Auto-tune Predictive Control of Switched Reluctance Motor

This paper is based our previous work on model predictive control (MPC) of switched reluctance motor (SRM). A local linear neuro-fuzzy model is used to model SRM. Then a MPC schema is devised considering an appropriate energy term in the objective function during optimization phase. Commutation occurs naturally as an outcome of the predictive control design process, not as an extra step added to the control policy. In this paper, an adaptive -time varying- objective function is proposed to better cope with nonlinear nature of the SRM. A fast and easy algorithm is devised to adjust the weights in the objective function. This new algorithm allow for an auto-tune MPC approach to SRM control. From a computational view point, we use locally linear model predictive control that with a quadratic cost and linear constraints reduces to a simple quadratic programming, which can be solved very fast in a closed form. Simulation studies justify applicability of our proposed method and algorithm to SRM applications