<p>This paper focuses on the event-triggered adaptive fixed-time tracking control problem for an uncertain n-joint robotic manipulator under asymmetric time-varying full-state constraints. First, by constructing a unified barrier function (UBF) with constraint boundaries and introducing the system state through coordinate transformation, state constraints are directly addressed. This approach avoids the conservative process of indirectly converting state constraints into error constraints in traditional Lyapunov barrier methods. Second, fuzzy logic is employed to identify unknown uncertainty functions in the robotic arm. Additionally, dynamic surface control is adopted to eliminate the “complexity explosion” issue present in traditional backstepping methods. Furthermore, a dynamic event triggering mechanism (DETM) is designed, where dynamic signals adaptively adjust the triggering threshold, significantly reducing the triggering frequency and computational load. Utilizing Lyapunov stability theory, it is proven that the proposed control strategy guarantees the boundedness of all signals in the closed-loop system, and the tracking error convergence to an arbitrarily small region near the origin within a fixed-time, and this time is unaffected by the initial conditions. Moreover, all states comply with the time-varying constraints. Finally, simulation and experimental results confirm the validity and performance of the proposed tracking control strategy.</p>