Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) fault-tolerant control of a six-phase permanent magnet synchronous motor (PMSM) position servo drive is proposed in this study. First, the fault detection and operating decision method of the six-phase PMSM position servo drive is developed. Then, an ideal computed torque controller is designed for the tracking of the rotor position reference command. In general, it is impossible to design an ideal computed control law owing to the uncertainties of the six-phase PMSM position servo drive, which are difficult to know in advance for practical applications. Therefore, the RFNCMAN, which combined the merits of a recurrent fuzzy cerebellar model articulation network and a recurrent fuzzy neural network, is proposed to estimate a nonlinear equation included in the ideal computed control law with a robust compensator designed to compensate the minimum reconstructed error. Furthermore, the adaptive learning algorithm for the online training of the RFNCMAN is derived using the Lyapunov stability to guarantee the closed-loop stability. Finally, the proposed RFNCMAN fault-tolerant control system is implemented in a 32-bit floating-point DSP. The effectiveness of the six-phase PMSM position servo drive using the proposed intelligent fault-tolerant control system is verified by some experimental results.

[1]  W. Thomas Miller,et al.  Sensor-based control of robotic manipulators using a general learning algorithm , 1987, IEEE J. Robotics Autom..

[2]  Plamen P. Angelov,et al.  PANFIS: A Novel Incremental Learning Machine , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Faa-Jeng Lin,et al.  Adaptive backstepping control for linear induction motor drive to track periodic references , 2000 .

[4]  Toshio Fukuda,et al.  Intelligent position/force controller for industrial robot manipulators-application of fuzzy neural networks , 1997, IEEE Trans. Ind. Electron..

[5]  Rong-Jong Wai,et al.  Hybrid control using recurrent fuzzy neural network for linear induction motor servo drive , 2001, IEEE Trans. Fuzzy Syst..

[6]  T.A. Lipo,et al.  Torque density improvement in a six-phase induction motor with third harmonic current injection , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[7]  Gin-Der Wu,et al.  A Maximizing-Discriminability-Based Self-Organizing Fuzzy Network for Classification Problems , 2010, IEEE Transactions on Fuzzy Systems.

[8]  Gérard-André Capolino,et al.  Modeling and Control of Six-Phase Symmetrical Induction Machine Under Fault Condition Due to Open Phases , 2008, IEEE Transactions on Industrial Electronics.

[9]  Chaio-Shiung Chen Supervisory Interval Type-2 TSK Neural Fuzzy Network Control for Linear Microstepping Motor Drives With Uncertainty Observer , 2011, IEEE Transactions on Power Electronics.

[10]  Shih-Chia Huang,et al.  Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Jing Tang,et al.  Application of new FCMAC neural network in power system marginal price forecasting , 2005, 2005 International Power Engineering Conference.

[12]  Mahardhika Pratama,et al.  pClass: An Effective Classifier for Streaming Examples , 2015, IEEE Transactions on Fuzzy Systems.

[13]  Faa-Jeng Lin,et al.  Fault-Tolerant Control of a Six-Phase Motor Drive System Using a Takagi–Sugeno–Kang Type Fuzzy Neural Network With Asymmetric Membership Function , 2013, IEEE Transactions on Power Electronics.

[14]  Meng Joo Er,et al.  Data driven modeling based on dynamic parsimonious fuzzy neural network , 2013, Neurocomputing.

[15]  Faa-Jeng Lin,et al.  Fault-Tolerant Control for Six-Phase PMSM Drive System via Intelligent Complementary Sliding-Mode Control Using TSKFNN-AMF , 2013, IEEE Transactions on Industrial Electronics.

[16]  Shuang Cong,et al.  PID-Like Neural Network Nonlinear Adaptive Control for Uncertain Multivariable Motion Control Systems , 2009, IEEE Transactions on Industrial Electronics.

[17]  Henrique Oliveira Henriques,et al.  Development of transient fault management methodology , 2014, IEEE Latin America Transactions.

[18]  Chin-Teng Lin,et al.  Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Ying-Chung Wang,et al.  Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Walmir M. Caminhas,et al.  A fast learning algorithm for evolving neo-fuzzy neuron , 2014, Appl. Soft Comput..

[21]  A. von Jouanne,et al.  Multilevel inverter modulation schemes to eliminate common-mode voltages , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[22]  Gérard-André Capolino,et al.  Fuzzy Logic and Sliding-Mode Controls Applied to Six-Phase Induction Machine With Open Phases , 2010, IEEE Transactions on Industrial Electronics.

[23]  Jose de Jesus Rubio,et al.  Modified optimal control with a backpropagation network for robotic arms , 2012 .

