Continuous model prediction control method and device for surface-mounted permanent magnet synchronous motor
A permanent magnet synchronous motor, model predictive control technology, applied in the direction of motor control, motor generator control, AC motor control, etc., can solve problems such as external disturbance, permanent magnet synchronous motor parameter mismatch, parameter sensitivity, etc.
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Embodiment 1
[0074] This embodiment provides a continuous model predictive control method for surface-mounted permanent magnet synchronous motors, such as figure 1 shown, including:
[0075] Step 10, discretize the d-q axis mathematical model of the permanent magnet synchronous motor, and obtain the d-q axis model of the incremental permanent magnet synchronous motor through the difference between k beat and k-1 beat, thereby eliminating the influence of the flux linkage parameter, wherein k is greater than the natural number of 1;
[0076] Step 20, constructing a least squares identification strategy, inputting the input and output data of the d-axis and q-axis into the least squares identification strategy in turn to obtain the observed values of the voltage increment coefficient and the current increment coefficient;
[0077] Step 30, feeding the observed values of the voltage increment coefficient and current increment coefficient into the d-q axis model of the incremental permane...
Embodiment 2
[0129] In this embodiment, a continuous model predictive control device for a surface-mounted permanent magnet synchronous motor is provided, such as figure 2 shown, including:
[0130] Incremental model construction module, observation value acquisition module, compensation module, weight function module and convergence module;
[0131] The incremental model building block is used to discretize the d-q axis mathematical model of the permanent magnet synchronous motor, and obtain the d-q axis model of the incremental permanent magnet synchronous motor through the difference between the k beat and the k-1 beat, thereby eliminating the flux linkage parameter Influence, wherein, k is a natural number greater than 1;
[0132] The observed value acquisition module is used to construct a least squares identification strategy, and sequentially input the input and output data of the d axis and the q axis into the least squares identification strategy to obtain the observed values ...
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