This article addresses the problem of fast fixed-time tracking control for robotic manipulator systems subject to model uncertainties and disturbances. First, on the basis of a newly constructed fixed-time stable system, a novel faster nonsingular fixed-time sliding mode (FNFTSM) surface is developed to ensure a faster convergence rate, and the settling time of the proposed surface is independent of initial values of system states. Subsequently, an extreme learning machine (ELM) algorithm is utilized to suppress the negative influence of system uncertainties and disturbances. By incorporating fixed-time stable theory and the ELM learning technique, an adaptive fixed-time sliding mode control scheme without knowing any information of system parameters is synthesized, which can circumvent chattering phenomenon and ensure that the tracking errors converge to a small region in fixed time. Finally, the superior of the proposed control strategy is substantiated with comparison simulation results.

中文翻译：

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基于 ELM 的机器人机械手系统自适应更快固定时间控制

本文解决了受模型不确定性和干扰影响的机器人操纵器系统的快速固定时间跟踪控制问题。首先，在新构建的固定时间稳定系统的基础上，开发了一种新型更快的非奇异固定时间滑模（FNFTSM）表面，以确保更快的收敛速度，并且所提出的表面的稳定时间与初始值无关系统状态。随后，利用极限学习机（ELM）算法来抑制系统不确定性和干扰的负面影响。结合固定时间稳定理论和ELM学习技术，合成了一种在不知道任何系统参数信息的情况下的自适应固定时间滑模控制方案，可以避免抖振现象，并确保跟踪误差在固定时间内收敛到一个小区域。时间。最后，通过比较仿真结果证实了所提出的控制策略的优越性。