模型预测控制神经网络方法的训练与评价
On Training and Evaluation of Neural Network Approaches for Model Predictive Control
摘要：
本文的贡献是一个框架的训练和评估模型预测控制（MPC）实施约束神经网络。最近的研究提出使用具有可微凸优化层的神经网络来实现模型预测控制器。其动机是用具有优化层的神经网络形式的学习映射代替安全关键反馈控制系统中的实时优化。这种映射将状态向量作为输入，并将预测的控制律作为输出。使用从离线MPC模拟生成的训练数据进行学习。然而，在文献中缺乏一个通用的框架来表征学习方法的模型验证和有效的训练数据生成。在本文中，我们采取的第一步发展这样一个连贯的框架。我们讨论了学习问题与系统辨识的相似之处，特别是输入设计，模型结构选择和模型验证。我们考虑在PyTorch中研究神经网络架构，使用CVXPY将显式MPC约束实现为可微优化层。我们提出了一种有效的方法，生成MPC输入样本的MPC模型的约束，使用打了就跑采样器。通过使用OSOP离线求解MPC来生成相应的真实输出。我们提出了不同的指标来验证所得到的方法。我们的研究进一步的目的是探索从培训和评估的角度将领域知识纳入网络结构的优势。不同的模型结构进行了数值测试，使用所提出的框架，以获得更多的见解的属性约束神经网络为基础的MPC。
英文摘要：
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks. Recent studies have proposed to use neural networks with differentiable convex optimization layers to implement model predictive controllers. The motivation is to replace real-time optimization in safety critical feedback control systems with learnt mappings in the form of neural networks with optimization layers. Such mappings take as the input the state vector and predict the control law as the output. The learning takes place using training data generated from off-line MPC simulations. However, a general framework for characterization of learning approaches in terms of both model validation and efficient training data generation is lacking in literature. In this paper, we take the first steps towards developing such a coherent framework. We discuss how the learning problem has similarities with system identification, in particular input design, model structure selection and model validation. We consider the study of neural network architectures in PyTorch with the explicit MPC constraints implemented as a differentiable optimization layer using CVXPY. We propose an efficient approach of generating MPC input samples subject to the MPC model constraints using a hit-and-run sampler. The corresponding true outputs are generated by solving the MPC offline using OSOP. We propose different metrics to validate the resulting approaches. Our study further aims to explore the advantages of incorporating domain knowledge into the network structure from a training and evaluation perspective. Different model structures are numerically tested using the proposed framework in order to obtain more insights in the properties of constrained neural networks based MPC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
