自动化学报2026，Vol.52Issue(3)：481-509,29.DOI:10.16383/j.aas.c250195
基于混合驱动与梯度优化的模糊宽度模型预测控制
Fuzzy Broad Model Predictive Control Based on Hybrid-driven and Gradient Optimization
摘要
Abstract
Model predictive control(MPC)is an advanced process control strategy widely applied across various in-dustrial processes.Although deep neural networks have been used to enhance traditional MPC performance,they often suffer from high computational complexity and the risk of overfitting.While the application of conventional particle swarm optimization(PSO)in MPC offers global search capabilities,it struggles to meet real-time control requirements due to excessive computational overhead and strong dependency on initial solutions.To address these challenges,this paper proposes a novel fuzzy broad model predictive control approach based on hybrid-driven and gradient optimization.Firstly,an interval type-2 fuzzy broad learning system is employed to construct the predict-ive model,thereby enhancing nonlinear modeling and uncertainty handling capabilities.Secondly,during the rolling optimization process,a hybrid strategy combining gradient descent and PSO is introduced to ensure fast conver-gence while improving global search performance.In addition,a knowledge-data-driven surrogate model is built by leveraging the system sample database and particle archive database to significantly reduce computational con-sumption.Finally,a baseline solving strategy for manipulated variables is designed to improve the safety and reliab-ility of control outputs.The effectiveness of the proposed method is verified through simulation experiments on typ-ical nonlinear systems and actual municipal solid waste incineration process.关键词
模型预测控制/区间二型模糊宽度学习系统/梯度粒子群优化/知识−数据驱动/代理模型/城市固废焚烧Key words
model predictive control/interval type-2 fuzzy broad learning system/gradient particle swarm optimiza-tion/knowledge-data-driven/surrogate model/municipal solid waste incineration引用本文复制引用
田昊,汤健,余文,乔俊飞..基于混合驱动与梯度优化的模糊宽度模型预测控制[J].自动化学报,2026,52(3):481-509,29.基金项目
国家自然科学基金(62573011,62373017)资助Supported by National Natural Science Foundation of China(62573011,62373017) （62573011,62373017）
