Unmanned helicopters are one of the most important unmanned aerial vehicles. This vehicle is a completely unstable dynamic system. In addition to the inherent instability of this aerial vehicle, by adding an external slung load to this system, control challenge of this system will increase dramatically. Thus, it is necessary to stabilize this system by means of a proper control system. Design of this controller requires nonlinear dynamical equations of the system, which can be obtained using the Newton Euler equations. In this case, the slung load should be modeled in three dimensions and on two planes, and unlike most of the previous works, it should not be considered as a single plane motion. Hover and forward flights are the most commonly used helicopter flight modes which are used in many operations. In order to achieve and remain stable in these situations, it requires a very precise and online adjustment of the aerial vehicle controller gains. This research seeks to do such online adjustments without human intervention. To perform such a task, it is necessary to design control algorithms for the aerial vehicle attitude stability. In order to improve the transient response of the system, overshoot and settling time in attitude control, a control system is proposed which is designed based on the fuzzy logic. This controller regulates the PID gains online using the fuzzy rule base. The designed controller is named self-tuning Fuzzy PID (STF-PID). In addition to the designed Fuzzy PID controller a new controller is presented. This controller name is self-tuning compensatory nerorufuzzy controller (STCNF-PID). In this controller, the training data and the gradient descent algorithm can be used to initially tune the centers and the widths of the input and output membership functions. Simulation results shows better performance of the presented STF-PID controller in comparison with the conventional PID controller. Moreover, this results indicates that the STCNF-PID is superior in performance with respect to the STF-PID. Keywords: Unmanned helicopter, self-tuning controller, PID, compensatory neurofuzzy system, attitude control system
