用神经网络建模非线性系统，自动获得可反馈线性化的近似模型。
Identification For Control Based on Neural Networks: Approximately Linearizable Models
- 神经网络拟合离散时间非线性状态空间模型
- 识别出的模型可通过反馈近似线性化
- 适合需稳定控制设计的非线性系统研究
本文提出一种面向控制的系统辨识方法，用于高效设计非线性系统的控制器并分析其稳定性。采用神经网络识别离散时间非线性状态空间模型，以逼近非线性系统的时域输入输出行为。网络结构被设计为使所识别模型可通过反馈实现近似线性化，从而在学习阶段即可直接导出控制律。完成辨识与准线性化后，可直接应用线性控制理论设计鲁棒控制器，并研究闭环系统的稳定性。该方法在多个经典系统辨识基准测试中展示了有效性和实用性。
原文摘要 · Abstract (English)
This work presents a control-oriented identification scheme for efficient control design and stability analysis of nonlinear systems. Neural networks are used to identify a discrete-time nonlinear state-space model to approximate time-domain input-output behavior of a nonlinear system. The network is constructed such that the identified model is approximately linearizable by feedback, ensuring that the control law trivially follows from the learning stage. After the identification and quasi-linearization procedures, linear control theory comes at hand to design robust controllers and study stability of the closed-loop system. The effectiveness and interest of the methodology are illustrated throughout the paper on popular benchmarks for system identification.
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