A dynamic event-triggered approach to state estimation for switched memristive neural networks with nonhomogeneous sojourn probabilities [J]. IEEE Transactions on Circuits and Systems I:Regular Papers., 2021, 68(12): 4924-4934.

XU B, LIU X, WANG H. Event-triggered control for nonlinear systems via feedback linearization [J]. International Journal of Control, 2020(2): 1-19.

HAN Y C, LIAN J. Periodic event-triggered and self-triggered control of singular system under stochastic cyber-attacks [J]. IET Control Theory Applications, 2020, 14(19): 156-165.

HASHIMOTO K, ADACHI S, DIMAROGONAS D V. Event-triggered intermittent sampling for nonlinear model predictive control [J]. Automatica, 2017, 81: 148-155.

HEEMELS W P M H, DONKERS M C F. Model-based periodic event-triggered control for linear systems [J]. Automatica, 2013, 49(3): 698-711.

DONKERS M C F, HEEMELS W P M H. Output-based event-triggered control with guaranteed L ∞ -gain and improved and decentralized event-triggering [J ] . IEEE Transactions on Automatic Control, 2012, 57(6): 1362-1376.

LU Q, SHI P, LIU J, et al . Model predictive control under event-triggered communication scheme for nonlinear networked systems [J ] . Journal of the Franklin Institute, 2019, 356(5): 2625-2644.

WANG X F, MICHAEL D L. Event-triggering in distributed networked control systems [J]. IEEE Transactions on Automatic Control, 2011, 56(3): 586-601.

POSTOYAN R, TABUADA P, NESIC D, et al . A framework for the event-triggered stabilization of nonlinear systems [J ] . IEEE Transactions on Automatic Control, 2015, 60(4): 982-996.

WEI M, LI Y X, TONG S. Event-triggered adaptive neural control of fractional-order nonlinear systems with full-state constraints [J]. Neurocomputing, 2020, 412: 320-326.

WANG X F, MICHAEL D L. On event design in event-triggered feedback systems [J]. Automatica, 2011, 47(10): 2319-2322.

ZHANG X M, HAN Q L, ZHANG B L. An overview and deep investigation on sampled-data-based event-triggered control and filtering for networked systems [J]. IEEE Transactions on Industrial Informatics, 2017, 13(1): 4-16.

LU Q, SHI P, WU L G, et al . Event-triggered estimation and model predictive control for linear systems with actuator fault [J ] . IET Control Theory Applications, 2020, 14(16): 2406-2412.

ZHAO F Y, JIANG Z P, LIU T F, et al . Event-triggered adaptive optimal control with output feedback: an adaptive dynamic programming approach [J ] . IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(11): 5208-5221.

ZHAO F Y, GAO W N, LIU T F. Learning-based event-triggered adaptive optimal output regulation of linear discrete-time systems[C]//Data Driven Control and Learning Systems Conference (DDCLS). Suzhou: IEEE, 2021: 1516-1521.

SAHOO A, HAO X, JAGANNATHAN S. Near optimal event-triggered control of nonlinear discrete-time systems using neuro dynamic programming [J]. IEEE Transactions on Neural Networks Learning Systems, 2016, 27(9): 1801-1815.

DAI W, LU W J, FU J, et al . Multi-rate layered optimal operational control of industrial processes [J ] . Acta Automatica Sinica, 2019, 45(10): 1946-1959.

YANG B, ZHOU Q, CAO L, et al . Event-triggered control for multi-agent systems with prescribed performance and full state constraints [J ] . Acta Automatica Sinica, 2019, 45(8): 1527-1535.

CHEN X S, YANG J, LI S H. Disturbance observer based multi-variable control of ball mill grinding circuits [J]. Journal of Process Control, 2009, 19(7): 1205-1213.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于模型预测的迭代学习控制器设计

基于高效时序建模的RGB-T跟踪

面向灾后救援的多无人机通感算一体化资源分配优化方法

双模态迭代交叉注意力融合集成框架

基于PSI技术的福州市地面形变监测与影响因素分析