Abstract
In this work, we pay attention to investigating fixed-time trajectory tracking with prescribed performance for a multi-degree-of-freedom manipulator system subjected to unknown dynamics and input saturation. The radial basis function neural network (RBFNN) is applied to online compensate for the unknown dynamics of the system. In order to guarantee the transient and steady-state performance of the trajectory tracking control, a prescribed performance function (PPF) is used to transform the tracking error. Based on the transformed error, a fixed-time auxiliary system is proposed to compensate for the input saturation impact. Using the compensation error, a non-singular terminal sliding surface is designed, and the corresponding fixed-time control scheme is also proposed. By Lyapunov theorem, it is proved that the reaching phase of the sliding manifold can be completed in finite time, and the stability of the closed-loop system is analyzed. Experimental results verify the effectiveness of the proposed method.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (62022030, 62033005 and 62103118), the Fundamental Research Funds for the Central Universities (HIT.OCEF.2021005), the Heilongjiang Provincial Natural Science Foundation of China (ZD2021F001), the Self-Planned Task (NO. SKLRS202215B) of State Key Laboratory of Robotics and System (HIT) and the China Postdoctoral Science Foundation under Grant 2021M700037 and Grant 2021T140160.
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Sun, Y., Kuang, J., Gao, Y. et al. Fixed-time prescribed performance tracking control for manipulators against input saturation. Nonlinear Dyn 111, 14077–14095 (2023). https://doi.org/10.1007/s11071-023-08499-3
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DOI: https://doi.org/10.1007/s11071-023-08499-3