Data-Driven Estimation of Quadrotor Motor Efficiency via Residual Minimization

Abstract

A data-driven framework is proposed for online estimation of quadrotor motor efficiency via residual minimization. The problem is formulated as a constrained nonlinear optimization that minimizes trajectory residuals between measured flight data and predictions generated by a quadrotor dynamics model. A sliding-window strategy enables online estimation, and the optimization is efficiently solved using an iteratively reweighted least squares (IRLS) scheme combined with a primal-dual interior-point method, with inequality constraints enforced through a logarithmic barrier function. Robust z-score weighting is employed to reject outliers, which is particularly effective in motor clipping scenarios where the proposed estimator exhibits smaller spikes than an EKF baseline. Compared to traditional filter-based approaches, the batch-mode formulation allows selective inclusion of data segments via IRLS reweighting and hard-rejection. This structure is well-suited for online estimation and supports applications such as fault detection and isolation (FDI), health monitoring, and predictive maintenance in aerial robotic systems. Simulation results under various degradation scenarios demonstrate the accuracy and robustness of the proposed estimator.