基于滑模神经网络直接瞬时转矩控制的优化

针对开关磁阻电机在滞环控制策略中存在的较大转矩脉动问题，在传统直接瞬时转矩控制的基础上引入了反向传播(BP)神经网络控制策略，以转矩误差的平方为性能指标函数对直接瞬时转矩控制进行优化调整，抑制了电机在运行时的转矩脉动。进一步，在整体双闭环控制策略基础上，引入滑模控制对转速环节进行改进，提高了系统的响应速度和鲁棒性。最后，利用Matlab/Simulink对传统直接瞬时转矩控制和改进直接瞬时转矩控制进行仿真测试，验证了所提策略的有效性和可行性。

Optimization of Direct Instantaneous Torque Control Based on Sliding Mode Neural Network

For the sake of resolving the problem of large torque ripple problem of switched reluctance motor, especially under hysteresis control strategy, back propagation (BP) neural network control strategy is introduced on the account of traditional direct instantaneous torque control, and the direct instantaneous torque control is optimized and adjusted with the square of the torque error as the performance index function, which restrains the torque ripple of the motor during operation. In addition, based on the overall double closed-loop control, sliding mode control is introduced for the improvement of the speed link, which improves the response speed and robustness of the system. Finally, Matlab/Simulink is used for simulating the traditional direct instantaneous torque control and optimized direct instantaneous torque control, the effectiveness and feasibility of the proposed strategy are proved.