Because the V-belt continuously variable transmission (CVT) system spurred by permanent magnet synchronous motor (PMSM) has unknown nonlinear and time-varying properties, the better control performance design for the linear control design is a time consuming procedure. In order to conquer difficulties for design of the linear controllers, the hybrid recurrent Laguerre orthogonal polynomials neural network (NN) control system, which has online learning ability to react to unknown nonlinear and time-varying characteristics, is developed for controlling PMSM servo-driven V-belt CVT system with the lumped nonlinear load disturbances. The hybrid recurrent Laguerre orthogonal polynomials NN control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two varied learning rates of the parameters by means of modified particle swarm optimization (PSO) are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated in order to verify the effectiveness of the proposed control scheme.
Issue Section:
Research Papers
References
1.
Tseng
, C. Y.
, Chen
, L. W.
, Lin
, Y. T.
, and Li
, J. Y.
, 2008
, “A Hybrid Dynamic Simulation Model for Urban Scooters With a Mechanical-Type CVT
,” IEEE International Conference on Automation and Logistics
, Qingdao, China, September 2–4, pp. 519
–519
.2.
Tseng
, C. Y.
, Lue
, Y. F.
, Lin
, Y. T.
, Siao
, J. C.
, Tsai
, C. H.
, and Fu
, L. M.
, 2009
, “Dynamic Simulation Model for Hybrid Electric Scooters
,” IEEE International Symposium on Industrial Electronics
, Seoul, Korea, July 24, pp. 1464
–1469
.3.
Guzzella
, L.
, and Schmid
, A. M.
, 1995
, “Feedback Linearization of Spark-Ignition Engines With Continuously Variable Transmissions
,” IEEE Trans. Control Syst. Technol.
, 3
(1
), pp. 54
–58
.10.1109/87.3707104.
Kim
, W.
, and Vachtsevanos
, G.
, 2000
, “Fuzzy Logic Ratio Control for a CVT Hydraulic Module
,” IEEE Symposium on Intelligent Control
, Rio, Greece, pp. 151
–156
.5.
Carbone
, G.
, Mangialardi
, L.
, Bonsen
, B.
, Tursi
, C.
, and Veenhuizen
, P. A.
, 2007
, “CVT Dynamics: Theory and Experiments
,” Mech. Mach. Theory
, 42
(4
), pp. 409
–428
.10.1016/j.mechmachtheory.2006.04.0126.
Sattler
, H.
, 1999
, “Efficiency of Metal Chain and V-Belt CVT
,” International Conference on Continuously Variable Power Transmissions
, Eindhoven, The Netherlands, pp. 99
–104
.7.
Carbone
, G.
, Mangialardi
, L.
, and Mantriota
, G.
, 2005
, “The Influence of Pulley Deformations on the Shifting Mechanisms of MVB-CVT
,” ASME J. Mech. Des.
, 127
(1
), pp. 103
–113
.10.1115/1.18254438.
Srivastava
, N.
, and Haque
, I.
, 2009
, “A Review on Belt and Chain Continuously Variable Transmissions (CVT): Dynamics and Control
,” Mech. Mach. Theory
, 44
(1
), pp. 19
–41
.10.1016/j.mechmachtheory.2008.06.0079.
Novotny
, D. W.
, and Lipo
, T. A.
, 1996
, Vector Control and Dynamics of AC Drives
, Oxford University
, New York
.10.
Leonhard
, W.
, 1996
, Control of Electrical Drives
, 2nd ed., Springer-Verlag
, Berlin
.11.
Lin
, F. J.
, 1997
, “Real-Time IP Position Controller Design With Torque Feedforward Control for PM Synchronous Motor
,” IEEE Trans. Ind. Electron.
, 4
(3
), pp. 398
–407
.12.
Narendra
, K. S.
, and Parthasarathy
, K.
