Optimization Design and Flight Validation of Pull-Up Control for Air-Deployed UAVs Based on Improved NSGA-II

During the automatic leveling process of small low-cost unmanned aerial vehicles (UAVs) after airdrop, their state parameters and control surface efficiency undergo drastic changes. It is difficult to achieve good control effects using controllers with fixed parameters. To solve these problems, this study proposes a parameter adaptive PID controller based on indicated airspeed. When tuning the controller parameters, in order to ensure the successful pulling of the UAV and the safety of structure and flight, it is necessary to optimize the success rate of pulling up, normal overload, angle of attack (AOA), airspeed, and descent altitude simultaneously. These five indicators are of different importance to the UAV. To facilitate parameter tuning based on these differences, an improved second-generation non-dominated sorting genetic algorithm (NSGA-II) is proposed, which combines a comprehensive fitness mechanism based on target priority and segmented scoring and an adaptive genetic strategy. In this study, different priorities were set for all indicators, and segmented scores were given based on individual indicators to calculate the comprehensive fitness, which guided the evolutionary direction of the population. Then, while the genetic parameters were modified, elite individuals were retained to balance search ability and convergence. Finally, the effectiveness of this mechanism was confirmed through comparative simulation. The flight test results show significant differences from the simulation results of the controller designed in this study, but the basic trend remains consistent. The controller can effectively suppress the oscillations caused by the initial state.