In order to create an accurate and stable two-dimensional acceleration overload environment for the test pieces and improve the control precision of the turntable tracking test system, the vector turntable is modeled.The PID controller based on the RBF neural network optimized by the adaptive chaotic ant colony algorithm is used, to solve the problem that the optimization of the weight of the RBF neural network is very slow, to effectively shorten the learning process of the neural network, and to improve the online adaptive ability of the PID controller, thus to enable the turntable tracking test system to rapidly track the target.The simulation results show that the PID controller based on the RBF neural network optimized by the adaptive chaotic ant colony algorithm is superior to that based on the traditional RBF neural network, and has satisfying accuracy and rapidity.It has great engineering significance for the design of the turntable tracking test system.