论文标题
界定模型预测控制和神经网络之间的差异
Bounding the difference between model predictive control and neural networks
论文作者
论文摘要
关于反馈控制系统的未来是否将由数据驱动或模型驱动的方法主导的争论越来越大。这两种方法中的每一种都有自己的免费优势和缺点，但是，到目前为止，只有有限的尝试来弥合它们之间的差距。为了解决这个问题，本文介绍了一种基于模型预测控制（MPC）和神经网络（NNS）的反馈控制策略之间最坏情况误差的方法。该结果将其杠杆化为一种方法，以自动合成MPC策略，以最大程度地减少相对于NN的最坏情况误差。数值示例强调了边界的应用，本文的目的是鼓励对数据驱动和模型驱动控制之间的关系有更定量的理解。
There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches. Each of these two approaches has their own complimentary set of advantages and disadvantages, however, only limited attempts have, so far, been developed to bridge the gap between them. To address this issue, this paper introduces a method to bound the worst-case error between feedback control policies based upon model predictive control (MPC) and neural networks (NNs). This result is leveraged into an approach to automatically synthesize MPC policies minimising the worst-case error with respect to a NN. Numerical examples highlight the application of the bounds, with the goal of the paper being to encourage a more quantitative understanding of the relationship between data-driven and model-driven control.
