Designing a Self-adaptive Union-Based Rule-Antecedent Fuzzy Controller Based on Two Step Optimization

摘要

A self-adaptive union-based rule-antecedent fuzzy controller (SURFCon), which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The SURFCon allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the SURFCon, we consider the union-based logic processor (ULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, genetic algorithm (GA) constructs a Boolean skeleton of SURFCon, while stochastic reinforcement learning refines the binary connections of GA-optimized SURFCon for further improvement of the performance index. A cart-pole system is considered to verify the effectiveness of the proposed method.