Method and system for controlling constant torque of switched reluctance motor by use of composite control current

Abstract

The invention relates to a method and a system for controlling constant torque of a switched reluctance motor by use of composite control current. According to the method, linear control current of each phase is obtained on the basis of linear inductance model torque distribution function control, and an echo state network is adopted and outputs non-linear current according to current total torque of the switched reluctance motor, given total torque and output non-linear current feedback as well as switched reluctance motor Jacobian information calculated by an RBF (radial basis function) neural network through parameter learning; non-linear control current of each phase and the linear control current are superposed, and the composite control current is obtained and taken as a set value for a current hysteresis loop controller. Current, torque and positon sensors of the system are connected with a signal processor, the signal processor executes the module of the method and outputs the composite control current, a power converter of the motor is controlled through the current hysteresis loop controller, and torque ripple of the switched reluctance motor is remarkably and effectively inhibited.

Description

technical field [0001] The invention relates to the technical field of switched reluctance motor control, in particular to a method and system for controlling constant torque of a switched reluctance motor with compound control current. Background technique [0002] Switched Reluctance motor (SRM for short) is solid and reliable, does not require rare earth materials and has flexible speed regulation, so it has become the mainstream of new energy electric vehicle motors. However, the switched reluctance motor has a special doubly salient pole structure, adopts a switching power supply mode, and the magnetic circuit is strongly nonlinear and saturated, so there are defects such as torque ripple, noise, and vibration when the switched reluctance motor is running, especially The torque fluctuation is more intense in the low-speed area, which hinders its more general application in the drive system of higher-performance new energy electric vehicles. Therefore, it is very import...

AI Technical Summary

Problems solved by technology

However, the classic BP (backpropagation) neural network algorithm is used in these neural network controls, and the convergence speed is slow.

In addition, the existing neural network control method considers the linear and nonlinear characteristics of the switched reluctance motor as a whole, which makes the control design complicated.

However, the linear control current obtained based on the linear inductance model TSF (TorqueDistribution Function) control is difficult to adapt to the nonlinear characteristics of the reluctance motor, resulting in large torque ripple

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