从数据中学习非线性系统的神经控制器
Learning neural controllers for nonlinear systems from data
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中文总结 AI 辅助
针对未知非线性系统，提出一种结合离线数据辨识、硬饱和输入约束、SMT与李雅普诺夫分析的间接数据驱动神经控制器合成框架，经数值例子验证有效。
中文摘要 AI 辅助
本文解决了为未知非线性系统设计神经反馈控制器的问题。我们提出一种间接数据驱动框架，利用离线数据辨识系统动力学，在此基础上联合合成神经反馈控制器与神经李雅普诺夫函数。通过在控制器架构中集成硬饱和结构来施加输入约束，推导了鲁棒合成条件以应对辨识过程中的数据扰动，结合SMT验证与平衡点附近的局部李雅普诺夫分析证明了形式稳定性，数值例子验证了该框架的有效性。
英文摘要
This article addresses the problem of designing neural feedback controllers for unknown nonlinear systems. We propose an indirect data-driven framework that uses offline data to identify the system dynamics, upon which a neural feedback controller and a neural Lyapunov function are jointly synthesized. Input constraints are enforced by integrating a hard-saturation structure into the controller architecture. Robust synthesis conditions are derived to account for data perturbations during identification. Formal stability is certified by combining SMT verification with local Lyapunov analysis near the equilibrium. Numerical examples validate the effectiveness of the proposed framework.
发表机构
- Huazhong University of Science and Technology（华中科技大学）
机构由 AI 辅助整理，请以论文原文为准。
