Influence of Radial Basis Activation Functions on Intelligent Controller for Robotic Manipulators
For robotic control engineers, this work provides practical guidance on selecting activation functions in neural network-based controllers, though the findings are incremental.
This paper investigates how the choice of radial basis function activation affects the performance of an intelligent controller for robotic manipulators, finding that activation function selection significantly influences adaptation dynamics and tracking accuracy.
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.