将深度神经网络与模型预测控制结合，动态分配控制权以提升系统安全性。
Algorithmic design and implementation considerations of deep MPC
- 用神经网络学习模型不确定性，由MPC处理约束条件，实现控制权分工。
- 实验表明，控制权分配不当会导致系统性能下降，尤其在四轮滑移转向系统中明显。
- 适合关注机器人控制安全性和学习型控制器设计的研究者阅读。
深度模型预测控制（Deep MPC）是将模型预测控制与深度学习相结合的新兴领域。本文聚焦于一种特定方法：将深度神经网络嵌入到MPC框架中，使神经网络与MPC共同分担控制职责。具体而言，神经网络负责学习模型中的不确定性，而MPC则确保系统满足约束条件。该方法的优势在于可利用系统运行时采集的数据对神经网络进行微调，同时在学习过渡阶段防止不安全行为的发生。本文阐述了Deep MPC的实现挑战，提出了一种算法化的方法来分配控制权，并指出控制权分配不当可能导致性能下降。通过一个四轮滑移转向动力学系统的数值实验，说明了这一问题的成因。
原文摘要 · Abstract (English)
Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC controller, in such a way that the neural network learns the model uncertainties while the MPC handles constraints. The approach is appealing because training data collected while the system is in operation can be used to fine-tune the neural network, and MPC prevents unsafe behavior during those learning transients. This manuscript explains implementation challenges of Deep MPC, algorithmic way to distribute control authority and argues that a poor choice in distributing control authority may lead to poor performance. A reason of poor performance is explained through a numerical experiment on a four-wheeled skid-steer dynamics.
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