Fixed‐Time Periodic Adaptive Event‐Triggered Control for Robotic Manipulator

ABSTRACT In this article, a fixed‐time periodic adaptive event‐triggered control for a robotic manipulator is proposed. Combined with the backstepping framework and finite‐time control theory, an adaptive fixed‐time controller is constructed to achieve the fast convergence performance of the robotic manipulator while maintaining system stability. Besides, a radial basis function neural network (RBFNN) is employed to approximate the uncertainties of the system. Further, Practical robotic applications frequently encounter critical challenges related to limited communication resources, especially during the execution of complex tasks. In contrast to the traditional event‐triggered control (ETC) mechanisms that require continuous system monitoring, a novel periodic adaptive event‐triggered control (PAETC) is proposed to schedule control signal transmission. The innovative PAETC strategy not only preserves system performance but also substantially reduces both communication burdens and continuous monitoring requirements, thereby enhancing practical implementability. Finally, both theoretical analysis and simulations are conducted to demonstrate the validity of the developed method.