Torque Error Based Auto-tuning of Weighting Factor in Model Predictive Torque Control of Induction Motor Drive

Weighting factor design is considered a challenging and tedious task in finite control set model predictive torque control (FCS-MPTC) for induction motor drives. The complexity involved in designing the weighting factor occurs due to the presence of different quantities in the cost function. In the absence of an accurate design method, a constant weighting factor is used for entire operating range of the drive which does not guarantee optimal performance. To improve the performance of the MPTC, an online tuning method for the weighting factor is proposed in this work. The tuning process is achieved by comparing torque error to predefined threshold value at any given sampling instant. If the error is larger than the threshold limit, weighting factor is increased to bring the error within the acceptable limit and vice versa. In each sampling interval, the cost function is optimized with the tuned weighting factor and optimal voltage vector is chosen. The effectiveness of the proposed method is validated experimentally for a two level three-phase inverter-fed induction motor drive on dSpace DS1104 controller board. The performance of the proposed method is compared to the conventional MPTC with fixed weighting factor and an online weighting factor tuning method based on the principle of coefficient-of-variation (CV-MPTC). It is concluded that the proposed method not only improves dynamic performance of the drive as compared to both methods but also offers computational advantages over CV-MPTC.