论文标题
使用基于模型的先验的数据驱动的显式预测控制器设计
Data-driven design of explicit predictive controllers using model-based priors
论文作者
论文摘要
在本文中，我们提出了一种数据驱动的方法来得出明确的预测控制定律，而无需任何中间标识步骤。提出的策略的基石是从基于模型的分析中对控制法的可用先验开发。具体而言，通过利用最佳预测控制器表示为分段仿射（PWA）定律的知识，我们直接从数据中优化了此类分析控制器的参数，而不是运行在线优化问题。由于提出的方法允许我们自动检索闭环系统的模型，因此我们表明我们可以在控制器部署之前应用基于模型的技术来执行稳定性检查。在两个基准模拟示例中评估了拟议策略的有效性，我们还通过该策略讨论了正则化及其与平均技术的使用以处理噪声的存在。
In this paper, we propose a data-driven approach to derive explicit predictive control laws, without requiring any intermediate identification step. The keystone of the presented strategy is the exploitation of available priors on the control law, coming from model-based analysis. Specifically, by leveraging on the knowledge that the optimal predictive controller is expressed as a piecewise affine (PWA) law, we directly optimize the parameters of such an analytical controller from data, instead of running an on-line optimization problem. As the proposed method allows us to automatically retrieve also a model of the closed-loop system, we show that we can apply model-based techniques to perform a stability check prior to the controller deployment. The effectiveness of the proposed strategy is assessed on two benchmark simulation examples, through which we also discuss the use of regularization and its combination with averaging techniques to handle the presence of noise.
