Parameter-Free Model-Free Deadbeat Predictive Current Control Based on Optimal Harmonic Current Injection

Deadbeat Predictive Current Control (DPCC) has become a promising control method due to its excellent dynamic performance. However, the control effectiveness of traditional methods is limited by the machine parameters set in advance, which inevitably reduces the parameter robustness of the method. When machine parameters change due to factors like temperature, the mismatch between the actual values and the parameters set in the controller leads to a decline in DPCC performance, and may even cause system instability. To address this issue of parameter dependency, this paper proposes an adaptive parameter-free model-free deadbeat predictive current control (PF-MFDPCC) method suitable for interior permanent magnet synchronous motors (IPMSM). The method characterizes the error between the estimated machine parameter values and the true values by the difference between the sampled current and the reference current, and determines the required injected harmonic current by combining the minimization of torque ripples.First, the relationship of the machine parameters to be estimated is derived based on a first-order ultra-local model. Then, the optimal harmonic current is injected in the rotor reference frame (RRF), and real-time inductance parameter estimation is achieved using the Least Mean Squares (LMS) algorithm's online adaptability. This suppresses the impact of parameter mismatch in the controller and realizes parameter-free motor control. In particular, the optimal harmonic current is derived with the aim of reducing torque ripple, and the resulting harmonic current can significantly reduce the adverse effects of injected harmonic current on DPCC performance. Finally, simulations validate the effectiveness and correctness of the proposed method.