Iterative learning trajectory control method based on reachable sets for perching maneuvers

The perch maneuver of an aircraft requires high-angle-of-attack flight, rapid deceleration, and precise landing in a target area, involving complex nonlinear dynamics and fast time-varying characteristics. To address the challenges posed by these dynamic complexities and stringent control performance demands, this paper proposes a trajectory control design method based on reachability sets and iterative learning. First, an improved backward reachability set computation algorithm is developed for the perch maneuver control system. Then, an iterative learning trajectory control method is introduced, guided by the reachability set to ensure convergence. This method begins with an inaccurate initial perch trajectory, and iteratively refines the trajectory and optimizes controller parameters using the reachability set of each iteration to guide the next. After several learning iterations, the aircraft can successfully perform the perch maneuver while meeting multiple constraints and accurately landing in the target area, with a large convergence domain guaranteed. Additionally, for cases where the perch maneuver model is unknown, a trajectory control method based on the SINDy identification algorithm is designed. Finally, simulation validation and comparison of the proposed reachability set algorithm and iterative learning trajectory control method are conducted. The results demonstrate that the improved reachability set algorithm more accurately captures the reachability set of the nonlinear perch model, and the designed trajectory control method can quickly learn and achieve successful perch maneuvers even with large initial deviations.