, 1990
, “Identification and Control of Dynamical Systems Using Neural Networks
,” IEEE Trans. Neural Networks
, 1
(1
), pp. 4
–26
.10.1109/72.8020213.
Sastry
, P. S.
, Santharam
, G.
, and Unnikrishnan
, K. P.
, 1994
, “Memory Neural Networks for Identification and Control of Dynamical Systems
,” IEEE Trans. Neural Networks
, 5
(2
), pp. 306
–319
.10.1109/72.27919314.
Grino
, R.
, Gembrano
, G.
, and Torras
, C.
, 2000
, “Nonlinear System Identification Using Additive Dynamic Neural Networks–Two On-Line Approaches
,” IEEE Trans. Circuits Syst. I
, 47
(2
), pp. 150
–165
.10.1109/81.82856915.
Mao
, X.
, and Hu
, H.
, 2010
, “Stability and Bifurcation Analysis of a Network of Four Neurons With Time Delays
,” ASME J. Comput. Nonlinear Dyn.
, 5
(4
), p. 041001
.10.1115/1.400031716.
Guo
, D.
, and Zhang
, Y.
, 2014
, “Neural Dynamics and Newton–Raphson Iteration for Nonlinear Optimization
,” ASME J. Comput. Nonlinear Dyn.
, 9
(2
), p. 021016
.10.1115/1.402574817.
Pao
, Y. H.
, 1989
, Adaptive Pattern Recognition and Neural Networks
, Addison-Wesley
, Boston, MA
.18.
Pao
, Y. H.
, and Philips
, S. M.
, 1995
, “The Functional Link Net and Learning Optimal Control
,” Neurocomputing
, 9
(2
), pp. 149
–164
.10.1016/0925-2312(95)00066-F19.
Patra
, J. C.
, Pal
, R. N.
, Chatterji
, B. N.
, and Panda
, G.
, 1999
, “Identification of Nonlinear Dynamic Systems Using Functional Link Artificial Neural Networks
,” IEEE Trans. Syst. Man Cybern. Part B, Cybern.
, 29
(2
), pp. 254
–262
.10.1109/3477.75279720.
Aadaleesan
, P.
, Miglan
, N.
, Sharma
, R.
, and Saha
, P.
, 2008
, “Nonlinear System Identification Using Wiener Type Laguerre–Wavelet Network Model
,” Chem. Eng. Sci.
, 63
(15
), pp. 3932
–3941
.10.1016/j.ces.2008.04.04321.
Mahmoodi
, S.
, Poshtan
, J.
, Jahed-Motlagh
, M. R.
, and Montazeri
, A.
, 2009
, “Nonlinear Model Predictive Control of a PH Neutralization Process Based on Wiener-Laguerre Model
,” Chem. Eng. J.
, 146
(3
), pp. 328
–337
.10.1016/j.cej.2008.06.01022.
Zou
, A.
, and Xiao
, X.
, 2009
, “An Asynchronous Encryption Arithmetic Based on Laguerre Chaotic Neural Networks
,” IEEE WRI Global Congress on Intelligent Systems, Xiamen, China, pp. 36
–39
.23.
Patra
, J. C.
, Bornand
, C.
, and Meher
, P. K.
, 2009
, “Laguerre Neural Network-Based Smart Sensors for Wireless Sensor Networks
,” IEEE Instrumentation and Measurement Technology Conference
, Singapore, pp. 832
–837
.24.
Patra
, J. C.
, Meher
, P. K.
, and Chakraborty
, G.
, 2011
, “Development of Laguerre Neural-Network-Based Intelligent Sensors for Wireless Sensor Networks
,” IEEE Trans. Instrum. Meas.
, 60
(3
), pp. 725
–734
.10.1109/TIM.2010.208239025.
Chow
, T. W. S.
, and Fang
, Y.
, 1998
, “A Recurrent Neural-Network-Based Real-Time Learning Control Strategy Applying to Nonlinear Systems With Unknown Dynamics
,” IEEE Trans. Ind. Electron.
