Multi-step predictive current control designed for electric drives with adaptive horizons

Traditional Model Predictive Control (MPC) strategies in the motor drive system cannot fully meet the requirements. As the third generation of advanced control technology, its potential must be further realized to achieve excellent control performance.

For the common MPC, the quality of the control objective is limited due to its limited processor resources and strong dependence on horizon coefficients and weighting factors, and the application in the high-end manufacturing field is restricted. A reasonable distribution scheme for the resources is important for the stability of the compensation system, the current quality and the calculation loads.

In a study published in IEEE Transactions on Industrial ElectronicsProf. Wang Fengxiang’s group from the Fujian Research Institute of the Structure of Matter of the Chinese Academy of Sciences designed a multi-step predictive current control strategy with adaptive horizons for electrical drives to achieve better performance Comprehensive control such as prediction accuracy, computational load, and objective importance by fully utilizing limited processor resources.

The researchers first analyzed the development of objective functions with different types of long prediction horizons and vector selections, and verified the need for a set of suitable weighting factors in MPC. Through many iterative forecasting processes, state variables are forecast for a future sampling period greater than one, and a set of weights converging to zero is selected to reduce suboptimal chance and improve the rate of convergence.

Although stability during transients can be improved by increasing the prediction horizon to ensure convergence, computational load and prediction error problems become serious. The researchers found that these problems can be effectively solved by using the flexible control horizon and the weighting factor. To perform this function, the operating states were divided into three types and determined by hysteresis logic to correct the signal for changing horizon steps.

An adaptive logic was generated to obtain adjusted horizons online and achieve a good control law according to the current operating state and accumulated speed errors.

The researchers took full advantage of the extra time scale to obtain an optimal weighting factor using the designed real-time limit and branch algorithm, and this value is applied in the system at the end of the control horizon to adjust the importance of the objectives. . All possibilities for the additional time scale to sufficiently utilize the constrained processor resources are listed, and this term becomes a potential optimization.

In addition, the researchers selected a permanent magnet synchronous motor (PMSM) speed control system as an example to demonstrate the effectiveness of the presented control strategy. According to the simulation and the experimental results, the presented control strategy obtains better tolerances, quality and current impact compared to conventional predictive controls under the same operating conditions. The presented method has enough compatibility to be applied to other motor driving systems to perform better control performances.

This study provides essential guidance for the future design and synthesis of long-horizon predictive control for the motor drive system.