Multi-History-Weighted Projection-Adaptive Jacobian Control for Close-Range UAV Visual Servoing near Overhead Ground Wires

Close-range UAV visual servoing near overhead ground wires requires simultaneous regulation of the target’s lateral position, apparent width, and orientation angle in the image. Owing to flight-control response lag and visual-processing delay, current image changes may reflect the combined effects of multiple historical control inputs. To address this issue, this paper proposes a multi-history-weighted projection-adaptive Jacobian control method (MHW-PAJ-CLF). For each visual channel, the method takes a weighted sum of the parameter corrections associated with different historical inputs based on response prediction errors and input magnitudes and updates the local Jacobian matrix online under projection bounds. The online estimate is then blended with the nominal model. Control Lyapunov function-based quadratic programming (CLF-QP) uses the blended model to generate velocity and yaw-rate commands, coordinating the regulation of the three visual errors under input constraints. Comparative experiments were conducted on the RflySim–PX4 hardware-in-the-loop platform. The proposed method achieves a mean success rate of 75.50%, exceeding that of the best baseline by 11.05 percentage points. In tests with additional visual-feedback delay and command–response lag, the proposed method achieves lower overall tracking error than single-history PAJ-CLF, indicating that multi-history weighting helps improve visual tracking under time delays.