Intelligent flight controllers for helicopter control

A methodology for design of an intelligent helicopter flight controller is presented in this paper. The methodology combines artificial neural network, genetic algorithms, conventional PID-controllers, and fuzzy logic algorithms in the design procedure. In this approach, the design of an intelligent controller is based on experimental data collected from actual helicopter flight and not based on analytical models. A neural network is trained to learn the dynamic characteristics of the helicopter. Based on the neural model, the coefficients of a PID-controller used for blade angle control are searched for using genetic algorithms. The main rotor speed controller is designed using fuzzy logic algorithm based on knowledge generated from understanding the aerodynamic theory and analyzing the helicopter experimental flight data. The intelligent helicopter flight controller is formed by combining the blade angle PID-controller and the rotor speed fuzzy controller. Simulation results showed that for desired altitude input H_d, the intelligent controller was able to generate proper control signals for both the blade angle control and the rotor speed control. The helicopter model altitude output H_t follows the desired altitude input H_d. The design procedures, theoretical background, results and conclusions are presented in this paper