Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. The model needs to capture the system behavior in multiple flight regimes and operating conditions, including those producing highly nonlinear effects such as aerodynamic forces and torques, rotor interactions, or possible system configuration modifications. Classical approaches rely on handcrafted models and struggle to generalize and scale to capture these effects. In this paper, we present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions. In addition, physics constraints are embedded in the training process to facilitate the network's generalization capabilities to data outside the training distribution. Finally, we design a model predictive control approach that incorporates the learned dynamics for accurate closed-loop trajectory tracking fully exploiting the learned model predictions in a receding horizon fashion. Experimental results demonstrate that our approach accurately extracts the structure of the quadrotor's dynamics from data, capturing effects that would remain hidden to classical approaches. To the best of our knowledge, this is the first time physics-inspired deep learning is successfully applied to temporal convolutional networks and to the system identification task, while concurrently enabling predictive control.
翻译:精确模拟二次钻探器的系统动态对于保证灵活、安全和稳定的导航至关重要。 模型需要从多个飞行系统和操作条件下捕捉系统行为, 包括产生高度非线性效应的系统行为, 如空气动力力和托盘、转子相互作用, 或可能的系统配置修改。 经典方法依赖于手工制作模型, 并努力推广和规模以捕捉这些效应。 在本文中, 我们展示了一种全新的物理- 受物理启发的时空演动网络( PI- TCN ) 方法, 来学习二次钻探器的系统动态, 纯粹从机器人的经验中学习。 我们的方法结合了稀疏的时空演动和密集的前向连接的显性力量, 以便作出准确的系统预测。 此外, 物理学方面的制约因素嵌入了培训过程, 以便利网络对培训分布之外的数据进行概括化。 最后, 我们设计了一种模型预测控制方法, 将学到的动力纳入准确的闭路轨轨轨轨轨迹跟踪, 完全利用所学的模型预测, 重新定位。 实验结果表明, 我们的方法精确地提取了时间变动的系统结构结构结构结构结构, 继续从深层分析到模型的模型的模型, 将成功的定位数据定位分析, 继续捕取。