Monday 24 November 2025
A data-driven approach to estimating quadrotor motor efficiency has been proposed, offering a more robust and accurate method for monitoring these aerial vehicles. The technique, which uses residual minimization within a sliding window structure, is designed to detect and reject outliers in real-time, making it particularly useful for applications such as fault detection and isolation.
Quadrotors are complex systems that rely on precise control of their motors to maintain stable flight. However, motor efficiency can degrade over time due to various factors such as voltage fluctuations, temperature changes, and mechanical wear. Accurate monitoring of motor efficiency is crucial for ensuring the reliability and performance of these vehicles, particularly in applications such as search and rescue, surveying, and environmental monitoring.
The proposed approach uses a combination of machine learning and optimization techniques to estimate motor efficiency from sensor data collected during flight. The system is designed to operate in real-time, allowing it to detect and respond to changes in motor efficiency quickly and accurately.
One of the key advantages of this approach is its ability to reject outliers in real-time. This is achieved through the use of robust z-score weighting, which helps to identify and discard data points that are significantly different from the rest of the dataset. This is particularly important for quadrotors, which are prone to experiencing sudden and unexpected changes in their environment.
The proposed approach has been tested using a combination of simulation and experimental data. The results show that it outperforms traditional filter-based methods, such as the extended Kalman filter (EKF), in terms of both accuracy and robustness. In particular, the proposed method is able to detect and respond to sudden changes in motor efficiency more quickly and accurately than the EKF.
The implications of this research are significant for the development of reliable and efficient quadrotor systems. By providing a more accurate and robust method for monitoring motor efficiency, this approach has the potential to improve the performance and reliability of these vehicles, making them more suitable for a wide range of applications.
In addition to its practical implications, this research also highlights the importance of data-driven approaches in the development of autonomous systems. As quadrotors become increasingly prevalent in a variety of applications, it is likely that data-driven methods will play an important role in ensuring their reliability and performance.
Overall, this research represents an important step forward in the development of reliable and efficient quadrotor systems.
Cite this article: “Data-Driven Approach for Estimating Quadrotor Motor Efficiency”, The Science Archive, 2025.
Quadrotors, Motor Efficiency, Data-Driven Approach, Machine Learning, Optimization Techniques, Real-Time Monitoring, Robustness, Accuracy, Fault Detection, Autonomous Systems
