选对RBF神经网络激活函数，能显著提升机械臂轨迹跟踪的精度与平滑性。
Influence of Radial Basis Activation Functions on Intelligent Controller for Robotic Manipulators
- 用RBF网络在线估计干扰，结合非线性控制补偿不确定性和摩擦
- 不同激活函数影响动态响应和稳态精度，但均保证系统稳定
- 为智能控制设计提供可调结构参数，适合机械臂控制研究者参考
本文提出一种基于径向基函数（RBF）神经网络的智能控制框架，用于机器人机械臂的轨迹跟踪控制。该方法结合模型驱动的非线性控制与自适应神经逼近器，以补偿参数不确定性、摩擦及未建模动态。采用基于李雅普诺夫的自适应律并引入投影机制，确保闭环信号有界且跟踪误差收敛至紧集。研究重点考察RBF网络中激活函数的选择对瞬态行为、稳态精度和控制平滑性的影响。实验结果表明，尽管所有核函数均保持系统稳定，但激活函数的选择显著影响自适应动态和实际跟踪性能。研究证实，激活函数选择是智能控制中的结构性设计参数，直接决定自适应动态与闭环性能。
原文摘要 · Abstract (English)
This paper presents an intelligent control framework for trajectory tracking of robotic manipulators using radial basis function (RBF) neural networks for online disturbance estimation. The proposed control structure combines model-based nonlinear control with an adaptive neural approximator that compensates for parametric uncertainties, friction, and unmodeled dynamics. A Lyapunov-based adaptation law with projection guarantees boundedness of the closed-loop signals and convergence of the tracking error to a compact region. The primary objective of this work is to investigate how the choice of activation function within the RBF network influences transient behavior, steady-state accuracy, and control smoothness. The controller is implemented on a robotic manipulator. Experimental results demonstrate that although stability is preserved for all kernels, activation function selection significantly affects adaptation dynamics and practical tracking performance. These findings demonstrate that activation function selection acts as a structural design parameter in intelligent control, directly shaping adaptation dynamics and practical closed-loop performance.
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