通过线性化高斯过程实现片上高频自适应线性最优控制
Enabling On-Chip High-Frequency Adaptive Linear Optimal Control via Linearized Gaussian Process
AI总结：
针对无人机复杂空气动力学与片上高频计算限制，提出线性化高斯过程与线性模型预测控制结合并辅以贝叶斯优化的自适应控制方法，实现实时高频求解且保持可接受跟踪误差。
摘要：
不可预测且复杂的空气动力学效应（如上方无人机对下方产生的下洗效应）对精确飞行控制构成了重大挑战。传统方法难以准确建模这些交互，导致控制器需在飞行器间保留较大的安全裕度。此外，实际无人机控制器通常要求高频运行且片上计算能力有限，使自适应控制设计更难实现。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，对自适应外部空气动力学进行建模。GP被线性化以实现实时高频求解。此外，为处理线性化带来的误差，我们在样本采集阶段引入端到端贝叶斯优化以提升控制性能。仿真与真实四旋翼实验结果表明，该方法可实现实时可解的计算速度，且跟踪误差在可接受范围内。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
