Experimental verification for cost function optimization using TOPSIS approach for predictive control of surface mounted PMSM

Abstract In this article, the selection of weight factor (WF) is improved using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based cost function (CF) optimization for predictive control in surface mounted permanent magnet synchronous motor (PMSM) drive under different conditions. Basically, the empirical approach used for WF is instinctive and consumes excess time. TOPSIS approach chooses a control action that is closest to the ideal control action's positive solution and farthest from its ideal control action's negative solution. Prioritizing control variables in the CF assures the choice of the best control action throughout each sampling duration. Additionally, predefined switching instants are employed for the CF optimization depending on past optimal control action, reducing the computing duration of the suggested technique. dSPACE controller is used for real‐time execution of traditional and proposed methods under steady and dynamic states. In order to verify the worth of predictive control based on TOPSIS, the hardware responses were compared with traditional DTC and predictive control strategies.