Aiming at the shortcomings of the existing automatic control methods for two-wheel self-balancing structure robots, such as the contradiction between inclination control accuracy, robot balance and working efficiency, a robot control method based on the quadruple optimization fuzzy PID algorithm is designed. Firstly, a precise dynamic model is constructed based on the Lagrange equation to analyze the balanced posture of the robot in motion. Then, the complexity of the model is reduced through linear optimization. During the optimization process of the dynamic model, additional disturbance terms are introduced to implement dynamic compensation, further enhancing the performance of the model. An RBF neural network model was constructed and integrated with the adaptive fuzzy algorithm and PID control algorithm to enhance the data training ability and optimal result output ability of the PID controller. Finally, an adaptive adjustment mechanism was adopted in the control of the three output nodes of the neural network output layer, and reasonable control results were obtained through the optimal control function. The experimental results show that under the proposed quadruple optimization fusion scheme, there is no situation where the inclination Angle exceeds 25°. Moreover, in the three cases of no obstacles, with obstacles, and with slopes, the proposed method all involves collisions with obstacles. In the cases of slopes and obstacles, the fluctuation value of the standard deviation of the sampling site is only 0.852.