To solve the instability problem caused by external uncertain friction, interference, and load changes in the sliding mode control system of an n-link manipulator, this paper proposes a novel adaptive fuzzy random vector function link (FRVFL)-inherited sliding mode control scheme for tracking the position of robot manipulators. The random vector function link (RVFL) network is fuzzified to improve its generalization ability. On fuzzification, the RVFL network can achieve self-mapping between fuzzy rules and hidden layers, thereby reducing the complexity of the algorithm. The proposed algorithm uses adaptive rules to achieve self-adjustment for output weights. Thus, precise approximation of the unknown nonlinear dynamics in the control system can be realized. A robust control term is added to the controller to further weaken the sliding mode chattering and improve the tracking effect of a two-link manipulator. The closed-loop stability of the proposed algorithm is analyzed using the Lyapunov theorem. Simulation results on the control of the two-link manipulator prove the effectiveness of the proposed algorithm.

为解决n连杆机械手的滑模控制系统中外部不确定的摩擦，干扰和载荷变化引起的不稳定性问题，提出了一种新颖的自适应模糊随机矢量函数链（FRVFL）继承的滑模控制方案。跟踪机器人操纵器的位置。模糊化随机向量函数链接（RVFL）网络以提高其泛化能力。在模糊化方面，RVFL网络可以实现模糊规则和隐藏层之间的自映射，从而降低了算法的复杂性。所提出的算法使用自适应规则来实现输出权重的自我调整。因此，可以实现控制系统中未知非线性动力学的精确近似。鲁棒的控制项被添加到控制器，以进一步减弱滑模颤动并改善两连杆机械手的跟踪效果。利用李雅普诺夫定理分析了所提算法的闭环稳定性。二连杆机械手控制的仿真结果证明了该算法的有效性。