The manipulator, due to the presence of unfavorable factors such as joint coupling, unknown disturbances, parameter ingestion and variable load tasks when performing complex tasks, greatly affects the control performance of the control system such as fast convergence, high precision tracking and robust robustness. Therefore, this paper proposes a self-tuning control method for manipulator based on fuzzy linear active disturbance rejection control (Fuzzy-LADRC) and particle swarm optimization (PSO) algorithm. Firstly, the PSO algorithm is the search algorithm for the optimal parameters of the controller, which ensures the reasonableness of the controller parameters. Secondly, the LADRC controller is utilized to assess and offset the unknown interference in the manipulator control system, which improves the manipulator control system’s anti-jamming capability. Then, the parameters of the linear error feedback control rate (LSEF) are dynamically adjusted using fuzzy control to improve the control accuracy and robustness of the control system. Finally, the effectiveness of the proposed method is proved by simulation experiments to improve the control accuracy and robustness of the manipulator control system.