Repeated ADRC parameter optimization under wide flight-envelope conditions usually leads to high computational cost when fixed-wing UAVs operate across multiple flight states, while the adaptability of controller parameters between different operating conditions also becomes difficult to maintain.
To address this issue, a Kriging-assisted efficient global optimization method is developed for pitch-channel ADRC tuning of fixed-wing UAVs. Several representative operating conditions distributed along the flight envelope are selected to establish corresponding pitch-channel models and the optimized controller parameters are further reconstructed into a continuous gain-scheduling strategy. A nonlinear six-degree-of-freedom simulation model with actuator constraints is subsequently adopted to evaluate the full-trajectory control performance of the proposed method.
The obtained results show that the proposed approach reduces the mean number of closed-loop simulation evaluations by approximately 32.7% and 43.5% relative to GA and PSO, respectively, while decreasing the average computational time by 28.4% and 39.8%.
This work combines Kriging surrogate modeling with ADRC gain scheduling for full-trajectory UAV control and provides an efficient engineering-oriented solution for multi-condition controller optimization and nonlinear flight implementation.
