Predictive torque control (PTC) is one of the widely used modern control techniques for induction motor drives due to its merits such as; implementation is straightforward and direct inclusion of control parameters into the costfunction is possible. However, the main drawback of this technique is the selection of appropriate weighting factor in the cost-function. In this study an attempt is made to simplify the weighting factor selection by using VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method and the cost function of PTC is modified accordingly. Usage of VIKOR method in the cost-function optimisation introduces the compromise ranking to select an optimal control action. Both simulation and experimental results are carried out for a two-level voltage source inverter fed induction motor drive with the proposed control technique and the results are compared with conventional PTC technique. These results confirm that proposed method retains the dynamic response achieved by the conventional PTC along with reduced torque ripple and switching frequency.

Finite control set predictive torque control (FCS-PTC) becomes popular for induction motor drives due to its simple structure and flexibility of including additional control parameters into the control law. However, primary concern of this control technique is the selection of suitable weighting factors in the cost-function. Usually, empirical method is used to select the weighting factors, which is time-consuming and heuristic process. In this study, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced in the cost-function optimization to simplify the difficulties involved in the weighting factor selection. This method selects an optimal control action, which is closer to positive ideal control action and far away from negative ideal control action. This e n s u r e st h es e l e c t i o no fo p t i m a lc o n t r o la c t i o ni ne a c hs a m p l i n gp e r i o db a s e do nt h ep r i o r i t i e sg i v e nt oc o n t r o l parameters in the cost-function. Further, to reduce the computational burden of proposed technique, a predefined set of switching states are used for the cost-function optimization based on previous optimal control action. Both simulation and experimental studies are carried out for a two-level voltage source inverter fed induction motor drive. These results are compared with conventional FCS-PTC technique to highlight the merits of proposed technique.

A selective finite states model predictive control is proposed for a grid interfaced three-level neutral point clamped solar photovoltaic inverter. The proposed control approach eliminates the weighting factor selection for dc-link capacitor voltage balancing and reduces the computational burden for real-time implementation. The switching states required for the prediction and objective function optimisation are selected based on the position of reference voltage vector in the space vector plane, inverter current directions and the charge status of the dc-link capacitors. As a result, the selection of optimal switching state is fast, easy to implement and eliminates the selection of weighting factor for capacitor voltage balancing. The feasibility of the proposed control approach is verified through simulation and laboratory-scale experimentation. The results confirm that the proposed method attains the inherent dc-link capacitor voltage balance and also retains the dynamic and steady-state current tracking in comparison with the classical finite control-set model predictive control.

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This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active-reactive power control. The proposed centralised model predictive control (CMPC) incorporates the constraints of maximum power extraction, dc-link capacitor voltage balancing and active-reactive power tracking in a single objective function. The dc-link voltage of the inverter is regulated to its reference for extracting the maximum power. In order to eliminate the impact of reactive power exchange on floating dc-link voltage regulation, a decoupled activereactive power control is used in the CMPC. Furthermore, a preference selective index-based dynamic weighting factor selection approach is introduced to maintain the relative importance between the power tracking and dc-link capacitor voltage balancing. The proposed control approach eliminates the outer dc-link voltage control loop and also, the empirical approach required for the selection of weighting factors. As a result, it ensures an optimal control action in each sampling period to improve the steady-state and dynamic tracking performance of the control objectives. The proposed control approach is experimentally verified by using a 1.2 kW laboratory-scale prototype and the results are presented to demonstrate its effectiveness compared to the classical proportional-integral-based model predictive control.