无需扰动上界信息，实现足式机器人扰动估计的预设收敛。
Disturbance Estimation of Legged Robots: Predefined Convergence via Dynamic Gains
- 基于可测变量设计动态增益反馈观测器
- 误差收敛可预设为有界、渐近或指数型
- 适合工程部署，无需扰动导数上界
本文针对足式机器人扰动估计难题，提出一种新型连续时间在线反馈观测器，利用可测变量实现扰动估计。其核心在于引入动态增益与比较函数，确保扰动估计误差具有预设收敛性，包括最终一致有界、渐近和指数收敛等类型。文中详细分析了动态增益特性及比较函数的充分条件，为工程师设计所需收敛行为提供指导。值得注意的是，该观测器在无需扰动及其导数上界信息的情况下仍能有效工作，显著提升工程适用性。实验验证了理论成果的有效性。
原文摘要 · Abstract (English)
In this study, we address the challenge of disturbance estimation in legged robots by introducing a novel continuous-time online feedback-based disturbance observer that leverages measurable variables. The distinct feature of our observer is the integration of dynamic gains and comparison functions, which guarantees predefined convergence of the disturbance estimation error, including ultimately uniformly bounded, asymptotic, and exponential convergence, among various types. The properties of dynamic gains and the sufficient conditions for comparison functions are detailed to guide engineers in designing desired convergence behaviors. Notably, the observer functions effectively without the need for upper bound information of the disturbance or its derivative, enhancing its engineering applicability. An experimental example corroborates the theoretical advancements achieved.
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