Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

Akbari R Ziarati K (2011) A rank based particle swarm optimization algorithm with dynamic adaptation. Journal of Computational and Applied Mathematics 235(8): 2694–2714.

Angeline PJ (1998) Evolutionary optimization verses particle swarm optimization: Philosophy and the performance difference. Lecture Notes in Computer Science 1447: 600–610.

Astrom KJ Hagglund T (1995) PID Controller: Theory, Design, and Tuning. Research Triangle Park, NC: Instrument Society of America.

Astrom KJ Wittenmark B (1995) Adaptive Control. New York: Addison-Wesley.

Bartolini G Ferrara A Giacomini L , et al. (2000) Properties of a combined adaptive/second-order sliding mode control algorithm for some classes of uncertain nonlinear systems. IEEE Transactions on Automatic Control 45(7): 1334–1341.

Clerc M Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation 6(1): 58–73.

Eberhart RC Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the Congress on Evolutionary Computation, La Jolla, CA, USA, 16–19 July 2000, pp. 84–88.

Fahimi B (2017) Switched Reluctance Motor Drives: Fundamentals of Magnetic Design and Control. CRC Press.

Fei M Zhang J Hu H , et al. (2006) A novel linear recurrent neural network for multivariable system identification. Transactions of the Institute of Measurement and Control 28(3): 229–242.

Gribble JJ Kjaer PC Miller THE (1999) Optimal commutation in average torque control of switched reluctance motors. IEE Proceedings-Electric Power Applications 146(1): 2–10.

Hagglund T Astrom KJ (2002) Revisiting the Ziegler–Nichols tuning rules for PI control. Asian Journal of Control 4(4): 364–380.

Hagglund T Astrom KJ (2004) Revisiting the Ziegler–Nichols tuning rules for PI control. Part II: the frequency response method. Asian Journal of Control 6(4): 469–482.

Kanellakopoulos I Kokotovic PV Morse AS (1991) Systematic design of adaptive controller for feedback linearizable system. IEEE Transactions on Automatic Control 36: 1241–1253.

Kennedy J Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia, 27 November–1 December 1995, pp.1942–1948.

Koblara T (2008) Implementation of speed controller for switched reluctance motor drive using fuzzy logic. In: 11th International Conference on Optimization of Electrical and Electronic Equipment, Brasov, Romania, 22–24 May 2008, pp.1–6.

Ku CC Lee KY (1995) Diagonal recurrent neural networks for dynamic system control. IEEE Transactions on Neural Networks 6(1): 144–156.

Lewis FL Campos J Selmic R (2012) Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities. SIAM Frontiers in Applied Mathematics.

Li J Li S Li S , et al. (2015) Neural-network-based composite disturbance rejection control for a distillation column. Transactions of the Institute of Measurement and Control 37(9): 1146–1158.

Lin CH (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 Systems 52: 143–160.

Lin CH (2015a) Adaptive recurrent Chebyshev neural network control for PM synchronous motor servo-drive electric scooter with V-belt continuously variable transmission. International Journal of Adaptive Control and Signal Processing 29(7): 805–834.

Lin CH (2015b) Novel adaptive modified recurrent Legendre neural network control for a PMSM servo-driven electric scooter with V-belt continuously variable transmission system dynamics. Transactions of the Institute of Measurement and Control 37(10): 1181–1196.

Lin CH (2016) A backstepping control of LSM drive systems using adaptive modified recurrent Laguerre OPNNUO. Journal of Power Electronics 16(2): 598–609.

Lin CH (2017a) Blend modified recurrent Gegenbauer orthogonal polynomial neural network control for six-phase copper rotor induction motor servo-driven continuously variable transmission system using amended artificial bee colony optimization. Transactions of the Institute of Measurement and Control 39(6): 921–950.

Lin CH (2017b) Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre othognal polynomial network. International Journal of Control, Automation and Systems 15(2): 905–917.

Lin CH Hwang CC (2016) Multiobjective optimization design for a six-phase copper rotor induction motor mounted with a scroll compressor. IEEE Transactions on Magnetics 52(7): 1–4.

Lin CH Hwang CC (2017) Multi-objective optimization design using amended particle swarm optimization and Taguchi method for a six-phase copper rotor induction motor. Engineering Optimization 49(4): 693–708.

Lin CW Li THS Chen CC (2018) Feedback linearization and feedforward neural network control with application to twin rotor mechanism. Transactions of the Institute of Measurement and Control 40(2): 351–362.

Miller THE (1993) Switched Reluctance Motor Drives and Their Control. Oxford: Clarendon Press.

Montague GA Gent C Morris AJ , et al. (1996) Industrial reactor modelling with artificial neural networks. Transactions of the Institute of Measurement and Control 18(3): 118–124.

Mufti MR Butt QR Iqbal S , et al. (2016) Position control of switched reluctance motor using super twisting algorithm. Mathematical Problems in Engineering 2016: 1–9.

Raposo AP Weber HJ Alvarez Castillo DE , et al. (2007) Kirchbach M. Romanovski polynomials in selected physics problems. Central European Journal of Physics 5(3): 253–284.

Romanovski V (1929) Sur quelques classes nouvelles de polynomes orthogonaux. C. R. Academy of Sciences Paris (in French) 188: 1023–1025.

Sadehgi M Farrokhi M (2018) Online identification of non-linear dynamic systems by Wiener model using subspace method and neural networks. Transactions of the Institute of Measurement and Control 40(2): 666–674.

Shao J Deng Z Gu Y (2017) Sensorless control for switched reluctance motor based on special position detection. ISA Transactions 70: 410–418.

Shi Y Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, USA, 4–9 May 1998, pp. 69–73.

Slotine JJE Li W (1991) Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall.

Suparmi A Cari C (2015) Romanovski polynomials method and its application for non-central potential system. In: Recent Trends in Physics of Material Science and Technology Singapore: Springer, pp.123–147.

Valle Y Venayagamoorthy GK Mohagheghi S , et al. (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation 12(2): 171–195.

Vijayraghavan P (2001) Design of Switched Reluctance Motors and Development of a Universal Controller for Switched Reluctance and Permanent Magnet Brushless DC Motor Drives. Doctoral Dissertations.

Wang F Chen B Lin C , et al. (2018) Adaptive neural network finite-time output feedback control of quantized nonlinear systems. IEEE Transaction on Cybernetics 48(6): 1839–1848.

Wang F Zhang X Chen B , et al. (2017) Adaptive finite-time tracking control of switched nonlinear systems. Information Sciences 421: 126–135.

Wang JJ (2017) Adaptive inverse position control of switched reluctance motor. Applied Soft Computing 60: 48–59.

Weber HJ (2007) Connection between Romanovski polynomials and other polynomials. Central European Journal of Mathematics 5(3): 581–595.

Zheng M Li K (2014) New results on stability analysis of delayed recurrent neural networks based on the integral quadratic constraints approach. Transactions of the Institute of Measurement and Control 36(6): 780–788.