Approach for classifying continuous control set‐predictive controllers applied in AC motor drives

In this article, basic concepts, operating principles and important characteristics of continuous control set-predictive controllers (CCS-PCs) applied to AC motors are explained in detail. Based on the existence or absence of cost function as well as the method used to find the optimal control action, CCS-PCs can be categorised into the following three categories: predictive controllers without cost function, predictive controllers with a cost function (or model predictive controllers) and deadbeat controllers. To identify the advantages and disadvantages of each category, one of the recent algorithms of each category is selected and implemented experimentally. Then, various experimental tests are conducted where performances of all the algorithms are assessed in a comprehensive manner by evaluating stator-flux ripple, torque ripple, stator current harmonics, robustness against parameter changes, computational complexity, memory requirement and torque dynamic response. To achieve general and meaningful conclusions, the analysis is performed on a traditional three-phase two-level voltage source inverter used to control an interior permanent-magnet synchronous motor. Based on the theoretical discussions and experimental results, it is indicated that which category of CCS-PCs can be adopted in high performance AC motor drives.

[1]  Robert D. Lorenz,et al.  Loss Manipulation Capabilities of Deadbeat Direct Torque and Flux Control Induction Machine Drives , 2015, IEEE Transactions on Industry Applications.

[2]  R. Kennel,et al.  An Improved FCS–MPC Algorithm for an Induction Motor With an Imposed Optimized Weighting Factor , 2012, IEEE Transactions on Power Electronics.

[3]  R.D. Lorenz,et al.  Stator and rotor flux based deadbeat direct torque control of induction machines , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[4]  Ralph Kennel,et al.  Ripple-reduced model predictive direct power control for active front-end power converters with extended switching vectors and time-optimised control , 2016 .

[5]  Dylan Dah-Chuan Lu,et al.  A Simplified Finite-State Predictive Direct Torque Control for Induction Motor Drive , 2016, IEEE Transactions on Industrial Electronics.

[6]  Robert D. Lorenz,et al.  Deadbeat-Direct Torque and Flux Control of Interior Permanent Magnet Synchronous Machines With Discrete Time Stator Current and Stator Flux Linkage Observer , 2011, IEEE Transactions on Industry Applications.

[7]  R. D. Lorenz,et al.  Dynamic loss minimization using improved deadbeat-direct torque and flux control for interior permanent magnet synchronous machines , 2012, 2012 IEEE Energy Conversion Congress and Exposition (ECCE).

[8]  Pavel Vaclavek,et al.  PMSM Model Predictive Control With Field-Weakening Implementation , 2016, IEEE Transactions on Industrial Electronics.

[9]  Heng Nian,et al.  Dead-beat predictive direct power control of voltage source inverters with optimised switching patterns , 2017 .

[10]  U. Ammann,et al.  Model Predictive Control—A Simple and Powerful Method to Control Power Converters , 2009, IEEE Transactions on Industrial Electronics.

[11]  Wei Xie,et al.  Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives , 2017, IEEE Transactions on Industrial Electronics.

[12]  A very fast direct torque control for interior permanent magnet synchronous motors start up , 2005 .

[13]  G. Abad,et al.  Two-Level VSC Based Predictive Direct Torque Control of the Doubly Fed Induction Machine With Reduced Torque and Flux Ripples at Low Constant Switching Frequency , 2008, IEEE Transactions on Power Electronics.

[14]  Heng Nian,et al.  Model predictive stator current control of doubly fed induction generator during network unbalance , 2018 .

[15]  Yongchang Zhang,et al.  A Simple Method to Reduce Torque Ripple in Direct Torque-Controlled Permanent-Magnet Synchronous Motor by Using Vectors With Variable Amplitude and Angle , 2011, IEEE Transactions on Industrial Electronics.

[16]  Wensheng Song,et al.  Virtual Direct Power Control Scheme of Dual Active Bridge DC–DC Converters for Fast Dynamic Response , 2018, IEEE Transactions on Power Electronics.

[17]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[18]  Frede Blaabjerg,et al.  Low-Complexity Model Predictive Control of Single-Phase Three-Level Rectifiers With Unbalanced Load , 2018, IEEE Transactions on Power Electronics.

[19]  Srinivasa Rao Sandepudi,et al.  Finite control set predictive torque control for induction motor drive with simplified weighting factor selection using TOPSIS method , 2017 .

[20]  Manfred Morari,et al.  A hybrid model predictive control approach to the direct torque control problem of induction motors , 2007 .

[21]  Carlos E. Garcia,et al.  QUADRATIC PROGRAMMING SOLUTION OF DYNAMIC MATRIX CONTROL (QDMC) , 1986 .

