The launch vehicle is prone to power system faults such as thrust drop in the ascending stage, and the traditional control method is difficult to meet the requirements of high precision and high stability control in the case of large interference. In this research, the adaptive augmentation control and fuzzy PD control of launch vehicle were studied, and their control characteristics were analyzed. A dynamic switching control algorithm based on fuzzy rules was proposed, and by calculating the output weight of adaptive PD control and fuzzy PD control, the optimal control scheme of the system was obtained. The control torque reconstruction and the dynamic switching control algorithm were combined to carry out fault-tolerant control research. The results show that the dynamic switching control algorithm based on fuzzy rules can effectively improve the attitude control quality. Compared with other control methods, the maximum yaw angle deviation is reduced by more than 20%, the system response time is at least 15% faster, and there is no oscillation in the control process.

Aiming at the problem of complex structure and high fault occurrence in the attitude control system of launch vehicle, a multiple model fault detection and isolation algorithm was proposed. The small deviation attitude dynamics model of the launch vehicle was established, and the Kalman filter of the system was designed. Combined with the idea of special observer, multiple Kalman filter banks with different structures were used to generate corresponding residuals, so that a single residual was only sensitive to a fault of the sensor or actuator. The fault isolation strategy was deduced theoretically to achieve the detection and isolation of different fault types of the launch vehicle. Simulation analysis shows that when no fault occurs, the residual results do not exceed the set threshold, and the algorithm does not alarm; when the sensor or actuator fails, the proposed isolation strategy can accurately locate the fault, which verifies the effectiveness of the algorithm.