具有访问约束的网络化系统鲁棒模型预测控制  

Robust Model Predictive Control for Networked Systems with Medium Access Constraints

作　　者：祝超群[1,2,3] 张磐 ZHU Chaoqun;ZHANG Pan(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050;Key Laboratory of Gansu Adanced Control for Industrial Processes,Lanzhou 730050;National Demonstration Center for Erperimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou730050)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]甘肃省工业过程先进控制重点实验室,兰州730050 [3]兰州理工大学电气与控制工程国家级实验教学示范中心,兰州730050

出　　处：《系统科学与数学》2022年第9期2258-2278,共21页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金(61563031,61863026);甘肃省科技重大专项项目(21ZD4GA028);甘肃省高等学校产业支撑引导项目(2019C-05);甘肃省工业过程先进控制重点实验室开放基金项目(2019KFJJ03)资助课题。

摘　　要：针对一类存在介质访问和控制输入约束的离散时滞网络化控制系统,提出一种鲁棒模型预测控制器设计方法.考虑到控制输入约束的影响,利用不变椭圆集将控制输入约束转化为线性矩阵不等式,根据网络介质的随机访问机制将网络化系统建模为具有不确定性的马尔可夫跳变系统,在此基础上利用时变时滞的上界信息构造李雅普诺夫函数,给出网络化系统随机稳定的充分条件和鲁棒模型预测控制器的设计方法,并分析了时变时滞上界与鲁棒性能指标之间的关系.最后,通过仿真算例验证了所提出方法的正确性和有效性.The design procedure of robust model predictive controller is proposed for a class of discrete time-delay networked control systems with medium access constraints and control input constraints.Considering the influence of control input,the invariant ellipse set is utilized to transform the control input constraints into linear matrix inequalities,and the networked systems is modelled as a Markov jump system with the uncertainty according to the random-access mechanism of network medium.Under such a framework,the Lyapunov function is constructed by using the upper bound information of time-varying delay.The stochastically stable sufficient conditions are derived for the networked systems and the design method for the robust model predictive controller is developed,and the relationship between the upper bound of time-varying delay and the robust performance index is analyzed.Finally,the correctness and effectiveness of the proposed method are verified by a simulation case.

关 键 词：网络化控制系统 离散时滞系统 介质访问约束 鲁棒模型预测控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]