用深度柯尔莫哥洛夫模型提升无人机轨迹跟踪与稳定性控制
Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control
- 通过深度柯尔莫哥洛夫算子将非线性无人机动力学线性化
- 相比传统非线性模型预测控制，轨迹追踪更准、计算更快
- 适合需要实时控制的嵌入式无人机系统开发
本文提出一种数据驱动的四旋翼飞行器控制框架，将深度柯尔莫哥洛夫算子与模型预测控制（DK-MPC）结合。该方法基于飞行采样数据训练深度柯尔莫哥洛夫算子，构建高维隐空间，在其中将非线性四旋翼动力学近似为线性模型，从而实现高效控制优化。在一系列轨迹跟踪与定点稳定性的数值实验中，该方法展现出更优的追踪精度和显著更低的计算耗时，验证了基于柯尔莫哥洛夫学习方法在处理复杂四旋翼动力学的同时满足嵌入式飞行控制实时性要求的潜力。未来工作将扩展至更敏捷的飞行场景并增强对外部扰动的鲁棒性。
原文摘要 · Abstract (English)
This paper presents a data-driven control framework for quadrotor systems that integrates a deep Koopman operator with model predictive control (DK-MPC). The deep Koopman operator is trained on sampled flight data to construct a high-dimensional latent representation in which the nonlinear quadrotor dynamics are approximated by linear models. This linearization enables the application of MPC to efficiently optimize control actions over a finite prediction horizon, ensuring accurate trajectory tracking and stabilization. The proposed DK-MPC approach is validated through a series of trajectory-following and point-stabilization numerical experiments, where it demonstrates superior tracking accuracy and significantly lower computation time compared to conventional nonlinear MPC. These results highlight the potential of Koopman-based learning methods to handle complex quadrotor dynamics while meeting the real-time requirements of embedded flight control. Future work will focus on extending the framework to more agile flight scenarios and improving robustness against external disturbances.
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