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Neural-network self-correcting control method of permanent magnet synchronous motor speed loop

A permanent magnet synchronous motor, self-tuning control technology, applied in the direction of motor generator control, biological neural network model, electronic commutation motor control, etc., can solve the problem of lack of online mechanism

Active Publication Date: 2012-06-13
SOUTHEAST UNIV +1
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

According to the identified inertia, use the fuzzy reasoning method to adjust the parameters of the ADRC controller accordingly. This method has a strong adaptability to the change of inertia, but this method requires rich prior knowledge and lacks online mechanism

Method used

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  • Neural-network self-correcting control method of permanent magnet synchronous motor speed loop
  • Neural-network self-correcting control method of permanent magnet synchronous motor speed loop
  • Neural-network self-correcting control method of permanent magnet synchronous motor speed loop

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Embodiment Construction

[0040] The neural network self-calibration control method of the speed loop of the permanent magnet synchronous motor of the present invention regards the current loop and the motor as the generalized controlled objects. Considering the high real-time performance of the current loop, in the design process, the current loop can be equivalent to a gain of 1 The proportion of the link (ie ). The discretization model of the system is established as: ω ( k ) = αω ( k - 1 ) + β i q * ( k - 1 ) + γ T L ( k - 1 ) , in, α ...

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Abstract

The invention discloses a neural-network self-correcting control method of a permanent magnet synchronous motor speed loop. The method is characterized by: taking a current loop and a motor as generalized objects; firstly, collecting information, such as a rotating speed, a current and the like; using an adaptive linear time-delay neural network to carry out off-line parameter identification to the motor; then, taking a weight obtained through off-line learning as an initial value of on-line learning; finally, carrying out on-line parameter identification to the system, calculating a load torque of the motor according to the identified parameter; designing a neural-network self-correcting control law according to the obtained parameter value and a load disturbance value, adjusting the network weight on line according to an error between a controlled object and an identification model, and then setting the parameter of the neural-network self-correcting controller on line so as to realize online adjustment of the controller parameter. Uncertainty of the system and influence brought by the external disturbance can be eliminated. Dynamic performance and an anti-disturbance ability of a servo system can be improved.

Description

technical field [0001] The invention relates to a speed loop self-correction control method of a high-precision permanent magnet synchronous motor servo system, in particular to a neural network self-correction control method of a permanent magnet synchronous motor speed loop, belonging to the technical field of high-precision servo control systems. Background technique [0002] The permanent magnet synchronous motor has the characteristics of no mechanical commutator, simple structure, easy to realize forward and reverse switching, good fast response, etc., and its application range is becoming wider and wider. High-performance all-digital servo control system has become the development trend of contemporary AC servo system, and is widely used in the field of industrial production automation, especially in the field of high control precision requirements such as robots, aerospace, CNC machine tools, and special processing equipment. Therefore, the requirements for its perfo...

Claims

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Application Information

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IPC IPC(8): H02P21/14G06N3/02H02P21/00
Inventor 李世华李娟杨俊吴波吴蔚齐丹丹
Owner SOUTHEAST UNIV
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