Fuzzy modeling of human control strategy for over head crane

A modified table look-up scheme for fuzzy systems is applied to learn and imitate human control strategy in tracking control of an overhead crane. Applications of modeling human behavior are not only designing controllers based on human action, but also learning human behavior for studying it and predicting its response. Learning human behavior in itself is not a new research issue; however, when paying particular attention to its random nature, it is still a challenging problem. Consequently, we focus here on fuzzy learning with a consideration for random nature of human behavior. A computer simulator is developed here for manual control of the crane and the simulation results after learning human control strategy confirm that the new modified method performs better than standard learning method