<p>To address the challenges related to speed variation and load disturbances in the speed control systems of aviation electric lubricating oil pumps, as well as the need for fault tolerance in the event of sensor failures, this paper introduces an innovative super twisting complementary sliding mode speed controller. This method aims to achieve high-precision control over the speed of these pumps. Initially, we developed a mathematical model for the permanent-magnet synchronous motor and the gear lubricating oil pump, taking into account the specific operational characteristics of aviation electric lubricating oil pumps. Subsequently, we proposed a comprehensive fault-tolerant control strategy that integrates the super twisting algorithm with complementary sliding mode control to effectively tackle the issues associated with speed sensor failures. Subsequently, a novel super-twisting complementary sliding mode speed controller design method is introduced, aimed at enhancing the system’s stability during abrupt changes in load. This approach successfully addresses the system’s challenges in dynamic performance and resilience to load variations. Additionally, a super-twisting sliding mode observer utilizing radial basis function neural networks is developed, allowing for precise estimation of the motor’s rotor speed and position, particularly during speed sensor failures. This capability ensures the system’s reliable operation even in the event of sensor malfunctions. Moreover, the stability of the system is thoroughly established through Lyapunov theory. Both simulation and experimental findings indicate that the proposed control strategy offers notable improvements in terms of dynamic response time, steady-state accuracy, and overshoot management, thereby reinforcing the validity of the methodology presented in this study.</p>