Quadrotor trajectory tracking under wind disturbance using backstepping control based on different optimization techniques

Enhancing the control techniques of quadrotors to improve their precision and robustness against wind is crucial for expanding their practical applications and reliability. Quadrotors are increasingly utilized in fields such as aerial surveying, delivery services, disaster response and military operations, where stability and accuracy are paramount. Wind disturbances pose a significant challenge, often compromising the performance and safety of these drones.

This research explores the efficacy of various optimization techniques in enhancing the performance of quadrotor control under wind disturbances. After mathematical modeling of a quadrotor, a backstepping controller is developed for this system and then is optimized by different metaheuristic methods: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Flower Pollination Algorithm (FPA). Each optimization technique is applied to fine-tune the backstepping controller parameters, with the objective of improving the quadrotor's precision, speed, stability, and robustness. Extensive simulations of quadrotor trajectory tracking are conducted to evaluate and compare the performance of these optimized controllers in the presence of wind disturbances.

The results highlight the relative advantages and limitations of each optimization method in terms of response time, overshoot and the deviation rate from the desired trajectory under wind disturbance, providing critical insights into their suitability for enhancing quadrotor control in dynamic and challenging environments.