The adaptive fixed-time consensus problem for a class of nonlinear multi-agent systems (MASs) with actuator faults is considered in this chapter. To approximate the unknown nonlinear functions in MASs, radial basis function neural networks are used. In addition, the first-order sliding mode differentiator is utilized to solve the “explosion of complexity” problem, and a filter error compensation method is proposed to ensure the convergence of filter error in fixed time. With the help of the Nussbaum function, the actuator failure compensation mechanism is constructed. By designing the adaptive fixed-time controller, all signals in MASs are bounded, and the consensus errors between the leader and all followers converge to a small area of origin. Finally, the effectiveness of the proposed control method is verified by simulation examples.