平行模糊控制:虚实互动、相互增强的自学习控制方法  

Parallel fuzzy control:a self-learning control method with virtual-real interaction and mutual enhancemen

作　　者：陈德旺 欧纪祥 CHEN Dewang;OU Jixiang(School of Transportation,Fujian University of Technology,Fuzhou 350118,China;College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福建理工大学交通运输学院,福建福州350118 [2]福州大学计算机与大数据学院,福建福州350108

出　　处：《智能科学与技术学报》2023年第2期267-273,共7页Chinese Journal of Intelligent Science and Technology

基　　金：国家自然科学基金项目(No.61976055);福建省财政厅教育和科研专项资金项目(No.GY-Z21001);福建理工大学科研启动基金项目(No.GY-Z22071)。

摘　　要：模糊控制具有可解释性和容易实现等优点,但是自学习能力较弱,难以有效利用控制过程中积累的大数据。平行控制是一种新型智能控制方法,能有效利用互联网和大数据,实现虚实互动、相互增强的智能控制。新方法将模糊控制与平行控制相互结合,提出了平行模糊控制的定义和框架,并对其可能的应用进行了探讨。平行模糊控制有可能扩展模糊控制的发展方向,也可能成为平行控制的一个新思路,在保证可解释与可信自动控制的基础上,有效利用大数据和基于数据驱动的一些机器学习算法实现自学习的控制。Fuzzy control has advantages such as interpretability and ease of implementation.However,it is limited by its weak self-learning capability,which makes it difficult to effectively utilize the large amount of data accumulated in the control process.Parallel control is a new intelligent control method that enables intelligent control with virtual-real interaction and mutual enhancement,effectively using the Internet and big data to achieve intelligent control.Fuzzy control and parallel control were combined as a new method,and the definition and framework of parallel fuzzy control were proposed and its possible applications were discussed.Parallel fuzzy control has the potential to extend the development direction of fuzzy control and become a new thinking for parallel control.It can effectively utilize big data and some machine learning algorithms based on data-driven to achieve self-learning control,while ensuring interpretability and credibility.

关 键 词：平行模糊控制 模糊控制 平行控制 ACP方法 自学习控制 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]