混合扰动下有限时域鲁棒性分析：基于信号IQC方法
Finite-Horizon Robustness Analysis under Mixed Disturbances using Signal-IQCs
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中文总结 AI 辅助
本文提出一种基于信号IQC的有限时域最坏情况分析方法，同时处理L2有界最坏信号与部分已知扰动，通过耗散不等式降低保守性，并在无人机案例中验证有效性。
中文摘要 AI 辅助
针对不确定有限时域系统的常见最坏情况分析，通常基于严格的Bounded Real Lemma考虑二次性能指标。因此，它们评估有界输入（例如L2中的信号）下的系统性能，这些输入呈现最坏情况形状。由此，已知的扰动特征未被利用和揭示，导致结果过于保守。本文开发了一种最坏情况分析，同时涵盖任意L2有界的最坏情况信号和部分已知扰动。这是通过使用信号积分二次约束（IQCs）对后者进行建模来实现的。所得分析条件依赖于有限时间范围内IQC框架内的耗散不等式。该框架还便于在分析中纳入额外的系统不确定性。通过一个小型无人机在城市环境中的最坏情况性能分析，验证了该方法的可行性。
英文摘要
Common worst-case analyses for uncertain finite-horizon systems consider quadratic performance metrics based on the strict Bounded Real Lemma. Thus, they assess system performance for bounded inputs, e.g., signals in L2, which exhibit a worst-case shape. Consequently, known disturbance characteristics are left unexploited and uncovered, leading to unnecessarily conservative results. The present paper develops a worst-case analysis covering arbitrarily L2-bounded worst-case signals and partially known disturbances simultaneously. This is achieved by modeling the latter using signal integral-quadratic constraints (IQCs). The resulting analysis condition relies on a dissipation inequality within the IQC framework for finite time horizon problems. This framework also readily allows to incorporate additional system uncertainties in the analysis. The approach's feasibility is demonstrated with the worst-case performance analysis of a small unmanned aerial vehicle in an urban environment.
发表机构
- Technische Universität Dresden（德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。
