Dynamic event-triggered adaptive fuzzy admittance control of robotic systems with uncertainties

In modern industrial production and daily life, the increasing complexity of application scenarios has led to higher requirements for the compliance and safety of robotic systems. However, during robot control, a large number of redundant signals are often sampled, which significantly increases the communication burden. Therefore, how to substantially reduce the communication burden while ensuring satisfactory performance has become an urgent issue to be addressed. To address this challenge, this paper proposes a dynamic event-triggered adaptive fuzzy admittance control (DETAFAC) strategy for robotic systems with uncertainties, where the more aggressive dynamic event-triggered condition can significantly reduce the communication burden and the admittance model is used to reshape the desired trajectory of the robotic systems. Additionally, a fuzzy logic system (FLS) is utilized to address the uncertainties of the robotic systems, the update law of the FLS and the stability of the control system are examined using the Lyapunov stability theorem, and the dynamic triggering condition is formulated to prevent Zeno behavior. Simulation and experimental validations are performed, and the results demonstrate that the proposed DETAFAC strategy can achieve better performances in comparison to the similar approaches.