<p>How to accomplish online remote control of robot manipulators (RMs) with constrained wireless network capacity has become a critical application direction in diverse domains such as medical surgery and space exploration, especially for the tracking control. This paper aims to develop a radial-basis-function-neural-network (RBFNN)-based online remote event-triggered adaptive sliding mode controller to address the tracking control problem of RMs with external disturbances. First, an uncertain nonlinear system is used to capture dynamics of RMs, and a tracking error system is formulated, combined with the desired state trajectory. Second, the RBFNN-based online remote dynamic event-triggered adaptive sliding mode controller is proposed by integrating the adaptive control method, the corrective control method, and the RBFNN control methodology, where the adaptive control is to compensate for the uncertainties, including the time-varying approximation error caused by the neural network approximation technique and the system uncertainty from the model uncertainty. Then, the control gain and the learning law of RBFNN are designed based on the Lyapunov theory to guarantee the asymptotic stability of tracking errors. A simulation study is presented to verify the derived control gain and learning law.</p>