Equal weighted cost function based weighting factor tuning method for model predictive control in power converters

Abstract

An equal-weighted cost function-based weighting factor tuning method for model predictive control (MPC) in power converters is presented here. In conventional MPC methods developed for power converters, the use of weighting factors is a necessity to determine the importance of sub-terms in multi-objective cost functions. The proposed method is based on equalizing the importance of cost terms by using normalized values rather than the actual values. As a consequence of normalized values, all sub-terms are transformed into a similar range independent from their actual values. Hence, the weights of cost terms are equalized, leading to the elimination of weighting factor tuning necessity. The proposed equal-weighted cost function not only offers simplicity in the MPC design but also guarantees the desired controller performance with the dynamically weighted sub-terms. The proposed control method is validated on a grid-connected single-phase three-level T-type inverter. Experimental results are carried out to demonstrate the effectiveness of the proposed method under steady-state and dynamic conditions. Moreover, the comparative results with the conventional MPC are presented. The results reveal that the proposed approach exhibits excellent performance against the variations in the operating point of the inverter.