New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model

The online evolution and learning of fuzzy systems is highly important when dealing with changing environments over time. This capability is especially relevant in the field of control, due to the special characteristics of control problems. In this field, techniques capable of developing controllers with a minimum amount of prior knowledge about the plants to be controlled are desired. Furthermore, these controllers should provide a reduced number of interpretable rules. This paper presents a new Online Self-Evolving Neuro Fuzzy controller based on the Taylor Series Neuro Fuzzy (TaSe-NF) model. Under the assumption of no prior knowledge about the differential equations that define the plant to be controlled, this methodology is capable of incrementally evolving the structure of the controller and adapting its parameters online, while controlling the plant. The new methodology uses a scattered distribution of the fuzzy rules, thereby reducing the number of rules in the fuzzy controller. Moreover, the use of the TaSe-NF model to represent the antecedents of the rules enhances the interpretability of the obtained rules. The proposed evolving fuzzy controller is composed of two main blocks: On the one hand, the online local learning of the rule consequents tackles the task of providing a proper control at the present moment. On the other hand, a structure self-evolution method analyzes the error surface to determine which cluster/rule suffers the worst performance and therefore, needs to be further split. Simulation results are presented to illustrate the capabilities of this new online self-evolving controller.