Multiobjective Fuzzy Predictive Torque Control of an induction machine fed by a 3L-NPC inverter

Recently the use of Model Predictive Control (MPC) in electrical motor drives has been reported both theoretically and experimentally. Predictive Torque Control (PTC) has been developed to control induction motor drives, allowing high-performance and fast dynamics. However, the optimization used in PTC is based on a single cost function minimization, where control objectives are merged by using weighting factors. The adjustment of these factors is achieved through a nontrivial process and they are heavily dependent on the system parameters and user requirements, being a complex task in systems where two or three weighting factors must be adjusted. To avoid the well-known time-consuming simulations and branch and bound search process, a Multiobjective Fuzzy Predictive Torque Control (FPTC) is presented for a Three-Level Neutral-Point-Clamped voltage source inverter (3L-NPC). The proposed strategy changes the single cost function with a Multiobjective Optimization based on Fuzzy-Decision-Making. Simulation results are presented to illustrate the behavior of the motor drive under steady-state and dynamic conditions.

[1]  Uzay Kaymak,et al.  Model predictive control using fuzzy decision functions , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Hendrik du T. Mouton,et al.  Natural balancing of three-level neutral-point-clamped PWM inverters , 2002, IEEE Trans. Ind. Electron..

[3]  J. Rodriguez,et al.  Direct torque control of an 3L-NPC inverter-fed induction machine: A model predictive approach , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[4]  Patricio Cortes,et al.  Predictive Control of Power Converters and Electrical Drives: Rodriguez/Predictive Control of Power Converters and Electrical Drives , 2012 .

[5]  Marian P. Kazmierkowski,et al.  Control in Power Electronics , 2013 .

[6]  Frede Blaabjerg,et al.  Control in Power Electronics , 2002 .

[7]  Ralph Kennel,et al.  Cascade-Free Predictive Speed Control for Electrical Drives , 2014, IEEE Transactions on Industrial Electronics.

[8]  Bin Wu,et al.  Electric Vehicle Charging Station Using a Neutral Point Clamped Converter With Bipolar DC Bus , 2015, IEEE Transactions on Industrial Electronics.

[9]  Thomas L. Saaty,et al.  Models, Methods, Concepts & Applications of the Analytic Hierarchy Process , 2012 .

[10]  José Rodríguez,et al.  A comparison of discrete-time models for model predictive control of induction motor drives , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[11]  Ronald R. Yager,et al.  Multiple objective decision-making using fuzzy sets , 1977 .

[12]  Ralph Kennel,et al.  An Improved FCS-MPC algorithm with Imposed Optimized Weighting Factor , 2011 .

[13]  José R. Espinoza,et al.  Multiobjective Switching State Selector for Finite-States Model Predictive Control Based on Fuzzy Decision Making in a Matrix Converter , 2013, IEEE Transactions on Industrial Electronics.

[14]  Tobias Geyer,et al.  Model predictive direct current control , 2010, 2010 IEEE International Conference on Industrial Technology.

[15]  Marian P. Kazmierkowski,et al.  State of the Art of Finite Control Set Model Predictive Control in Power Electronics , 2013, IEEE Transactions on Industrial Informatics.

[16]  Ralph Kennel,et al.  High-Performance Control Strategies for Electrical Drives: An Experimental Assessment , 2012, IEEE Transactions on Industrial Electronics.

[17]  R. S. Kanchan,et al.  Model-Based Predictive Control of Electric Drives , 2010 .

[18]  Dushan Boroyevich,et al.  A comprehensive study of neutral-point voltage balancing problem in three-level neutral-point-clamped voltage source PWM inverters , 2000 .

[19]  M. Brunelli Introduction to the Analytic Hierarchy Process , 2014 .

[20]  Jay H. Lee,et al.  Model predictive control: Review of the three decades of development , 2011 .

[21]  Petr Ekel,et al.  Decision making in a fuzzy environment and its application to multicriteria power engineering problems , 2007 .