Novel adaptive modified recurrent Legendre neural network control for a PMSM servo-driven electric scooter with V-belt continuously variable transmission system dynamics
Restricted accessResearch articleFirst published online November, 2015
Novel adaptive modified recurrent Legendre neural network control for a PMSM servo-driven electric scooter with V-belt continuously variable transmission system dynamics
Because of the unknown nonlinearity and time-varying characteristics of a V-belt continuously variable transmission (CVT) driven electric scooter system using a permanent magnet synchronous motor (PMSM) servo drive, its accurate dynamic model is difficult to establish for the design of the linear controller in the whole system. In order to conquer this difficulty and increase robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system is proposed for controlling a PMSM servo-driven electric scooter with a V-belt CVT system. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a compensated control with estimation law. Additionally, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the compensated control can be derived by using the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Legendre NN are proposed according to a discrete-type Lyapunov function in order to raise the speed of convergence. Finally, comparative studies are provided by experimental results in order to show the effectiveness of the proposed control scheme.
AstromKJHagglundT (1995) PID Controller: Theory, Design, and Tuning. Research Triangle Park, NC: Instrument Society of America.
2.
AstromKJWittenmarkB (1995) Adaptive Control. New York: Addison-Wesley.
3.
BrdysMAKulawskiGJ (1999) Dynamic neural controllers for induction motor. IEEE Transactions on Neural Networks10: 340–355.
4.
ChowTWSFangY (1998) A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics. IEEE Transactions on Industrial Electronics45: 151–161.
5.
DasKKSatapathyJK (2012) Novel algorithms based on Legendre neural network for nonlinear active noise control with nonlinear secondary path. International Journal of Computer Science and Information Technology3: 5036–5039.
6.
DehuriSChoSB (2010) A comprehensive survey on functional link neural networks and an adaptive PSOBP learning for CFLNN. Neural Computing Applications9: 187–205.
7.
ElmanJL (1990) Finding structure in time. Cognitive Science2: 179–211.
8.
FeiMZhangJHuH. (2006) A novel linear recurrent neural network for multivariable system identification. Transactions of the Institute of Measurement and Control28: 229–242.
9.
GaoXZGaoXMOvaskaSJ (1996) A modified Elman neural network model with application to dynamical systems identification. In: IEEE international conference on system, man and cybernetics, Beijing, China, 14–17 October 1996, pp.1376–1381.
10.
GrinoRCembranoGTorrasC (2000) Nonlinear system identification using additive dynamic neural networks–two on-line approaches. IEEE Transactions on Circuits and Systems I: Regular Papers471: 150–165.
11.
HagglundTAstromKJ (2002) Revisiting the Ziegler–Nichols tuning rules for PI control. Asian Journal of Control4: 364–380.
12.
HagglundTAstromKJ (2004) Revisiting the Ziegler–Nichols tuning rules for PI control. Part II: the frequency response method. Asian Journal of Control6: 469–482.
13.
LeonhardW (1996) Control of Electrical Drives. Berlin: Springer-Verlag.
14.
LiJLiSChenX (2012) Adaptive speed control of a PMSM servo system using an RBFN disturbance observer. Transactions of the Institute of Measurement and Control34: 615–626.
15.
LiJWuHPianJ (2011) The application of the equipment fault diagnosis based on modified Elman neural network. In: IEEE international conference on electronic, mechanical engineering and information technology, Heilongjiang, China, 12–14 August 2011, pp.4135–4137.
16.
LiXDHoJKLChowTWS (2005) Approximation of dynamical time-variant systems by continuous-time recurrent neural networks. IEEE Transactions on Circuits and Systems II: Express Briefs52: 656–660.
17.
LinCH (2012) A novel hybrid recurrent wavelet neural network control for a PMSM drive electric scooter using rotor flux estimator. International Review of Electrical Engineering7: 4486–4498.
18.
LinCH (2013) Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive. International Journal of Electrical Power and Energy Systems52: 143–160.
19.
LinCHLinCP (2012) The hybrid RFNN control for a PMSM drive system using rotor flux estimator. International Journal of Power Electronics4: 33–48.
20.
LinCHChiangPHTsengCS. (2010) Hybrid recurrent fuzzy neural network control for permanent magnet synchronous motor applied in electric scooter. In: International power electronics conference, Sapporo, Japan, 21–24 June 2010, pp.1371–1376.
21.
LinFJ (1997) Real-time IP position controller design with torque feedforward control for PM synchronous motor. IEEE Transactions on Industrial Electronics41: 398–407.
22.
LiuFWangJ (2012) Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing83: 12–21.
23.
LuCH (2009) Design and application of stable predictive controller using recurrent wavelet neural networks. IEEE Transactions on Industrial Electronics56: 3733–3742.
24.
LuCHTsaiCC (2008) Adaptive predictive control with recurrent neural network for industrial processes: an application to temperature control of a variable-frequency oil-cooling machine. IEEE Transactions on Industrial Electronics55: 1366–1375.
25.
NovotnyDWLipoTA (1996) Vector Control and Dynamics of AC Drives. New York: Oxford University Press.
PaoYHPhilipsSM (1995) The functional link net and learning optimal control. Neurocomputing9: 149–164.
28.
PatraJCBornandC (2010) Nonlinear dynamic system identification using Legendre neural network. In: International joint conference on neural networks, Barcelona, Spain, 18–23 July 2010, pp.1–7.
29.
PatraJCChinWCMeherPK. (2008) Legendre-FLANN-based nonlinear channel equalization in wireless communication systems. In: IEEE international conference on system, man and cybernetics, Beijing, China, 14–17 October 1996, pp.1826–1831.
30.
PatraJCMeherPKChakrabortyG (2009) Nonlinear channel equalization for wireless communication systems using Legendre neural networks. Signal Processing89: 2251–2262.
31.
PatraJCPalRNChatterjiBN. 1999: Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics29: 254–262.
32.
SadatiSHParvarMSMenhajMB. (2007) Backstepping controller design using neural networks for a fighter aircraft. European Journal of Control13: 516–526.
33.
SastryPSSantharamGUnnikrishnanKP (1994) Memory neural networks for identification and control of dynamical systems. IEEE Transactions on Neural Networks5: 306–319.
SuWZhuYLiuF. (2013) An online outlier detection method based on wavelet technique and robust RBF network. Transactions of the Institute of Measurement and Control35: 1046–1057.
36.
TsengCYChenLWLinYT. (2008) A hybrid dynamic simulation model for urban scooters with a mechanical-type CVT. In: IEEE international conference on automation and logistics, Qingdao, China, 1–3 September 2008, pp. 515–519.
37.
TsengCYLueYFLinYT. (2009) Dynamic simulation model for hybrid electric scooters. In: IEEE international symposium on industrial electronics, Seoul, Korea, 5–8 July 2009, pp.1464–1469.
38.
WangDBaiYZhaoJ (2010) Robot manipulator calibration using neural network and a camera-based measurement system. Transactions of the Institute of Measurement and Control34: 105–121.
39.
YangSSTsengCS (1996) An orthogonal neural network for function approximation. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics26: 779–785.
40.
YangXYXuDPHanXJ. (2005) Predictive functional control with modified Elman neural network for reheated steam temperature. In: IEEE international conference on machine learning and cybernetics, Guangzhou, China, 18–21 August 2005, pp.4699–4703.
41.
YooSJParkJBChoiYH (2005) Stable predictive control of chaotic systems using self-recurrent wavelet neural network. International Journal of Automatic Control Systems3: 43–55.
