控制理论与应用2026，Vol.43Issue(5)：979-988,10.DOI:10.7641/CTA.2025.50302
轮询通信下基于隶属函数优化的T-S模糊模型预测控制
Membership-function-optimized T-S fuzzy model predictive control under round-robin communication
摘要
Abstract
This paper investigates the membership-function-dependent model predictive control problem for Takagi-Sugeno(T-S)fuzzy systems under communication bandwidth constraints.Firstly,a round robin protocol is adopted to op-timize the data transmission mechanism between controllers and actuators,thereby reducing network load and improving data transmission reliability.Building upon this framework,a piecewise switched T-S fuzzy model is constructed by inte-grating token-dependent quadratic functions with premise variable space partitioning.Secondly,based on token-dependent piecewise Lyapunov function theory,a membership-function-token co-dependent terminal constraint set is designed and embedded into the online optimization problem to solve for feedback gain.Furthermore,continuous membership functions are approximated using staircase membership functions,while linear matrix inequality constraints incorporating shape in-formation of membership functions are introduced.Under the joint consideration of T-S fuzzy system nonlinearities and round robin protocol characteristics,sufficient conditions are derived to guarantee both asymptotic stability of the sys-tem and recursive feasibility of the algorithm.Finally,simulation experiments validate the effectiveness of the proposed membership-function-dependent T-S fuzzy predictive control strategy under round robin protocol.关键词
模型预测控制/Takagi-Sugeno(T-S)模糊系统/轮询通信协议/阶梯隶属函数/隶属函数-令牌联合依赖/分段李雅普诺夫函数Key words
model predictive control/Takagi-Sugeno(T-S)fuzzy systems/round robin protocol/staircase membership functions/membership function-token joint dependent/piecewise Lyapunov function引用本文复制引用
董钰莹,黄朕荣,高晨曦..轮询通信下基于隶属函数优化的T-S模糊模型预测控制[J].控制理论与应用,2026,43(5):979-988,10.基金项目
国家自然科学基金项目(62403204),福建省自然科学基金项目(2023J0545)资助.Supported by the National Natural Science Foundation of China(62403204)and the Natural Science Foundation of Fujian Province(2023J0545). （62403204）
