A Novel ADP-Based Neurooptimal Control Methodology for Teleoperation Systems Under Interactive Shared-Control Framework

This paper delves a robust neurooptimal shared control tactic for teleoperation systems affected by model uncertainties and external disturbances. A novel shared-control structure is established with an explicitly defined and dynamically adjustable weights mechanism, enabling smooth authority transitions across control modes and consistent task execution. A neural near-optimal adaptive dynamic programming (ADP) framework firstly applied to uncertain teleoperation eschews the conventional dependence on precisely known dynamics to deliver near-optimal control. Moreover, a prescribed-time exponential disturbance observer (PPTEDO) featuring a rigorously designed continuous switching function, thereby overcomes the inherent instability and chattering pitfalls associated with discontinuous PT schemes. Under the impedance control layer, a reset regularized exponentiation sliding mode (RRESM) surface is designed to ensure fast transient-state response while elevating system transparency and improve human-robot interaction fluency. Additionally, the absolute stability of the closed-loop teleoperation system is rigorously proved via establishment of Lyapunov candidate function. Finally, experiment results are presented to demonstrate the practical availability of the designed algorithm.