研究人员开发了一个新的框架，用于创建简化的控制仿射降阶模型（ROM）。该方法采用自编码器将高维系统状态和输入映射到低维潜在空间。自编码器和状态空间模型同时进行训练，并扩展到基于序列的建模，以提高预测精度，同时保持控制仿射结构。通过反馈线性化证明了该框架的有效性，并在数值示例上进行了评估，将其预测和控制性能与基线进行了比较。 AI
影响 这项研究提供了一种新颖的模型降阶方法，有望提高复杂系统控制系统设计和仿真的效率。
排序理由 该集群包含一篇详细介绍创建降阶模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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arXiv:2606.05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensio…
We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for …
We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for …