As the brain of manipulators, the control system is the core part which directly affects the overall performance indicators of manipulators, such as control precision, robustness and the ability of autonomous decision-making, etc. From the perspective of cybernetics, multi-joint series manipulator is a complicated multiple-input multiple-output (MIMO) system which is characterized by nonlinearity, time variation, strong coupling and uncertainty. As a result, traditional methods have poor control efficiency on multi-joint manipulator. To solve the above problems, an adaptive fuzzy backstepping controller is designed in this paper. First, the complex nonlinear system is decomposed into several subsystems with inversion method, and the virtual control for each of them is adopted until the whole design of the control law is completed. Then, the model information of manipulator is approximated with adaptive fuzzy system, which realizes the model-free control and reduces the uncertain influence of external disturbance. Finally, the controller can make the manipulator to track a predetermined trajectory. What's more, the adaptive fuzzy backstepping control algorithm and the simple backstepping control algorithm are simulated by MATLAB SIMULINK, respectively. The results show that the adaptive fuzzy backstepping control algorithm greatly reduces the trajectory tracking error and achieves high precision control of the manipulator. © 2019 Association for Computing Machinery.