, 45
(1
), pp. 151
–161
.10.1109/41.66131626.
Brdys
, M. A.
, and Kulawski
, G. J.
, 1999
, “Dynamic Neural Controllers for Induction Motor
,” IEEE Trans. Neural Networks
, 10
(2
), pp. 340
–355
.10.1109/72.75056427.
Li
, X. D.
, Ho
, J. K. L.
, and Chow
, T. W. S.
, 2005
, “Approximation of Dynamical Time-Variant Systems by Continuous-Time Recurrent Neural Networks
,” IEEE Trans. Circuits Syst. II
, 52
(10
), pp. 656
–660
.28.
Lu
, C. H.
, and Tsai
, C. C.
, 2008
, “Adaptive Predictive Control With Recurrent Neural Network for Industrial Processes: An Application to Temperature Control of a Variable-Frequency Oil-Cooling Machine
,” IEEE Trans. Ind. Electron.
, 55
(3
), pp. 1366
–1375
.10.1109/TIE.2007.89649229.
Lin
, C. H.
, 2014
, “Adaptive Recurrent Chebyshev Neural Network Control for Permanent Magnet Synchronous Motor Servo-Drive Electric Scooter
,” Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng.
, 228
(9
), pp. 699
–714
.30.
Kennedy
, J.
, and Eberhart
, R.
, 1995
, “Particle Swarm Optimization
,” IEEE International Conference on Neural Networks
, Perth, WA, pp. 1942
–1948
.31.
Goldberg
, D.
, 2002
, The Design of Innovation: Lessons from and for Competent Genetic Algorithms
, Kluwer Academic Publishers
, Norwell, MA
.32.
Clerc
, M.
, and Kennedy
, J.
, 2002
, “The Particle Swarm-Explosion, Stability, and Convergence in a Multi-Dimensional Complex space
,” IEEE Trans. Evol. Comput.
, 6
(1
), pp. 58
–73
.10.1109/4235.98569233.
Carvalho
, A. B.
, Pozo
, A.
, and Vergilio
, S. R.
, 2010
, “A Symbolic Fault-Prediction Model Based on Multi Objective Particle Swarm Optimization
,” J. Syst. Software
, 83
(5
), pp. 868
–882
.10.1016/j.jss.2009.12.02334.
Li
, Q.
, Chen
, W.
, Wang
, Y.
, Liu
, S.
, and Jia
, J.
, 2011
, “Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization
,” IEEE Trans. Ind. Electron.
, 58
(6
), pp. 2410
–2419
.10.1109/TIE.2010.206045635.
Cheng
, C. T.
, Liao
, S. L.
, Tang
, Z. T.
, and Zhao
, M. Y.
, 2009
, “Comparison of Particle Swarm Optimization and Dynamic Programming for Large Scale Hydro Unit Load Dispatch
,” Energy Convers. Manage.
, 50
(12
), pp. 3007
–3014
.10.1016/j.enconman.2009.07.02036.
Liao
, Y. X.
, She
, J. H.
, and Wu
, M.
, 2009
, “Integrated Hybrid-PSO and Fuzzy-NN Decoupling Control for Temperature of Reheating Furnace
,” IEEE Trans. Ind. Electron.
, 56
(7
), pp. 2704
–2714
.10.1109/TIE.2009.201975337.
Zitzler
, E.
, Deb
, K.
, and Thiele
, L.
, 2000
, “Comparison of Multi Objective Evolutionary Algorithms: Empirical Results
,” Evol. Comput.
, 8
(2
), pp. 173
–195
.10.1162/10636560056820238.
Eberhart
, R. C.
, and Shi
, Y.
, 1998
, “Comparison Between Genetic Algorithms and Particle Swarm Optimization
,” Proceedings of 7th International Conference Evolutionary Programming VII
, Diego, CA, pp. 611
–616
.39.
Eberhart
, R. C.
, and Shi
, Y.
