An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

Model-based predictive control that highly depends on system parameters is widely investigated. In contrast, model-free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. Current gradient knowledge therefore provides a significant foundation for MFPCC. In conventional MFPCC, however, the stagnation existing in the current gradient update always impacts the reliability of current gradients and further worsens the control performance. In this article, an improved MFPCC with an advanced current gradient updating mechanism is proposed. In the proposed strategy, to guarantee the reliability of the current gradients, two contiguous measured current gradients due to the applied voltage vectors are used to estimate the current gradients for all the possible voltage vectors. This simple method takes advantage of the mathematical relationships between the voltage vectors. In this way, all the current gradients can be obtained within one control period, effectively reducing the stagnation effect in conventional MFPCC. The proposed MFPCC scheme is evaluated on a permanent magnet synchronous machine drive setup to demonstrate its effectiveness.

Automated summary

Model-based predictive control relies on system parameters, while model-free predictive current control eliminates the influence of parameter uncertainties. This study introduces an improved MFPCC scheme with an advanced current gradient updating mechanism, which effectively reduces stagnation effects.