Disturbance Estimation of Legged Robots: Predefined Convergence via Dynamic Gains
🤖 AI Summary
This paper addresses the online estimation of unknown external disturbances during legged robot locomotion. We propose a continuous-time nonlinear disturbance observer relying solely on measurable states, without requiring prior knowledge of disturbance bounds or their derivatives. By co-designing dynamic gains and comparison functions, our approach unifies three predefined convergence modes—uniform ultimate boundedness, asymptotic convergence, and exponential convergence—within a single framework, enabling engineering-configurable mode selection. Rigorous Lyapunov-based stability analysis guarantees multi-mode convergence properties. The observer is experimentally validated on a physical legged robot platform, demonstrating high-accuracy and robust disturbance estimation under realistic operating conditions. The key innovation lies in eliminating the conventional dependency on disturbance boundary information, thereby reconciling theoretical rigor with practical deployability and flexibility.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Develops a disturbance observer for legged robots.
Ensures predefined convergence of estimation error.
Operates without disturbance upper bound information.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Continuous-time online feedback-based disturbance observer
Dynamic gains and comparison functions integration
No need for disturbance upper bound information
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