In this paper, a hybrid fault-tolerant control (FTC) system for quadcopter unmanned aerial vehicles (UAVs) is proposed to counteract the deterioration of the performance of the quadcopter due to motor faults. A robust and adaptive approach to controlling fault conditions is simulated by combining an integral back-stepping controller for translational motion and a nonlinear observer-based sliding-mode controller for rotational motion, and then implemented on an FPGA. Finally, motor faults are treated as disturbances and are successfully compensated by the controller to ensure safe and high-performance flight. Simulations were taken at 0%, 10%, 30%, and 50% motor faults to test how effective the proposed FTC system is. After simulations, the controller’s real-time performance and reliability were validated through hardware-in-the-loop (HIL) experiments. The results validated that the proposed hybrid controller can guarantee stable flight and precision tracking of the desired trajectory when any single motor fails up to the order of 50%. It shows that the controller is of high fault tolerance and robustness, which will be a potential solution for improving the reliability of UAVs in fault-prone conditions.

This review article provides a comprehensive analysis of the control systems for Unmanned Aerial Vehicles (UAVs), focusing on current trends and future directions. The primary objective is to synthesize recent advancements, evaluate emerging technologies, and identify future research pathways in UAV control systems. The review article includes various UAV types, control system types and architectures, some key technologies, and also the actuators and sensor faults in UAVs. Key findings include advancements in autonomous navigation, Swarm UAVs, and the integration of artificial intelligence and machine learning (AI and ML) techniques. Hybrid control systems are another emerging mechanism that integrates different control strategies to optimize performance. Advancements in sensor technology, communication systems, and AI are driving the evolution of UAV control systems. The applications of UAVs presented in this article demonstrate the versatility and growing importance of UAVs across various sectors. Furthermore, some challenges and limitations of control algorithms are also presented. Future directions include advanced hybrid models, enhanced AI integration, hardware in the loop (HIL) simulations, brain-computer interface (BCI) remote control, and emerging optical communication technologies are also discussed. Sustainable UAV operations and evolving regulatory frameworks will play critical roles in shaping the future landscape of UAV control systems.

The quadcopters are extremely sensitive to motor failures, which may occur secondary to broken propellers or motor faults during the flight operation. These faults alter the dynamics of the quadcopters, whereas the control algorithms generally used for controlling are no longer as effective. This paper presents a reliable fault-tolerant controller that can handle quadcopter motor failures in order to address these issues. The proposed control system uses a nonlinear disturbance observer-based sliding mode control (NLDO-SMC) to significantly manage the quadcopter’s rotational dynamics, while an integrated back-stepping controller (IBSC) is employed to supervise the translational movement. The developed nonlinear formulation’s NLDO-SMC, predicts motor failures and keeps the system resilient to unforeseen circumstances and disruptions that can arise while the vehicle is in the air. The suggested fault-tolerant control paradigm is validated by hardware-in-the-loop (HIL) experimental investigation and extensive simulations. The controller’s performance in real-world scenarios is thoroughly examined using the FPGA model. The results indicate that the controller can effectively deal with the effects of motor faults and restore control over the quadcopter’s stability. The proposed controller achieves robustness to up to 50% fault in a single motor, and the quadcopter follows the desired path with well-predicted rotation angles, thus proving the ability of the model to increase fault tolerance and operational safety under fault conditions.