神经李雅普诺夫模型预测控制：从次优实例中学习安全全局控制器
Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples
摘要：
随着对数据驱动控制技术的兴趣日益增长，模型预测控制（MPC）提供了可靠地利用剩余数据的机会，特别是在考虑安全性和稳定性的同时。在许多现实世界和工业应用中，通常具有现有的控制策略，例如，由人类操作员执行。这项工作的目的是通过学习一个新的控制器，保持安全性和稳定性，以改善这种未知的，安全的，但次优的政策。学习如何安全是直接从数据和系统约束的知识中实现的。该算法交替学习终端成本和更新MPC参数根据稳定性度量。终端成本被构造为一个李雅普诺夫函数神经网络的目的是恢复或扩展的稳定区域的初始演示使用一个短的预测范围。定理的特点的稳定性和性能的学习MPC轴承模型的不确定性和次优性，由于函数逼近。所提出的算法的有效性证明了非线性连续控制任务的软约束。所提出的方法可以改进初始演示器也在实践中，并实现更好的稳定性比流行的强化学习基线。
英文摘要：
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account. In many real-world and industrial applications, it is typical to have an existing control strategy, for instance, execution from a human operator. The objective of this work is to improve upon this unknown, safe but suboptimal policy by learning a new controller that retains safety and stability. Learning how to be safe is achieved directly from data and from a knowledge of the system constraints. The proposed algorithm alternatively learns the terminal cost and updates the MPC parameters according to a stability metric. The terminal cost is constructed as a Lyapunov function neural network with the aim of recovering or extending the stable region of the initial demonstrator using a short prediction horizon. Theorems that characterize the stability and performance of the learned MPC in the bearing of model uncertainties and sub-optimality due to function approximation are presented. The efficacy of the proposed algorithm is demonstrated on non-linear continuous control tasks with soft constraints. The proposed approach can improve upon the initial demonstrator also in practice and achieve better stability than popular reinforcement learning baselines.
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。
