Self-Organising Fuzzy Logic Control with a New On-Line Particle Swarm Optimisation-based Supervisory Layer
Self-Organising Fuzzy Logic Control with a New On-Line Particle Swarm Optimisation-based Supervisory Layer
复制标题
具有基于新在线粒子群优化的监控层的自组织模糊逻辑控制
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Mahfouf
中科院分区：
文献类型：
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作者：
M. Ehtiawesh;M. Mahfouf
自组织模糊逻辑控制（SOFLC）是模糊逻辑控制器的扩展版本，旨在使模糊控制器减少对先前知识的依赖。自引入SOFLC以来，仅进行了几次尝试来创建一个性能索引表，该表负责根据受控过程的动态对低级控制“适应性”进行修正。本文提出了一种新的动态监控层，使控制器的结构能够在线适应任意给定的性能标准。在该机制中，控制器从空规则库开始，使用在线粒子群优化（PSO）算法自适应性能指标（PI）表的单元格，同时向低级模糊规则库发出控制动作。在非线性肌肉关系过程中进行的仿真结果表明，该方案在精确跟踪和有效的模糊规则提取（模糊规则的保守数量）方面优于标准SOFLC方案。
The Self-Organising Fuzzy Logic Control (SOFLC) which is an extended version of the Fuzzy logic controller was designed to make Fuzzy controllers work with less dependency on previous knowledge. Since the introduction of the SOFLC, only a few attempts have been made to create a performance index table that is responsible for the corrections of the low-level control ‘adaptable’ according to the dynamics of the process under control. In this paper a new dynamic supervisory layer is proposed which enables the controller to adapt its structure on-line to any given certain performance criteria. In this mechanism, the controller starts from an empty rule-base and uses an on-line Particle Swarm Optimisation (PSO) algorithm to adapt the cells of the performance index (PI) table while issuing control actions to the low-level fuzzy rule-base. The Simulation results achieved when the proposed scheme was tested on a non-linear muscle relation process showed that it is superior to the standard SOFLC scheme in terms of accurate tracking and efficient fuzzy rule-base elicitation (a conservative number of fuzzy rules).