论文标题
数据驱动的内部模型控制二阶离散伏尔泰系统
Data-Driven Internal Model Control of Second-Order Discrete Volterra Systems
论文作者
论文摘要
系统复杂性的提高与越来越多的运营数据相结合的促进了传统控制设计范式的变化。数据驱动的控制范式不是通过第一原理对系统进行建模，然后使用（基于模型的）控制设计进行建模，而是建议直接从数据中表征控制器。通过利用Willem和合作者的基本结果，该方法已成功应用于线性系统，为许多经典的线性控制器提供了基于数据的公式。在本文中，数据驱动的方法扩展到一类非线性系统，即二阶离散伏尔泰拉系统。为这类系统做出了两个主要贡献。首先，我们表明 - 在输入数据激发的必要条件下 - 可以从输入输出数据派生基于数据的系统表示，并用于替换显式系统模型。也就是说，Willems等人的基本结果。扩展到这类系统。随后得出了用于输出跟踪的数据驱动的内部模型控制公式。通过两个仿真示例说明了该方法。
The increase in system complexity paired with a growing availability of operational data has motivated a change in the traditional control design paradigm. Instead of modeling the system by first principles and then proceeding with a (model-based) control design, the data-driven control paradigm proposes to directly characterize the controller from data. By exploiting a fundamental result of Willems and collaborators, this approach has been successfully applied to linear systems, yielding data-based formulas for many classical linear controllers. In the present paper, the data-driven approach is extended to a class of nonlinear systems, namely second-order discrete Volterra systems. Two main contributions are made for this class of systems. At first, we show that - under a necessary and sufficient condition on the input data excitation - a data-based system representation can be derived from input-output data and used to replace an explicit system model. That is, the fundamental result of Willems et al. is extended to this class of systems. Subsequently a data-driven internal model control formula for output-tracking is derived. The approach is illustrated via two simulation examples.
