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In the burgeoning field of new energy vehicles, silicon carbide representing a new generation of semi-conductor power devices is progressively replacing silicon-based IGBTs, which also sets higher standards for the motor control performance within the corresponding innovative technological ecosystem. The precision of motor parameters is becoming increasingly critical for enhancing the performance of electric control systems as they evolve from the tradi-tional PI control and direct torque control to advanced algorithms such as model predictive control and neural network control. Aimed at the problem that the classic linear model for permanent magnet synchronous motors cannot adapt to complex and variable conditions due to nonlinear factors such as cross-saturation, a nonlinear magnetic flux identifica-tion method based on Gaussian process regression is proposed. By employing a second-order generalized integrator to acquire the magnetic flux data under dynamic conditions, the system identification is completed. Finally, the effective-ness of the proposed approach and the accuracy of parameter identification were verified through simulation and experi-mental results.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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