, 2000
, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization
,” Proceedings of Congress on Evolutionary Computation
, La Jolla, CA, pp. 84
–88
.40.
Gao
, H.
, and Xu
, W.
, 2011
, “A New Particle Swarm Algorithm and Its Globally Convergent Modifications
,” IEEE Trans. Syst. Man Cybern., Part B: Cybern.
, 41
(5
), pp. 1334
–1351
.10.1109/TSMCB.2011.214458241.
Sun
, T. Y.
, Liu
, C. C.
, Tsai
, S. J.
, Hsieh
, S. T.
, and Li
, K. Y.
, 2011
, “Cluster Guide Particle Swarm Optimization (CGPSO) for Underdetermined Blind Source Separation With Advanced Conditions
,” IEEE Trans. Evol. Comput.
, 15
(6
), pp. 798
–811
.10.1109/TEVC.2010.204936142.
Zhang
, Y.
, Xiong
, X.
, and Zhang
, Q. D.
, 2013
, “An Improved Self-Adaptive PSO Algorithm With Detection Function for Multimodal Function Optimization Problems
,” Math. Prob. Eng.
, 2013
, p. 716952
.43.
Lin
, C. H.
, Chiang
, P. H.
, Tseng
, C. S.
, Lin
, Y. L.
, and Lee
, M. Y.
, 2010
, “Hybrid Recurrent Fuzzy Neural Network Control for Permanent Magnet Synchronous Motor Applied in Electric Scooter
,” 6th International Power Electron Conference
, Sapporo, Japan, pp. 1371
–1376
.44.
Lin
, C. H.
, and Lin
, C. P.
, 2012
, “The Hybrid RFNN Control for a PMSM Drive System Using Rotor Flux Estimator
,” Int. J. Power Electron.
, 4
(1
), pp. 33
–48
.10.1504/IJPELEC.2012.04415045.
Lin
, C. H.
, 2014
, “Hybrid Recurrent Wavelet Neural Network Control of PMSM Servo-Drive System for Electric Scooter
,” Int. J. Control Autom. Syst.
, 12
(1
), pp. 177
–187
.10.1007/s12555-012-0190-246.
Lin
, C. H.
, 2014
, “A Novel Hybrid Recurrent Wavelet Neural Network Control of PMSM Servo-Drive System for Electric Scooter
,” Turkish J. Electr. Eng. Comput. Sci.
, 22
(4
), pp. 1056
–1175
.10.3906/elk-1204-147.
Lin
, C. H.
, 2015
, “Novel Adaptive Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter
,” ASME J. Dyn. Syst. Meas. Control
, 137
(1
), p. 011010
.10.1115/1.402750748.
Ziegler
, J. G.
, and Nichols
, N. B.
, 1942
, “Optimum Settings for Automatic Controllers
,” ASME Trans.
, 64
, pp. 759
–768
.49.
Astrom
, K. J.
, and Hagglund
, T.
, 1995
, PID Controller: Theory, Design, and Tuning
, Instrument Society of America
, Research Triangle Park, NC
.50.
Hägglund
, T.
, and Åström
, K. J.
, 2002
, “Revisiting the Ziegler-Nichols Tuning Rules for PI Control
,” Asian J. Controls
, 4
(4
), pp. 364
–380
.10.1111/j.1934-6093.2002.tb00076.x51.
Hägglund
, T.
, and Åström
, K. J.
, 2004
, “Revisiting the Ziegler-Nichols Tuning Rules for PI Control—Part II: The Frequency Response Method
,” Asian J. Controls
, 6
(4
), pp. 469
–482
.10.1111/j.1934-6093.2004.tb00368.x52.
Slotine
, J. J. E.
, and Li
, W.
, 1991
, Applied Nonlinear Control
, Prentice Hall
, Englewood Cliffs, NJ
.53.
Astrom
, K. J.
, and Wittenmark
, B.
, 1995
, Adaptive Control
, Addison Wesley
, New York
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