论文标题
坡道网络：通过物理信息神经网络的四肢运动的强大自适应MPC
RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network
论文作者
论文摘要
模型预测控制（MPC）是一种最先进的（SOTA）控制技术，需要迭代地解决硬约束优化问题。对于不确定的动态，基于分析模型的强大MPC施加了其他约束，从而增加了问题的硬度。当需要在较小的时间内需要更多计算时，问题加剧了绩效至关重要的应用程序。过去已经提出了诸如神经网络之类的数据驱动回归方法来近似系统动力学。但是，在没有符号分析先验的情况下，此类模型依赖于大量的标记数据。这会引起非平凡的培训开销。物理知识的神经网络（PINN）以合理的精度获得了近似非线性微分方程（ODE）的非线性系统的吸引力。在这项工作中，我们通过PINNS（RAMP-NET）提出了一个强大的自适应MPC框架，该框架使用了一种神经网络，部分从简单的ODE中训练，部分是由数据训练的。物理损失用于学习代表理想动态的简单odes。访问损失函数内部的分析功能是正规器，为参数不确定性执行了可靠的行为。另一方面，定期数据丢失用于适应剩余干扰（非参数不确定性），在数学建模期间未被误解。实验是在模拟环境中进行轨迹跟踪四极管的。与两种基于SOTA回归的MPC方法相比，我们报告了7.8％至43.2％和8.04％和8.04％至61.5％的速度降低0.5至1.75 m/s的降低。
Model Predictive Control (MPC) is a state-of-the-art (SOTA) control technique which requires solving hard constrained optimization problems iteratively. For uncertain dynamics, analytical model based robust MPC imposes additional constraints, increasing the hardness of the problem. The problem exacerbates in performance-critical applications, when more compute is required in lesser time. Data-driven regression methods such as Neural Networks have been proposed in the past to approximate system dynamics. However, such models rely on high volumes of labeled data, in the absence of symbolic analytical priors. This incurs non-trivial training overheads. Physics-informed Neural Networks (PINNs) have gained traction for approximating non-linear system of ordinary differential equations (ODEs), with reasonable accuracy. In this work, we propose a Robust Adaptive MPC framework via PINNs (RAMP-Net), which uses a neural network trained partly from simple ODEs and partly from data. A physics loss is used to learn simple ODEs representing ideal dynamics. Having access to analytical functions inside the loss function acts as a regularizer, enforcing robust behavior for parametric uncertainties. On the other hand, a regular data loss is used for adapting to residual disturbances (non-parametric uncertainties), unaccounted during mathematical modelling. Experiments are performed in a simulated environment for trajectory tracking of a quadrotor. We report 7.8% to 43.2% and 8.04% to 61.5% reduction in tracking errors for speeds ranging from 0.5 to 1.75 m/s compared to two SOTA regression based MPC methods.
