In the field of nonlinear system control, model uncertainties, external disturbances, and limited communication efficiency pose major challenges to achieving stabilization. Although traditional continuous-time control strategies can ensure system performance, they often impose unnecessary computational and communication burdens in practical applications. To address these issues, this study introduces an innovative integration of an event-triggered mechanism (ETM) into the adaptive backstepping control (ABC) framework. In the designed control system \({\text{ETM}} \to {\text{ABC}}\) , communication and controller updates are activated only when predefined triggering conditions are met. This approach significantly reduces the communication rate. Moreover, a robust adaptive law is developed to perform real-time online parameter estimation, effectively compensating for the adverse effects caused by model uncertainties and external disturbances, thereby enhancing the overall robustness of the control system. Simulation results demonstrate that, while ensuring trajectory tracking performance and closed-loop stability, the proposed method achieves a 68% reduction in communication data transmission. Compared with benchmark methods, the root mean square (RMS) of the trajectory tracking error is reduced by 25%. In addition, significant improvements are observed in both the convergence speed and the accuracy of parameter estimation. These results verify that the proposed control strategy effectively balances communication efficiency and control performance, offering considerable theoretical value for practical robotic applications.
