论文标题
非线性模型预测控制中可行区域的数据驱动近似
Data-driven approximation for feasible regions in nonlinear model predictive control
论文作者
论文摘要
本文开发了一个数据驱动的学习框架，用于在非线性模型预测控制（MPC）方案下近似非线性系统的可行区域和不变集。开发的方法是基于使用低分配序列的点数据集的可行性信息。使用基于内核的支持向量机（SVM）学习，我们构建了可行区域边界的外部和内部近似，然后为系统获得MPC的可行区域。此外，我们使用SET理论方法将方法扩展到了扰动的非线性系统。最后，提供了一个说明性的数值示例，以显示所提出的方法的有效性。
This paper develops a data-driven learning framework for approximating the feasible region and invariant set of a nonlinear system under the nonlinear Model Predictive Control (MPC) scheme. The developed approach is based on the feasibility information of a point-wise data set using low-discrepancy sequence. Using kernel-based Support Vector Machine (SVM) learning, we construct outer and inner approximations of the boundary of the feasible region and then, obtain the feasible region of MPC for the system. Furthermore, we extend our approach to the perturbed nonlinear systems using set-theoretic method. Finally, an illustrative numerical example is provided to show the effectiveness of the proposed approach.
