Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Abstract

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers

“…They can be used to translate a point's coordinates from one reference frame to another and to explain the relationship between two reference frames. The Euler angles, which show how a body rotates around the axes of a coordinate system, are represented by the symbols ϕ, θ, and ψ for the roll, pitch, and yaw angles, respectively [37][38][39][40]. The three fundamental Euler angles can be combined to achieve any orientation for a rigid body.…”

“…and from the equation (33) This study optimizes controller design for agricultural drones by identifying a suitable penalty rate gain, understanding constraints, and drawing conclusions. It develops a design technique based on fuzzy PID control law, digitalizing traditional PID control law for modern applications and utilizing feedback for gain regulation [35][36][37][38][39].…”