Reduced-order Observer Based Trajectory Tracking Control of Quadrotor Subject to Noises and Disturbances

• Published in: International journal of control, automation, and systems Vol. 23; no. 3; pp. 860 - 868
• Main Authors: Ahmed, Nigar, Er, Meng Joo, Shah, Syed Awais Ali
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.03.2025; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Adaptive algorithms; Adaptive control; Control; Control algorithms; Control theory; Disturbance observers; Engineering; Mechatronics; Regular Papers; Robotics; Robust control; Stability analysis; State observers; Tracking control; Tracking errors; 제어계측공학; quadrotor; robust control; disturbance observer; reduced order observer; Adaptive control
• ISSN: 2005-4092; 1598-6446; 2005-4092
• DOI: 10.1007/s12555-024-0209-5

This paper presents a control technique to achieve trajectory tracking for a quadrotor subject to internal noises and external disturbance. The methodology involves designing a nonlinear disturbance observer to estimate and reject six different types of disturbances within the quadrotor model, followed by employing state transformation using tracking error and backstepping state space variables to formulate a robust control algorithm. In addition, adaptive laws are obtained using Lyapunov criteria to achieve online control gain tuning. Furthermore, a state observer technique utilizing a reduced-order observer is designed to estimate only the rotational and translational rates using measurable outputs. Finally, the control algorithm is finalized by using the estimated states and disturbances, and adaptive laws. The developed robust adaptive control technique relies solely on desired trajectory information and measurable rotational and translational outputs, thereby reducing the number of onboard sensors required for measurements of rotational and translational rates. The stability analysis is carried out using Lyapunov theory proving asymptotic convergence to the neighbourhood of origin. Simulations are performed on a DJI F450 quadrotor, demonstrating effectiveness and tracking performance of controller for two desired reference trajectories.