An Online Learning Approach to Model Predictive Control
An Online Learning Approach to Model Predictive Control
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DOI：
10.15607/rss.2019.xv.033
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Nolan Wagener;Ching-An Cheng;Jacob Sacks;Byron Boots
中科院分区：
文献类型：
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作者：
Nolan Wagener;Ching-An Cheng;Jacob Sacks;Byron Boots
模型预测控制（MPC）是一种解决动态控制任务的强大技术。在本文中，我们表明MPC与在线学习之间存在紧密联系，在线学习是优化文献中分析在线决策的一个抽象理论框架。这种新视角为利用强大的在线学习算法设计MPC算法提供了基础。具体而言，我们基于动态镜像下降（DMD）提出了一种新算法，DMD是一种为非平稳设置而设计的在线学习算法。我们的算法——动态镜像下降模型预测控制（DMD - MPC）代表了一类通用的MPC算法，其中包括许多现有技术作为特殊实例。DMD - MPC还为MPC中先前使用的启发式方法提供了新视角，并提出了一种设计新MPC算法的原则性方法。在本文的实验部分，我们展示了DMD - MPC的灵活性，在一个简单的模拟倒立摆以及模拟和现实世界的激进驾驶任务上呈现了一组新的MPC算法。现实世界实验的视频可在这个https网址和这个https网址找到。
Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision making in the optimization literature. This new perspective provides a foundation for leveraging powerful online learning algorithms to design MPC algorithms. Specifically, we propose a new algorithm based on dynamic mirror descent (DMD), an online learning algorithm that is designed for non-stationary setups. Our algorithm, Dynamic Mirror Descent Model Predictive Control (DMD-MPC), represents a general family of MPC algorithms that includes many existing techniques as special instances. DMD-MPC also provides a fresh perspective on previous heuristics used in MPC and suggests a principled way to design new MPC algorithms. In the experimental section of this paper, we demonstrate the flexibility of DMD-MPC, presenting a set of new MPC algorithms on a simple simulated cartpole and a simulated and real-world aggressive driving task. Videos of the real-world experiments can be found at this https URL and this https URL.