Framsticks

This paper presents an approach that allows to evolve fuzzy controllers that can be expressed as fuzzy rules in human-readable form and interpreted. For comparison, the evolution is also performed on simple neural controllers. The control task considered here is a balancing problem, where a construct made of articulated elastic elements is equipped with sensors and actuators. The goal of the construct is to keep the top heavy part from touching the ground. Evolved controllers are evaluated using computer simulation. Control systems process signals from tilt sensors to actuators fixed in the construct. During evolution, fuzzy controllers (including their fuzzy sets and rules) are reconfigured by genetic operators in order to maximize fitness of the control. The article compares evolvability of neural and fuzzy controllers and demonstrates how additional, comprehensible knowledge can be gained which explains the work of the fuzzy controller. The representation for the fuzzy control system, evolutionary operators, various evaluation functions, and the best evolved control systems are presented. A sample evolved fuzzy control system is analyzed in detail to explain its behavior.