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This paper investigates bilateral teleoperation under time-varying communication delays and dynamic uncertainties. A role-specific asymmetric master–slave control architecture is proposed, in which impedance control is assigned to the master side for interaction-dynamics shaping and force feedback, while adaptive sliding mode control (SMC) is employed at the slave side for robust position tracking. A radial basis function neural network (RBFNN) is integrated into the slave-side SMC to compensate for uncertain nonlinear dynamics without requiring exact identification of the slave model. The adaptive laws are derived using Lyapunov stability analysis, and the approximation and adaptive estimation errors are explicitly considered. Under the stated boundedness conditions, the adaptive estimation errors and sliding variable remain bounded, and the slave position tracking error asymptotically converges to zero. The simulation results show that the proposed method substantially reduces the sensitivity of tracking performance to model uncertainty, with the IAPE remaining between 0.0031 and 0.0035 under 20–40% mismatch, compared with 0.2029–0.4057 for conventional SMC. Additional simulations with time-varying communication delays demonstrate bounded tracking performance, although delay-induced phase shifts and transient oscillations remain. The results demonstrate the robustness of the proposed role-specific architecture under the considered uncertainty and communication conditions, while higher-DOF and experimental validation remain subjects for future work.
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Tong et al. (2026) studied this question.
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