Dynamic neural network adaptive inverse SRM torque control method and system

A technology of dynamic neural network and self-adaptive inverse, applied in the direction of control system, direct torque control, AC motor control, etc., can solve the problem of increasing SRM torque ripple

Active Publication Date: 2019-05-10
GUILIN UNIV OF ELECTRONIC TECH
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  • Description
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Problems solved by technology

[0004] Some studies have proposed that the flux linkage of SRM direct torque control based on phase plane variable structure is fixed, but during the operation of SRM, the fixed flux linkage amplitude will increase the torque ripple when SRM starts

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  • Dynamic neural network adaptive inverse SRM torque control method and system
  • Dynamic neural network adaptive inverse SRM torque control method and system
  • Dynamic neural network adaptive inverse SRM torque control method and system

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Embodiment Construction

[0058] Embodiment of SRM torque control method based on dynamic neural network self-adaptive inversion

[0059] The main steps of the SRM torque control method embodiment of the dynamic neural network self-adaptive inversion are as follows: figure 1 As shown, the details are as follows:

[0060] Ⅰ. PD control

[0061] PD control is proportional and differential control, by the total reference torque T ref The torque deviation ΔT obtained from the measured output total torque T is used as the input signal of PD control, and its output is the torque control value u, which is used for neural network learning deviation after preprocessing, and is used as the reference magnetic value after filtering. Part of the chain, the reference flux linkage output from the torque-flux linkage model is compensated.

[0062] Ⅱ. Dynamic RBF Neural Network Adaptive Inversion

[0063] The input signal of the RBF neural network in this example is the actual total flux linkage ψ(k-1) at the momen...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02P25/08H02P25/098H02P23/30H02P23/00
Inventor 党选举经本钦彭慧敏李珊伍锡如张向文姜辉李晓唐士杰刘帆潘登
Owner GUILIN UNIV OF ELECTRONIC TECH
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