AUTO2050 Soft Computing A review of fuzzy control for helicopter navigation By Thomas Höglund

1. AUTO2050 Soft Computing A review of fuzzy control for helicopter navigation By Thomas Höglund 19.3.2011

2. About helicopters • Handling a normal helicopter requires much skill of the operator who has to simultaneously manipulate several controls. • A normal control system is very complex and non-linear. • The helicopter has six degrees of freedom (6DOF). • Translation or rotation in one direction is dependent on several mechanisms at once, i.e. the DOF are coupled. • These couplings have to be counterbalanced by the pilot or the navigation system. University of Vaasa

3. The basic controls Vertical translation Lateral Longitudinal Because of the inherent delay between control and response, the pilot uses smooth corrections or impulse commands. University of Vaasa

4. The control level • The RPM of the rotor is kept constant at all times. • The angles of attack of the blades are changed to produce all movements except yaw. • The throttle thus has to respond to the varying resistance. • The angle of attack of the blades of the tailrotor governs the yaw. This force sideways has to be compensated. Swash plate: University of Vaasa

5. Fuzzy control • Using fuzzy control it is possible to aid or replace the pilot using a linguistic control strategy. • Such a control system can be called an ”intelligent agent”. • The intelligent agent can fly the helicopter completely autonomously, or act as a middle layer between the pilot and the craft. University of Vaasa

6. The model • Three concepts have to be modeled: • Fuzzy logic control • Human experience and behavior • Helicopter aviation and controls • Each task must follow a set point within a precision range. “If speed is reached and speed is increasing then action is brake” University of Vaasa

7. Control and response • The difference between the current state and the set point is called the ”error”. • The fuzzy control logic acts to continuously minimize the error as the set points change. • In order to avoid overshoots due to inertia, the control often has to reverse just before reaching the target set point. Scaled response Vertical, longitudinal and lateral translations and yaw University of Vaasa

8. Fuzzy sets Fuzzy sets for output Fuzzy sets for input • If the pilot continuously changes the current set point (by manual control), the input from him is the error that is to be minimized. • By having no overlap of sets close to zero, oscillation in the response is reduced, but the steady state error is slightly increased. Fuzzy sets for input, tuned after simulation University of Vaasa

9. Tuning the sets • The translations and rotations each have one range of sets for their instantaneous values and another for the rate of change of these. • Furthermore, the acceleration in each DOF could be taken into account. • In order to make tuning easier and simplify the overall control algorithm, the number of sets used has to be kept to a minimum. University of Vaasa

10. Sources • As sources the following two reports were used: • http://www.cic.unb.br/~ckoike/SympBrazilJapan94.pdf • http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.132.4029&rep=rep1&type=pdf University of Vaasa