论文标题
基于物理学的基于学习的自适应控制SE（3）歧管
Physics-guided Learning-based Adaptive Control on the SE(3) Manifold
论文作者
论文摘要
在现实世界的机器人技术应用中，机器人动力学的准确模型对于在快速变化的操作条件下的安全和稳定控制至关重要。这激发了使用机器学习技术来近似机器人动力学及其在状态控制轨迹的训练集中的干扰。本文表明，物理定律引起的归纳偏见可用于提高近似动力学模型的数据效率和准确性。例如，使用其$ SE（3）$姿势和满足能量原理的保存来描述许多机器人的动态，包括地面，空中和水下车辆。我们通过在神经普通微分方程（ODE）网络的设计中施加汉密尔顿运动方程的结构来设计机器人动力学的物理合理模型。汉密尔顿结构保证了$ SE（3）$运动限制和通过建筑节能的满意度。它还使我们能够得出一个基于能量的自适应控制器，该控制器可以在补偿干扰的同时实现轨迹跟踪。我们基于学习的自适应控制器将在发动不足的四极管机器人上进行验证。
In real-world robotics applications, accurate models of robot dynamics are critical for safe and stable control in rapidly changing operational conditions. This motivates the use of machine learning techniques to approximate robot dynamics and their disturbances over a training set of state-control trajectories. This paper demonstrates that inductive biases arising from physics laws can be used to improve the data efficiency and accuracy of the approximated dynamics model. For example, the dynamics of many robots, including ground, aerial, and underwater vehicles, are described using their $SE(3)$ pose and satisfy conservation of energy principles. We design a physically plausible model of the robot dynamics by imposing the structure of Hamilton's equations of motion in the design of a neural ordinary differential equation (ODE) network. The Hamiltonian structure guarantees satisfaction of $SE(3)$ kinematic constraints and energy conservation by construction. It also allows us to derive an energy-based adaptive controller that achieves trajectory tracking while compensating for disturbances. Our learning-based adaptive controller is verified on an under-actuated quadrotor robot.
