Unpredictable and complex aerodynamic effects pose significant challenges to achieving precise flight control, such as the downwash effect from upper vehicles to lower ones. Conventional methods often struggle to accurately model these interactions, leading to controllers that require large safety margins between vehicles. Moreover, the controller on real drones usually requires high-frequency and has limited on-chip computation, making the adaptive control design more difficult to implement. To address these challenges, we incorporate Gaussian process (GP) to model the adaptive external aerodynamics with linear model predictive control. The GP is linearized to enable real-time high-frequency solutions. Moreover, to handle the error caused by linearization, we integrate end-to-end Bayesian optimization during sample collection stages to improve the control performance. Experimental results on both simulations and real quadrotors show that we can achieve real-time solvable computation speed with acceptable tracking errors.

翻译：不可预测且复杂的气动效应（例如上层飞行器对下层飞行器的下洗效应）对实现精确飞行控制构成了重大挑战。传统方法通常难以准确建模这些相互作用，导致控制器需要在飞行器之间设置较大的安全裕度。此外，真实无人机上的控制器通常需要高频运行且片上计算资源有限，这使得自适应控制设计更难以实现。为应对这些挑战，我们将高斯过程（GP）与线性模型预测控制相结合，以建模自适应外部气动效应。通过对GP进行线性化处理，实现了实时高频求解。此外，为处理线性化引入的误差，我们在样本采集阶段集成了端到端的贝叶斯优化，以提升控制性能。在仿真和真实四旋翼飞行器上的实验结果表明，我们的方法能以可接受的跟踪误差实现实时可求解的计算速度。