Affine Ultra-Local-based Model-Free Predictive Control Strategy on AC Motor Drives
In complex operating conditions and motion states,the AC motor system has nonlinear time-varying physical parameters due to factors such as magnetic field coupling and iron core saturation,which weakens the motor control performance and system robustness.To address the above issues,the model-free predictive control strategy utilizes the inherent relationship between variables of the AC motor to construct a data-driven model,thereby eliminating the dependence on physical parameters and eliminating the impact of parameter mismatches.In this paper,combined with the permanent magnet synchronous motordrives,an affine ultra-local model is designed,and a model-free predictive current control strategy is designed.This method uses the least squares algorithmto estimate affine operators online and designs a state compensation mechanism.State compensation gains are selected through the ε-approximation consideration to reflect the system motion characteristics in real time.The stability of the method is verified through the analysis of system zeros and poles,and its effectiveness,model adaptability,current quality,and system robustness are verified through experiments.
AC motormodel-free predictive controlaffine ultra-local modelε-estimating considerationdata-driven model
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