论文标题
安全有效的模型预测控制使用神经网络：内部点方法
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach
论文作者
论文摘要
模型预测控制（MPC）提供了一种有用的手段，用于控制具有约束的系统，但遭受重复解决优化问题的计算负担。 MPC的离线（明确）解决方案尝试使用多参数编程或机器学习来减轻实时计算挑战。多参数方法通常应用于线性或二次MPC问题，而基于学习的方法可以更灵活，并且较少的记忆密集度。现有的基于学习的方法提供了重要的加速，但是挑战在保持良好的性能的同时确保了限制满意度。在本文中，我们提供了MPC策略的神经网络参数化，该策略明确编码了问题的约束。通过在无监督的学习范式中探索MPC可行设置的内部，神经网络可以比基于投影的方法更快地发现策略更好，并且表现出大大较短的求解时间。我们使用拟议的策略来解决强大的MPC问题，并在标准测试系统上演示性能和计算提高。
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time. Offline (explicit) solutions for MPC attempt to alleviate real time computational challenges using either multiparametric programming or machine learning. The multiparametric approaches are typically applied to linear or quadratic MPC problems, while learning-based approaches can be more flexible and are less memory-intensive. Existing learning-based approaches offer significant speedups, but the challenge becomes ensuring constraint satisfaction while maintaining good performance. In this paper, we provide a neural network parameterization of MPC policies that explicitly encodes the constraints of the problem. By exploring the interior of the MPC feasible set in an unsupervised learning paradigm, the neural network finds better policies faster than projection-based methods and exhibits substantially shorter solve times. We use the proposed policy to solve a robust MPC problem, and demonstrate the performance and computational gains on a standard test system.
