Vol.:(0123456789) 1 3 Journal of Ambient Intelligence and Humanized Computing https://doi.org/10.1007/s12652-021-03244-3 ORIGINAL RESEARCH DTC‑IM drive using adaptive neuro fuzzy inference strategy with multilevel inverter J. Barsana Banu 1  · J. Jeyashanthi 2  · A. Thameem Ansari 3 Received: 25 June 2020 / Accepted: 29 March 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract This paper presents the speed control of direct torque controlled 3ɸ induction motor using adaptive neuro-fuzzy inference strategy (ANFIS). ANFIS controller has been utilized to produce a reference signal for the SVPWM. The gate pulses for the 3ɸ voltage source inverter (VSI) have been obtained from SVPWM. The VSI has fnally controlled the induction motor. The Simulink model for this work has been created in MATLAB. The performance exploration of the DTC-IM drive system using ANFIS has been considered, trained, and accomplished in this paper. Simulations have been done for diferent speeds such as 800, 1000, 1200, and 1400 rpm for both conventional and fve-level inverter. The simulation results have revealed that dynamic along with a transient performance of the drive has been improved using ANFIS control strategy. During the sudden variation in load torque, the machine gives good stabilization with admirable learning capability of neural networks by the use of the ANFIS controller. Moreover, the proposed fve-level inverter minimizes the total harmonic distortion (THD) in the current and voltage of the inverter compared to the conventional two-level inverter. The same model has been implemented in an experimental prototype to check the feasibility of the proposed confguration. Keywords Adaptive neuro-fuzzy inference strategy · Direct torque control · Space vector pulse width modulation · Total harmonic distortion · Voltage source inverter Abbreviations ANFIS Adaptive neuro-fuzzy inference system ANN Artifcial neural network DSC Direct self control DTC Direct torque control DSVM Discrete space vector modulation FLC Fuzzy logic control e(k) Speed control error FOC Field oriented control J Moment of inertia T e Instantaneous value of electromagnetic torque LSPMSM Line-start permanent magnet synchronous motor MPPT Maximum power point tracking P No. of pole pairs PID Proportional integral derivative PTC Predictive torque control PI Proportional integral PWM Pulse width modulation θ s, θr Stator and rotor angle L s, L r Stator and rotor inductance L m Mutual inductance L ls, L lr Stator and rotor leakage inductance R s, R r Stator and rotor resistance T L Load torque N s Number of switching states N V Number of space vectors N T Number of triangles SCR Silicon controlled rectifer SVM Space vector modulation SVPWM Space vector pulse width modulation Ψ ds, Ψ dr D-axis stator and rotor fux linkage * J. Barsana Banu barsanajamal@gmail.com J. Jeyashanthi ssanthisiddhu@gmail.com A. Thameem Ansari takbarbatcha@kisr.edu.kw 1 Department of Electrical and Electronics Engineering, Mahath Amma Institute of Engineering and Technology, Pudukkottai, India 2 Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India 3 Petroleum Research Center, Kuwait Institute of Scientifc Research, Safat, Kuwait