论文标题
一个基于GP的实时GP MPC，适用于未知干扰的四轮驱动器
A real-time GP based MPC for quadcopters with unknown disturbances
论文作者
论文摘要
高斯流程（GP）回归已被证明是预测干扰和模型不匹配并将这些信息纳入模型预测控制（MPC）预测的有价值的工具。不幸的是，对经典GPS的推理和学习的计算复杂性是立方体尺度的，这对于实时应用很棘手。因此，全科医生通常受到离线训练，这不适合学习干扰，因为它们的动态可能会随着时间而变化。最近，已经引入了GPS的状态空间公式，从而可以通过线性计算复杂性进行推理和学习。本文提出了一个框架，该框架可以在线学习四轮驱动器上的干扰动态，可以使用GPS的状态空间公式在毫秒内执行。获得的干扰预测与MPC相结合，从而导致JMAVSIM模拟的性能显着提高。在Raspberry Pi 4 B上评估计算负担，以证明实时适用性。
Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications. Thus GPs are commonly trained offline, which is not suited for learning disturbances as their dynamics may vary with time. Recently, state-space formulation of GPs has been introduced, allowing inference and learning with linear computational complexity. This paper presents a framework that enables online learning of disturbance dynamics on quadcopters, which can be executed within milliseconds using a state-space formulation of GPs. The obtained disturbance predictions are combined with MPC leading to a significant performance increase in simulations with jMAVSim. The computational burden is evaluated on a Raspberry Pi 4 B to prove the real-time applicability.
