基于算子分解的上下文增强性能提升

中文摘要 AI 辅助

性能提升（PB）是一种控制框架，针对受$\boldsymbol{\textit{L}}_p$过程扰动的预稳定系统，通过因果$\boldsymbol{\textit{L}}_p$稳定算子映射重构扰动到校正控制作用，参数化保持闭环$\boldsymbol{\textit{L}}_p$稳定的控制器。尽管这允许对表达性稳定保持控制器进行优化，但仅从扰动信息中学习期望策略可能较为困难。我们为上下文增强、多输入PB算子引入结构化分解，该架构结合处理重构扰动的$\boldsymbol{\textit{L}}_p$稳定动力学模块，以及依赖于扰动和上下文信号的一致有界矩阵值混合器。在标准PB假设下，该分解通过构造保持闭环$\boldsymbol{\textit{L}}_p$稳定；在加权包络扰动域上，我们证明该分解是满足上下文一致包络保持属性的因果算子的充要条件。数值移动门导航实验表明，该架构优于上下文无关PB、MAD和参考感知PB基线。

英文摘要

Performance Boosting (PB) is a control framework that, for a pre-stabilized system subject to $\mathcal L_p$ process disturbances, parametrizes the controllers that preserve closed-loop $\mathcal L_p$-stability through a causal $\mathcal L_p$-stable operator mapping reconstructed disturbances to corrective control actions. Although this permits optimization over expressive stability-preserving controllers, learning a desired policy from disturbance information alone can be difficult. We introduce a structured factorization for context-enriched, multi-input PB operators. The proposed architecture combines an $\mathcal L_p$-stable dynamical module that processes reconstructed disturbances with a uniformly bounded matrix-valued mixer depending on disturbances and contextual signals. Under the standard PB assumptions, this factorization preserves closed-loop $\mathcal L_p$-stability by construction. Moreover, on a weighted-envelope disturbance domain, we prove that the factorization is necessary and sufficient for causal operators satisfying a context-uniform envelope-preservation property. A numerical moving-gate navigation experiment demonstrates the advantages of the proposed architecture over context-agnostic PB, MAD, and reference-aware PB baselines.

发表机构

• Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
• Politecnico di Torino（都灵理工大学）

机构由 AI 辅助整理，请以论文原文为准。