aVsIs: An Analytical-Solution-Based Solver for Model-Predictive Control With Hexagonal Constraints in Voltage-Source Inverter Applications

The theory of a new analytical-solution-based algorithm for calculating the optimal solution in model-predictive control applications with hexagonal constraints is discussed in this article. Three-phase voltage-source inverters for power electronic and electric motor drive applications are the target of the proposed method. The indirect model-predictive control requires a constrained quadratic programming (QP) solver to calculate the optimal solution. Most of the QP solvers use numerical algorithms, which may result in unbearable computational burdens. However, the optimal constrained solution can be calculated in an analytical way when the control horizon is limited to the first step. A computationally efficient algorithm with a certain maximum number of operations is proposed in this article. A thorough mathematical description of the solver in both the stationary and rotating reference frames is provided. Experimental results on real test rigs featuring either an electricmotor or a resistive-inductive load are reported to demonstrate the feasibility of the proposed solver, thus smoothing theway for its implementation in industrial applications. The name of the proposed solver is aVsIs, which is released under Apache License 2.0 in GitHub, and a free example is available in Code Ocean.

Automated summary

This article discusses the theory of a new analytical-solution-based algorithm for calculating the optimal solution with hexagonal constraints in model-predictive control applications. The proposed method targets three-phase voltage-source inverters for power electronic and electric motor drive applications. The algorithm provides a computationally efficient way to calculate the optimal solution in an analytical manner when the control horizon is limited to the first step.