Switch reluctance motor is suitable for an electric vehicle driving system, which has the advantages of simple structure, high reliability of system and wide range of speed adjustment. In order to reduce the torque ripple of the system and improve the dynamic performance, a strategy of direct instantaneous torque control (DITC) based on a fractional‐order proportion integration differentiation (PID) controller is proposed. According to the mathematical characteristics of the fractional‐order controller, the form of the fractional‐order speed loop controller is determined. Also, the parameters of the speed regulator are designed using the frequency‐domain design theory of the control system. Then, the fractional‐order controller is discretised. Compared with the traditional proportion integration (PI) controller, the fractional‐order controller can have a better control effect. Simulation experimental results of the system show that the DITC control method has effectively reduced the torque ripple and the fractional‐integral controller has reduced the overshoot and adjustment time and improved the robustness and disturbance resistance of the system.

In this work, we aimed to determine the nonlinear disturbance caused by cascaded coupling rigid–flexible deformation and friction in a direct-driven servo electric cylinder terminal positioning system (DDSEC-TPS) during feed motion of an intermittent, reciprocating, and time-varying load. For this purpose, a cascaded coupling dynamic error model of DDSEC-TPS was established based on the position–pose error model of the parallel motion platform and the rotor field-oriented vector transform. Then, a model to observe the dynamic error of the DDSEC-TPS was established using the improved beetle antennae search algorithm backpropagation neural network (IBAS-BPNN) prediction model according to the rigid–flexible deformation error theory of feed motion, and the observed dynamic error was compensated for in the vector control strategy of the DDSEC-TPS. The length and error prediction models were trained and validated using opposite and mixed datasets tested on the experimental platform, to observe dynamic errors and evaluate and optimize the prediction models. The experimental results show that dynamic error compensation can improve the position tracking accuracy of the DDSEC-TPS and the position–pose performance of the parallel motion platform. This study is of great significance for improving the consistency of following multiple DDSEC-TPSs and the position–pose accuracy of parallel motion platforms.