湖南大学学报（自然科学版）2023，Vol.50Issue(12)：168-177,10.DOI:10.16339/j.cnki.hdxbzkb.2023190
基于FCM-ELM-BBPS的预测控制参数整定
Predictive Control Parameter Tuning Algorithm Based on FCM-ELM-BBPS
摘要
Abstract
The design parameter selection of model predictive control significantly affects the performance of the controlled system.The current mainstream parameter tuning methods based on expert experience have the disadvan-tages of poor controller robustness and high calculation cost.To solve the above problems,this paper proposes a pa-rameter tuning algorithm based on Fuzzy C-means-Extreme Learning Machine-Bare Bones Particle Swarm(FCM-ELM-BBPS).Firstly,Fuzzy C-means(FCM)clustering is used to preprocess the data,and the complex data of the controlled system is clustered according to its own characteristics,so as to reduce the training error of the neural net-work and improve the prediction accuracy.Secondly,for each kind of characteristic data,the Extreme Learning Ma-chine(ELM)was used to establish the mapping relationship model between predictive control parameters and perfor-mance indices,and the parameter tuning rules were further obtained.Then the Bare Bones Particle Swarm(BBPS)optimization algorithm is used to tune the predictive control parameters.The Gaussian distribution is adopted to up-date the particle position,which accelerats the convergence of the objective function and effectively reducs the pa-rameter optimization time.Finally,simulation and experiment of the water tank system are carried out respectively to prove the effectiveness of the proposed algorithm.Experimental results show that,compared with existing methods,the proposed algorithm has more advantages,in which the tuning time is reduced by 34.84％,and the time domain performance indices such as the adjustment time are improved by 43.98％.关键词
模型预测控制/聚类/极限学习机/裸骨粒子群/参数整定Key words
model predictive control/clustering/extreme learning machine/bare bones particle swam/param-eter tuning分类
计算机与自动化引用本文复制引用
贺宁,习坤,高峰,刘月笙..基于FCM-ELM-BBPS的预测控制参数整定[J].湖南大学学报（自然科学版）,2023,50(12):168-177,10.基金项目
国家自然科学基金资助项目(61903291),National Natural Science Foundation of China(61903291) （61903291）
陕西省重点研发计划项目(2022NY-094),Key Research and Development Program of Shaanxi Province(2022NY-094) （2022NY-094）
