Robust Intelligent Self-Tuning Active Force Control of a Quadrotor with Improved Body Jerk Performance

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Abdelmaksoud, Sherif I., Mailah, Musa, Abdallah, Ayman M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active control; active force control; Aerodynamics; Algorithms; Artificial intelligence; body jerk performance; Comparative studies; Control systems design; Controllers; Disturbances; Earth; Force control; Fuzzy logic; Hybrid control; Intelligent control; iterative learning; Iterative methods; Machine learning; Mathematical model; Mathematical models; Parameter estimation; Propellers; Proportional integral derivative; Quadrotor UAV; Rejection; robust; Robust control; Robustness; Self tuning; Sine waves; Unmanned aerial vehicles; Unmanned helicopters
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3015101

This work presents an innovative hybrid control scheme for a quadrotor unmanned aerial vehicle (UAV) model to improve the disturbances rejection capability and body jerk performance by utilizing an active force control (AFC)-based robust intelligent control system via a simulation study. The proposed intelligent control approach incorporates a proportional-integral-derivative (PID) and an intelligent active force control (IAFC) element yielding a robust PID-IAFC scheme. A detailed mathematical model of a quadrotor system with six degrees of freedom (DOFs) was first derived using the Newton-Euler method taking into consideration the gyroscopic terms, disturbances, aerodynamics, and friction effects. In the derived model, the PID controller was first designed to stabilize the quadrotor model and achieve the required altitude and attitude motions. In addition, different types of external disturbances in the form of sinusoidal waves and repeated impulses (pulsating) were added. An AFC strategy, known as PID-AFC, was designed and incorporated into the PID controller, and was initially tuned heuristically. Then, an artificial intelligence (AI)-based method employing an iterative learning (IL) algorithm was designed and implemented into the AFC (ILAFC) to estimate the control parameters automatically while on-line. Thereafter, the performance of the ILAFC was compared to the AFC with fuzzy logic (FL) which became known as FLAFC. Also, a selftuning (ST) PID controller was designed and employed based on the FL method to automatically tune the PID gains based on the prescribed operating and loading conditions. Moreover, a comparative study of the system performance was carried out utilizing the PID, PID-AFC, ILAFC, FLAFC, and ST-FPID-AFC schemes to analyze the system characteristics. Furthermore, the effectiveness of the AFC-based intelligent controller was investigated in connection with the body jerk performance in the presence of external disturbances. The simulated results reveal the effectiveness and robustness of the proposed control strategy based on the IAFC technique in improving the disturbance rejection capability and body jerk performance by 17% in the presence of uncertainties and external disturbances.