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This paper introduces a time-delay-estimation-based adaptive fractional-order nonsingular terminal sliding mode control strategy for cable-driven robots, addressing the challenges posed by their inherent nonlinearities and low stiffness. By incorporating a novel adaptive exponential term into the update gain, the proposed method enhances adaptive regulation and reduces noise-induced chattering during trajectory tracking. Experimental results demonstrate significant improvements in performance metrics, with reductions in RMSE and ITAE by over 30% compared to baseline methods, confirming the robustness and effectiveness of the approach under varying operational conditions.
A new adaptive control law reduces tracking error chattering by over 30% in cable-driven robots, enhancing performance in dynamic environments.
Cable-driven manipulators exhibit strong nonlinearities and low structural stiffness, which make precise control challenging under time-varying uncertainties and external disturbances. This paper presents a time-delay-estimation (TDE)-based adaptive fractional-order nonsingular terminal sliding mode (AFONTSM) control strategy for cable-driven robots. A robust controller is constructed within a TDE-based model-free framework by combining fractional-order nonsingular terminal sliding mode error dynamics with a fast terminal sliding mode reaching law. The main contribution is a new adaptive law that introduces an adaptive exponential term into the update gain to form a nonlinear adaptive mechanism. This design improves adaptive regulation under different operating conditions by suppressing noise-induced chattering during smooth tracking while preserving or enhancing the adaptive gain during trajectory reversal. Lyapunov analysis proves the ultimate uniform boundedness of the tracking error. Experimental results show that, compared with the baseline method, the proposed controller reduces RMSE by 34.52% and 31.11%, ITAE by 33.79% and 32.97%, and ISCT by 6.69% and 17.77% for the two joints, respectively. Further comparisons with recently reported adaptive laws demonstrate that the proposed law provides faster adaptive response, more stable gain evolution, and improved chattering suppression. Additional payload tests further verify the robustness and repeatability of the proposed method.
