Adaptive state feedback speed controller for PMSM based on Artificial Bee Colony algorithm

Abstract This article focuses on the application of nature-inspired optimization algorithm for adaptive speed control of permanent synchronous motor (PMSM) drive with variable parameters. In the proposed approach, a state feedback controller (SFC) is utilized for speed control of the PMSM, while on-line adaptation of its coefficients is made with the help of Artificial Bee Colony (ABC) algorithm. Since ABC is the first time applied for adaptation of SFC, its necessary modifications are depicted with details. In order to assure stability and robustness of the considered control scheme, a linear–quadratic optimization method is employed during adaptation. To ensure repeatable response of the plant regardless of parameter’s variation, a model reference adaptive system (MRAS) is used. The proposed approach is examined in simulation and experimental studies, including variable moment of inertia, non-measurable load torque and unmodelled friction. These confirm that adaptive SFC based on ABC noticeably improves control performance in comparison to a non-adaptive one.

[1]  Mohamed Elhoseny,et al.  Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm , 2015, IEEE Communications Letters.

[2]  Nasser Sadati,et al.  Design of a fractional order PID controller for an AVR using particle swarm optimization , 2009 .

[3]  Nilanjan Dey,et al.  Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings , 2016, Neural Computing and Applications.

[4]  R. Krishnan,et al.  Electric Motor Drives: Modeling, Analysis, and Control , 2001 .

[5]  Lech M. Grzesiak,et al.  PMSM servo‐drive control system with a state feedback and a load torque feedforward compensation , 2012 .

[6]  Michael Athans,et al.  Gain and phase margin for multiloop LQG regulators , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[7]  Santanu Chattopadhyay,et al.  Application Mapping Onto Mesh-Based Network-on-Chip Using Discrete Particle Swarm Optimization , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[8]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[9]  A. Golea,et al.  Design and implementation of DTC based on AFLC and PSO of a PMSM , 2020, Math. Comput. Simul..

[10]  Mark Sumner,et al.  A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Marcin Kamiński.  Adaptacyjny regulator neuronowy typu RBF zastosowany w sterowaniu napędem elektrycznym z silnikami PMSM , 2018 .

[12]  Marco Tursini,et al.  Real-time gain tuning of PI controllers for high-performance PMSM drives , 2002 .

[13]  Lech M. Grzesiak,et al.  An Application of Novel Nature-Inspired Optimization Algorithms to Auto-Tuning State Feedback Speed Controller for PMSM , 2018, IEEE Transactions on Industry Applications.

[14]  Shigeo Morimoto,et al.  Trend of permanent magnet synchronous machines , 2007 .

[15]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[16]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[17]  Lech M. Grzesiak,et al.  Adaptive speed control in the PMSM drive for a non-stationary repetitive process using particle swarms , 2016, 2016 10th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG).

[18]  Seyed Abbas Taher,et al.  Neural network-based sensorless direct power control of permanent magnet synchronous motor , 2016 .

[19]  T. TARCZEWSKI,et al.  High-performance PMSM servo-drive with constrained state feedback position controller , 2018 .

[20]  Krystian Erwinski,et al.  Accelerating PSO based feedrate optimization for NURBS toolpaths using parallel computation with OpenMP , 2017, 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR).

[21]  Ronald Aylmer Sir Fisher,et al.  The genetical theory of natural selection: a complete variorum edition. , 1999 .

[22]  Bing Chen,et al.  Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Lech M. Grzesiak,et al.  Constrained State Feedback Speed Control of PMSM Based on Model Predictive Approach , 2016, IEEE Transactions on Industrial Electronics.

[24]  Krystel K. Castillo-Villar,et al.  Pareto design of an adaptive robust hybrid of PID and sliding control for a biped robot via genetic algorithm optimization , 2014, Nonlinear Dynamics.

[25]  Shigeo Morimoto Senior Member.  Trend of permanent magnet synchronous machines , 2007 .

[26]  M. A. Rahman,et al.  On-line adaptive artificial neural network based vector control of permanent magnet synchronous motors , 1998 .

[27]  Mohamed Machmoum,et al.  Grey Wolf based control for speed ripple reduction at low speed operation of PMSM drives. , 2018, ISA transactions.

[28]  Lalit Chandra Saikia,et al.  Automatic generation control using two degree of freedom fractional order PID controller , 2014 .

[29]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[30]  Lin Zhao,et al.  Finite-time adaptive fuzzy control for induction motors with input saturation based on command filtering , 2018 .

[31]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[32]  Gene F. Franklin,et al.  Digital control of dynamic systems , 1980 .

[33]  Hamid Reza Karimi,et al.  An ant colony optimization-based fuzzy predictive control approach for nonlinear processes , 2015, Inf. Sci..

[34]  Han Ho Choi,et al.  Nonlinear Optimal DTC Design and Stability Analysis for Interior Permanent Magnet Synchronous Motor Drives , 2015, IEEE/ASME Transactions on Mechatronics.

[35]  Di Wang,et al.  Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design , 2015 .

[36]  Chong Lin,et al.  Neural network-based discrete-time command filtered adaptive position tracking control for induction motors via backstepping , 2017, Neurocomputing.

[37]  Mustafa Aktas,et al.  Comparison of DC link current and stator phase current in inverter switching faults detection of PMSM drives in HEVs , 2018, Engineering Science and Technology, an International Journal.

[38]  Bing Chen,et al.  Approximation-Based Discrete-Time Adaptive Position Tracking Control for Interior Permanent Magnet Synchronous Motors , 2015, IEEE Transactions on Cybernetics.

[39]  Pierre-Yves Glorennec,et al.  Tuning fuzzy PID controllers using ant colony optimization , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[40]  Frede Blaabjerg,et al.  Control in Power Electronics: selected problems , 2002 .