Dynamic neural network adaptive inverse SRM torque control method and system

Abstract

The invention provides a dynamic neural network adaptive inverse SRM torque control method and system. An actual total flux linkage at a previous moment of a system, a current reference torque and a previous-moment reference flux linkage output by an RBF neural network serve as input signals of the RBF neural network, the reference flux linkage is output, and a dynamic RBF neural network, namely,a torque-flux linkage model is formed; and a torque deviation is subjected to PD control to obtain a control quantity, the control quantity is pre-processed to serve as a learning deviation of RBF neural network adaptive inverse control, and the control quantity is subjected to filtering processing to serve as part of a total reference flux linkage, thereby compensating an output of the flux linkage model. The total reference flux linkage and the actual total flux linkage are subjected to subtraction to obtain a flux linkage deviation, and through flux linkage deviation distribution, the fluxlinkage deviation hysteresis control of each phase is accessed, so that the torque pulsation of an SRM is effectively inhibited. The rapid control requirement of the motor is met; a feedback error learning method accelerates the neural network modeling and improves the modeling precision; and the influence of the torque pulsation is reduced.

Description

technical field

[0001] The invention relates to the control field of electric vehicle motors, in particular to a dynamic neural network self-adaptive inverse SRM torque control method and system. Background technique

[0002] The switched reluctance motor (Switched Reluctance Motor, SRM) has a simple and firm structure, no permanent magnet material, low manufacturing cost, high system reliability, and wide speed range, and is used in many fields. However, due to the doubly salient pole structure of SRM, the switching power supply mode and magnetic circuit saturation produce large torque ripple, which seriously restricts the application of SRM.

[0003] In the traditional control method of SRM, the current chopping control uses the current as the control quantity, the voltage chopping control takes the voltage as the control quantity, and the angle position control takes the switch angle as the control quantity. Although these control methods are simple, they cannot achieve i...

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