Learning Helicopter Control through \teaching by Showing"

A model-free "teaching by showing" methodology is developed to train a fuzzy-neural controller for an autonomous robot helicopter. The controller is generated and tuned using training data gathered while a teacher operates the helicopter. A hierarchical behavior-based control architecture is used, with each behavior implemented as a hybrid fuzzy logic controller (FLC) and general regression neural network controller (GRNNC). The FLCs and GRNNCs are generated through "teach-ing by showing." The FLCs are built during initial controller generation, remain static once created, and provide coarse control of the helicopter. The GRN-NCs are incrementally built and modiied whenever the controller does not meet performance criteria, are dynamic , and provide ne control, enhancing the control of the FLCs. The methodology has been successfully applied in simulation and, in the future, will be applied on a radio control (RC) model helicopter for real world validation.