Lyapunov-Stable Neural Network Control for Position and Force Coordination in Delayed Bilateral Teleoperation Systems
Journal
IEEE Open Journal of the Industrial Electronics Society
Date Issued
2026
Author(s)
Slawiñski, Emanuel
Rossomando, Francisco G.
Mut, Vicente
Moreno-Valenzuela, Javier
Type
Article
Abstract
Stability and transparency are intrinsically coupled and must be addressed simultaneously in delayed bilateral teleoperation systems, where there is a fundamental tradeoff between ensuring closed-loop stability and achieving accurate force–position coordination. This challenge becomes particularly critical when interacting with remote environments under time-varying communication delays, since the human operator introduces nonlinear, time-varying, and often unpredictable dynamics into the closed-loop system. This article presents an adaptive neural-network (NN)-based compensation strategy embedded within a model-based control framework to enhance dual coordination in bilateral teleoperation. By combining classical control design with online NN adaptation, the proposed controller compensates for parametric uncertainties, unmodeled dynamics, interaction forces, and communication delays without requiring explicit models of the human operator or the remote environment. The adaptive structure enables real-time learning of unknown nonlinearities while preserving the stability guarantees provided by the underlying control architecture. A theoretical analysis of the closed-loop teleoperation system is developed, demonstrating stability in the presence of human-applied forces, environment interaction forces, and time-varying communication delays. Numerical simulations conducted on two-degrees-of-freedom manipulators validate the feasibility and practical viability of the proposed approach. Different explicit models of the human operator are considered in simulation to assess the sensitivity to operator dynamics. The results show that a bounded dual coordination of force and position is achieved under delayed communication regardless of the assumed operator model, supporting the potential application of the method in real-world teleoperation scenarios.