Takagi–Sugeno–Kang Fuzzy Inference Tracking Controller for UAV Bicopter

The UAV bicopter is a double-propeller system whose main objective is to stabilize a rod at a given angle by precisely controlling the rotation speed of each propeller. This mechanism generates asymmetric thrust forces that induce a torque on the bar, thus allowing its pitch angle to be modified. Since its dynamics involve complex interactions between the thrust generated by the rotors, aerodynamic effects, and the pendulum behavior of the system, the bicopter is classified as a highly nonlinear system sensitive to external disturbances. To address this complexity, the implementation of a fuzzy Takagi–Sugeno–Kang (TSK) controller is proposed. This controller decomposes the nonlinear dynamics into multiple local linear models associated with a specific operating condition, such as different pitch angles and rotor speeds. The control strategy provides accurate trajectory tracking and effectively handles disturbances and varying conditions, making this approach a practical solution for both dynamic and uncertain environments. This strategy ensures precise trajectory tracking and demonstrates robust performance compared to other control methods, such as PID and LQR, which often struggle with disturbances and system nonlinearities. The TSK controller has proven its effectiveness in experimental trajectory tracking tests, achieving root mean square errors (RMSEs) of 0.2049, 0.3269, 0.3899, 0.3335, and 0.2494, which evaluate the average error in degrees of the system concerning the target position, for tracking trajectories of −10 to 10, −12 to 12, −15 to 15, −17 to 17, and −20 to 20 degrees, respectively.