论文标题
数据驱动的非线性预测控制可线化系统
Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
论文作者
论文摘要
我们提供了一个数据驱动的非线性预测控制方法，用于离散的多输入多输出反馈可线化的非线性系统。该方案仅基于输入和嘈杂的输出数据以及一组近似未知非线性的基础函数，使用非参数预测模型。尽管嘈杂的输出数据以及使用基本函数引起的不匹配，但我们表明，提出的多步稳定数据驱动的非线性预测控制方案是可行的，并使闭环系统实际上是指数级稳定的。我们在完全呈现的双重倒摆模型上说明了我们的结果。
We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multistep robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.
