Design Optimal Adaptive Trajectory Tracking Control for Station Keeping and Attitude Control of Quadrotor Using Gray Wolf Optimization

Nowadays, various applications of quadrotors, in different fields such as monitoring, inspection, and rescue has attracted the attention of many researchers. Because of the extensive range of applications for quadrotors, maintaining their position control, condition control, and tracking is important. Thus, it is essential that these issues are considered and resolved. Therefore, based on Newton Euler’s method, structure of a model for analyzing the behavior of the system and design of the controller is introduced in the present research. As the dynamics of quadrotors are nonlinear, the adaptive optimal control algorithm for resolving this issue was used in order for the control parameters to be updated. For updating these control parameters, methods such as optimizing, fuzzy, neural networks and hybrid methods can be used. The fuzzy or neural networks methods do not have the capability of applying constraints such as the operator’s physical restrictions in the updating process. Their overall response speed is greater than the optimization methods. The optimization methods have more capabilities to find better and more acceptable responses than the above methods, but it is noteworthy that they require high-speed processors. Since the objective was to achieve better responses for station keeping and tracking maneuvers, the gray wolf optimization algorithm for updating the control parameters was utilized. In addition, to increase the resistance of the controllers and to optimize the control efforts, the authors proposed to combine the control inputs with other methods such as optimal nonlinear proportional–integral–derivative (pid). Finally, the results of simulations were presented. The results demonstrated that the proposed approach resolved all aspects of the main problem. additionally, the proposed approach can be used in maintaining position control, condition control and tracking.