Online self-evolving fuzzy controller with global learning capabilities
This paper presents an online self-evolving fuzzy controller with global learning capabilities. Starting from very simple or even empty configurations, the controller learns from its own actions while controlling the plant. It applies learning techniques based on the input/output data collected during normal operation to modify online the fuzzy controllers structure and parameters. The controller does not need any information about the differential equations that govern the plant, nor any offline training. It consists of two main blocks: a parameter learning block that learns proper values for the rule consequents applying a local and a global strategy, and a self-evolving block that modifies the controllers structure online. The modification of the topology is based on the analysis of the error surface and the determination of the input variables which are most responsible for the error. Simulation and experimental results are presented to show the controllers capabilities.
evolving fuzzy system adaptive control evolutionary methodology adaptive fuzzy control