Finite-time composite learning control for nonlinear teleoperation systems under networked time-varying delays

The robust finite-time synchronization control problem is investigated for master-slave networked nonlinear telerobotics systems (NNTSs) in this article. Although there have been some research achievements on finite-time control for the NNTSs, these studies are based on the strong assumptions of communication time delays or can only achieve finite-time bounded convergence even when the external forces are zero. Accordingly and in view of the importance of these issues, a novel robust composite learning adaptive control scheme rendering the finite-time master-slave synchronization is proposed in this paper. In particular, the influence of time delays on finite-time convergence of the system is analyzed by employing the multi-dimension finite-time small-gain framework. Meanwhile, in order to achieve accurate and fast estimation of uncertain parameters of the system, both the online historical and the instantaneous data of the estimation data are explored to derive the new parameter adaptive law under a more realizable interval-excitation (IE) condition. Therefore, the convergence of the position/force synchronization errors and the adaptive parameter estimation errors is obtained in finite time, and enhanced robustness of the closed-loop system will also be ensured. Finally, the superior performance of the proposed control algorithms is validated by numerical simulations and hardware experiments.