PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments.

Target tracking for fixed-wing unmanned aerial vehicles (UAVs) in complex urban environments faces challenges including potential target state loss and occlusion by multiple obstacles, typically large vertical structures like high-rise buildings. This necessitates tracking algorithms capable of both target state estimation and prediction. To address this, this paper proposes a Predictive-Estimative Nonlinear Control (PENC) framework. This framework optimizes the UAV-gimbal system's control inputs in real-time to ensure the target remains within the camera's field of view (FOV) despite obstacles, while simultaneously using historical target state measurements to accurately estimate its current state. When target state measurements is lost, PENC dynamically adjusts the measurement noise covariance matrix (R) and the process noise covariance matrix (Q) within the estimator via a unique weight-switching mechanism. This shifts reliance to the target's dynamic model and historical data for state prediction. Simulations conducted in an environment simulating typical urban vertical obstacles demonstrate that the proposed method significantly outperforms both conventional Nonlinear Model Predictive Control (NMPC) and NMPC with Extended Kalman Filtering (NMPC-EKF) in UAV target tracking performance. This improvement is particularly evident during target measurements loss scenarios, effectively ensuring tracking continuity and robustness. Quantitative results show that PENC increases the Target Visibility Percentage (TVP) by up to 14 percentage points, reduces the Mean Recovery Time (MRT) to 0.03 seconds, and lowers the prediction Root Mean Square Error (RMSE) during state loss by [Formula: see text].