[24]  Chih-Kai Chang,et al.  FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive , 2007, IEEE Transactions on Power Electronics.

[25]  Mahardhika Pratama,et al.  GENEFIS: Toward an Effective Localist Network , 2014, IEEE Transactions on Fuzzy Systems.

[26]  Chia-Feng Juang,et al.  A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms , 2002, IEEE Trans. Fuzzy Syst..

[27]  Chun-Shin Lin,et al.  Learning convergence of CMAC technique , 1997, IEEE Trans. Neural Networks.

[28]  Babak Nahid-Mobarakeh,et al.  Fault Tolerant and Minimum Loss Control of Double-Star Synchronous Machines Under Open Phase Conditions , 2008, IEEE Transactions on Industrial Electronics.

[29]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[30]  Gin-Der Wu,et al.  An enhanced discriminability recurrent fuzzy neural network for temporal classification problems , 2014, Fuzzy Sets Syst..

[31]  De-Shuang Huang,et al.  A Hybrid Forward Algorithm for RBF Neural Network Construction , 2006, IEEE Transactions on Neural Networks.

[32]  Demba Diallo,et al.  Advanced Fault-Tolerant Control of Induction-Motor Drives for EV/HEV Traction Applications: From Conventional to Modern and Intelligent Control Techniques , 2007, IEEE Transactions on Vehicular Technology.

[33]  Choon Ki Ahn,et al.  Takagi–Sugeno Fuzzy Hopfield Neural Networks for $${\mathcal{H}_{\infty}}$$ Nonlinear System Identification , 2011, Neural Processing Letters.

[34]  Rodolfo E. Haber,et al.  A Transductive Neuro-Fuzzy Controller: Application to a Drilling Process , 2010, IEEE Transactions on Neural Networks.

[35]  K. Yubai,et al.  Fault-tolerant control system of flexible arm for sensor fault by using reaction force observer , 2004, IEEE/ASME Transactions on Mechatronics.

[36]  Aníbal R. Figueiras-Vidal,et al.  Generalizing CMAC architecture and training , 1998, IEEE Trans. Neural Networks.

[37]  Jose Ruiz Ascencio,et al.  Identification and Control of Systems With and Without Zeros Via Approximation of the State Evolution Function , 2014, IEEE Latin America Transactions.

[38]  Jyh-Yeong Chang,et al.  A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[39]  Rong-Jong Wai,et al.  Adaptive Fuzzy-Neural-Network Design for Voltage Tracking Control of a DC–DC Boost Converter , 2012, IEEE Transactions on Power Electronics.

[40]  Fernando Bordignon,et al.  Uninorm based evolving neural networks and approximation capabilities , 2014, Neurocomputing.

[41]  Michel Pasquier,et al.  RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction , 2011, Expert Syst. Appl..

[42]  Y.S. Abu-Mostafa,et al.  Information theory, complexity and neural networks , 1989, IEEE Communications Magazine.

[43]  José de Jesús Rubio,et al.  Evolving intelligent algorithms for the modelling of brain and eye signals , 2014, Appl. Soft Comput..

[44]  J.-Y. Chen,et al.  Adaptive design of a fuzzy cerebellar model arithmetic controller neural network , 2005 .

[45]  Marcio Zamboti Fortes,et al.  Development of transient fault management methodology , 2014 .

[46]  J.C. Salmon,et al.  A split-wound induction motor design to improve the reliability of PWM inverter drives , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[47]  Ming-Yang Cheng,et al.  Development of a Recurrent Fuzzy CMAC With Adjustable Input Space Quantization and Self-Tuning Learning Rate for Control of a Dual-Axis Piezoelectric Actuated Micromotion Stage , 2013, IEEE Transactions on Industrial Electronics.

[48]  Faa-Jeng Lin,et al.  Robust Adaptive Backstepping Motion Control of Linear Ultrasonic Motors Using Fuzzy Neural Network , 2008, IEEE Transactions on Fuzzy Systems.

[49]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[50]  Faa-Jeng Lin,et al.  Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control System , 2006, IEEE Transactions on Industrial Electronics.

[51]  Mahardhika Pratama,et al.  Evolving fuzzy rule-based classifier based on GENEFIS , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[52]  De Xu,et al.  Adding Active Learning to LWR for Ping-Pong Playing Robot , 2013, IEEE Transactions on Control Systems Technology.

[53]  Gin-Der Wu,et al.  A TS-Type Maximizing-Discriminability-Based Recurrent Fuzzy Network for Classification Problems , 2011, IEEE Transactions on Fuzzy Systems.