[22]  Krzysztof Szabat,et al.  Application of the MPC to the Position Control of the Two-Mass Drive System , 2013, IEEE Transactions on Industrial Electronics.

[23]  Marian P. Kazmierkowski,et al.  “Predictive control in power electronics and drives” , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[24]  Jun Wang,et al.  A Novel Recurrent Neural Network for Solving Nonlinear Optimization Problems With Inequality Constraints , 2008, IEEE Transactions on Neural Networks.

[25]  Yongchang Zhang,et al.  Three-Vectors-Based Predictive Direct Power Control of the Doubly Fed Induction Generator for Wind Energy Applications , 2014, IEEE Transactions on Power Electronics.

[26]  Chen Qi,et al.  Deadbeat control for a single-phase cascaded H-bridge rectifier with voltage balancing modulation , 2017 .

[27]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[28]  Kimon P. Valavanis,et al.  Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control , 2010, J. Intell. Robotic Syst..

[29]  Leonidas G. Bleris,et al.  Towards embedded model predictive control for System-on-a-Chip applications , 2006 .

[30]  Vassilios G. Agelidis,et al.  Model Predictive Control for Single-Phase NPC Converters Based on Optimal Switching Sequences , 2016, IEEE Transactions on Industrial Electronics.

[31]  Hao Zhu,et al.  Torque Ripple Reduction of the Torque Predictive Control Scheme for Permanent-Magnet Synchronous Motors , 2012, IEEE Transactions on Industrial Electronics.

[32]  Juan I. Yuz,et al.  Predictive Speed Control of a Two-Mass System Driven by a Permanent Magnet Synchronous Motor , 2012, IEEE Transactions on Industrial Electronics.

[33]  Mohammad Hossein Vafaie,et al.  Improving the Steady-State and Transient-State Performances of PMSM Through an Advanced Deadbeat Direct Torque and Flux Control System , 2017, IEEE Transactions on Power Electronics.

[34]  Saad Mekhilef,et al.  Digital predictive current control of multi-level four-leg voltage-source inverter under balanced and unbalanced load conditions , 2017 .

[35]  Vincent A Akpan,et al.  Nonlinear model identification and adaptive model predictive control using neural networks. , 2011, ISA transactions.

[36]  Leonidas G. Bleris,et al.  A System-on-a-Chip Implementation for Embedded Real-Time Model Predictive Control , 2009, IEEE Transactions on Control Systems Technology.

[37]  Jan Melkebeek,et al.  Weight factor selection for model-based predictive control of a four-level flying-capacitor inverter , 2012 .

[38]  Marco Rivera,et al.  A Computationally Efficient Lookup Table Based FCS-MPC for PMSM Drives Fed by Matrix Converters , 2017, IEEE Transactions on Industrial Electronics.

[39]  Sangshin Kwak,et al.  Finite control set predictive control based on Lyapunov function for three-phase voltage source inverters , 2014 .

[40]  Mohammad Hossein Vafaie,et al.  Minimizing Torque and Flux Ripples and Improving Dynamic Response of PMSM Using a Voltage Vector With Optimal Parameters , 2016, IEEE Transactions on Industrial Electronics.

[41]  Hong Chen,et al.  Field programmable gate array/system on a programmable chip-based implementation of model predictive controller , 2012 .

[42]  Mohammad Hossein Vafaie,et al.  Approach for classifying direct PCs applied to AC motor drives , 2019, IET Electric Power Applications.

[43]  Daniel E. Quevedo,et al.  Performance of Multistep Finite Control Set Model Predictive Control for Power Electronics , 2015 .

[44]  Jun Wang,et al.  Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks , 2012, IEEE Transactions on Industrial Electronics.

[45]  Mohammad Hossein Vafaie,et al.  A New Predictive Direct Torque Control Method for Improving Both Steady-State and Transient-State Operations of the PMSM , 2016, IEEE Transactions on Power Electronics.

[46]  S. Mariethoz,et al.  High-Bandwidth Explicit Model Predictive Control of Electrical Drives , 2012, IEEE Transactions on Industry Applications.

[47]  Matthias Preindl,et al.  Robust Control Invariant Sets and Lyapunov-Based MPC for IPM Synchronous Motor Drives , 2016, IEEE Transactions on Industrial Electronics.

[48]  Alberto Bemporad,et al.  Model predictive control based on linear programming - the explicit solution , 2002, IEEE Transactions on Automatic Control.

[49]  Wensheng Song,et al.  Deadbeat Predictive Power Control of Single-Phase Three-Level Neutral-Point-Clamped Converters Using Space-Vector Modulation for Electric Railway Traction , 2016, IEEE Transactions on Power Electronics.