用线性化高斯过程实现无人机实时高频自适应控制。
Enabling On-Chip High-Frequency Adaptive Linear Optimal Control via Linearized Gaussian Process
- 用线性化高斯过程建模外部气动干扰，结合模型预测控制。
- 实测表明可在真实无人机上实现高频实时控制，跟踪误差可接受。
- 采样阶段融合端到端贝叶斯优化，提升控制鲁棒性，适合复杂飞行场景。
不可预测且复杂的气动效应给精确飞行控制带来挑战，例如上层飞行器对下层的尾流影响。传统方法难以准确建模此类交互，导致飞行器间需预留较大安全距离。此外，真实无人机上的控制器通常要求高频率运行且片上计算资源有限，使自适应控制设计更难实现。为此，本文引入高斯过程（GP）建模自适应外部气动效应，并结合线性模型预测控制（LMPC）。通过将GP线性化，实现高频率实时求解。同时，在采样阶段集成端到端贝叶斯优化以缓解线性化带来的误差，提升控制性能。仿真与真实四旋翼实验结果均表明，该方法可实现可实时求解的计算速度，且跟踪误差在可接受范围内。
原文摘要 · Abstract (English)
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.
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