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The paper proposes a command filtering adaptive output feedback control scheme with preset performance for a Robotic Arm Model (RAM) designed specifically for steel structures. Initially, a neural network observer is employed to approximate the nonlinear functions within the model and to estimate the unmeasured states of the system. Subsequently, within the backstepping framework, the integration of preset performance theory and command filtering technology addresses the differential complexity challenges commonly encountered in traditional backstepping methods. This approach ensures rapid convergence of the system’s tracking error within predetermined boundaries. The efficacy of this strategy is demonstrated through simulation instances.