Adaptive Prescribed-Performance Guidance Law for UAVs with Predefined-Time Convergence

Highlights: What are the main findings? A novel prescribed performance control (PPC) method is developed, which robustly modifies traditional performance function to achieve predefined-time convergence without control singularity, even under the uncertainty of target maneuver. An integrated guidance framework is constructed by synthesizing the prescribed-performance control with an adaptive law, achieving predefined-time convergence of the line-of-sight angle and uncertainty compensation simultaneously. What are the implications of the main findings? This work addresses the control singularity issue, which is intractable for PPC-based guidance laws against highly maneuverable targets, providing a reliable guidance scheme for unmanned aerial vehicles (UAVs) in complex environments. The developed guidance framework can be adapted to UAVs, effectively improving the guidance accuracy and time controllability in dynamic pursuer–evader scenarios. In order to evade interception, advanced aircraft often adopt jump-gliding trajectories to efficiently utilize aerodynamics and achieve complex maneuvers. Precise guidance of UAVs for intercepting such targets is critically challenged due to their high speed and uncertain maneuvers. For terminal guidance scenarios, the extremely short engagement window necessitates strict convergence within the predefined finite time. While PPC offers a promising framework to ensure such convergence with guaranteed transient performance, it suffers from singularity when target uncertainties drive tracking errors beyond performance bounds. To address these challenges, this paper proposes an adaptive prescribed-performance guidance law with predefined-time convergence for UAVs. Built upon the analysis that jump-gliding targets exhibit predominantly longitudinal oscillatory maneuvers, we first establish a velocity model to characterize their motion uncertainties. Using the derived uncertainty bounds and estimated parameters, a predefined-time performance function (PPF) is then developed and robustly modified to eliminate the singularity risk. By integrating this modified PPC with an adaptive law, the proposed framework achieves robust predefined-time convergence of the line-of-sight angle while simultaneously compensating for unknown target maneuvers. Theoretical analysis verifies the framework’s stability, and simulation results demonstrate its effectiveness in intercepting highly maneuverable targets.