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
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2015-04-29
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Abstract
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...
Examples
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 = ...