Model-free predictive current control method for permanent magnet synchronous motor

Abstract

The invention provides a model-free predictive current control method for a permanent magnet synchronous motor. Firstly, a permanent magnet synchronous motor mathematical model is replaced by a single-input single-output hyper-local model, and the hyper-local model does not need any motor parameter information and is also called no model. Then, a sliding mode observer is adopted to estimate current errors caused by parameter disturbance, and feedforward compensation is carried out; and finally, simulation and experiment results verify that the provided model-free predictive control method can suppress parameter disturbance and has parameter robustness.

Description

technical field [0001] The invention belongs to the technical field of permanent magnet synchronous motor control, and in particular relates to parameter disturbance suppression realized by a model-free current prediction method when parameters of a permanent magnet synchronous motor are mismatched. Background technique [0002] Permanent magnet synchronous motor is a strongly coupled, multivariable nonlinear system, and its control performance is closely related to the adopted control strategy. The control strategies applied in the permanent magnet synchronous motor drive system in the prior art mainly include proportional-integral control (PI), direct torque control (DTC) and current predictive control. PI control has good steady-state performance, but poor dynamic performance, and there is a natural contradiction between fast response and no overshoot. DTC has the advantages of fast response speed and strong parameter robustness, but it is hysteretic control in essence, ...

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Problems solved by technology

However, predictive control is essentially a model-based control method, and the uncertainty of model parameters can lead to model mismatch and negatively affect the performance of predictively controlled permanent magnet synchronous motor drive systems.

In order to solve the problem of reduced control performance caused by the uncertainty of motor parameters, some control strategies for suppressing disturbances are used in some solutions. Common suppression strategies include sliding mode variable structure control, fuzzy control, neural network, etc. However, these control strategies The strategy still relies heavily on motor parameter information, and there are still some shortcomings in terms of robustness

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