A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system

References

• Ahmed-Ali, T., Kenne, G., & Lamnabhi-Lagarrigue, F. (2009). Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer. Neurocomputing, 72, 1611–1620.
   Web of Science ®Google Scholar
• Ahmed-Ali, T., Lamnabhi-Lagarrigue, F., & Ortega, R. (1999). A globally-stable adaptive indirect field-oriented controller for current-fed induction motors. International Journal of Control, 72, 996–1005.
   Web of Science ®Google Scholar
• Akatsu, K., & Kawamura, A. (2000). On-line rotor resistance estimation using the transient state under the speed sensorless control of induction motor. IEEE Transaction on Power Electronics, 15, 553–560.
   Web of Science ®Google Scholar
• Barut, M., Bogosyan, S., & Gokasan, M. (2005). Speed sensorless direct torque control of induction motors with rotor resistance estimation. Energy Conversion and Management, 46, 335–349.
   Web of Science ®Google Scholar
• Beltran, B., Ahmed-Ali, T., & Benbouzid, M. (2009). High-order sliding mode control of variable speed wind Turbines. IEEE Transactions on Energy Conversion, 56, 3314–3321.
   Google Scholar
• Bouafassa, A., Rahmani, L., & Mekhilef, S. (2015). Design and real time implementation of single phase boost power factor correction converter. ISA Transactions, 55, 267–274.
   PubMed Web of Science ®Google Scholar
• Castaldi, P., Geri, W., Montanari, M., & Tilli, A. (2005). A new adaptive approach for on-line parameter and state estimation of induction motors. Control Engineering Practice, 13, 81–94.
   Web of Science ®Google Scholar
• Castillo, B., Gennaro, S.D., Loukianov, A., & Rivera, J. (2007). Robust nested sliding mode regulation with application to induction motors. Proceedings of the 2007 American control conference, New York City, USA (pp. 5242–5247).
   Google Scholar
• Chen, C., & Billings, S.A. (1992). Neural networks for nonlinear dynamic system modelling and identification. International Journal of Control, 56, 319–346.
   Web of Science ®Google Scholar
• Chen, C., Billings, S.A., Cowan, C.F.N., & Grant, P.M. (1990). Practical identification of NARMAX models using radial basis functions. International Journal of Control, 52, 1327–1350.
   Web of Science ®Google Scholar
• Emelyanov, S.V., Korovin, S.K., & Levantovsky, L.V. (1986a). Higher order sliding modes in the binary control systems. Soviet Physics, Doklady, 31, 291–293.
   Google Scholar
• Emelyanov, S.V., Korovin, S.K., & Levantovsky, L.V. (1986b). Second order sliding modes in controlling uncertain systems. Soviet Journal of Computer and System Science, 24, 63–68.
   Web of Science ®Google Scholar
• Fei, J., & Ding, H. (2012). Adaptive sliding mode control of dynamic system using RBF neural network. Nonlinear Dynamics, 70, 1563–1573.
   Web of Science ®Google Scholar
• Fridman, L., & Levant, A. (2002). Sliding mode control in engineering. Higher order sliding modes, New York, NY: Marcel Dekker.
   Google Scholar
• Ge, S.S., Hang, C.C., & Zhang, T. (1999). A direct method for robust adaptive nonlinear control with guaranteed transient performance. Systems and Control Letters, 37, 275–284.
   Web of Science ®Google Scholar
• Ge, S., & Wang, C. (2002). Direct adaptive NN control of a class of nonlinear systems. IEEE Trasaction on Neural Networks, 13, 214–221.
   PubMed Web of Science ®Google Scholar
• Haque, M., Negnevitsky, M., & Muttaqi, K. (2008). A novel control strategy for a variable speed wind turbine with a permanent magnet synchronous generator. 2008 IEEE Industry Applications Society annual meeting (pp. 1–8), University of Wollongong, Wollongong, NSW.
   Google Scholar
• Hua, C., Yu, C., & Guan, X. (2014). Neural network observer-based networked control for a class of nonlinear systems. Neurocomputing, 133, 103–110.
   Web of Science ®Google Scholar
• Jaramillo-Lopez, F., Kenne, G., & Lamnabhi-Lagarrigue, F. (2016). A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction. Renewable Energy, 86, 38–48.
   Web of Science ®Google Scholar
• Kenne, G., Ahmed-Ali, T., Lamnabhi-Lagarrigue, F., & Arzandé, A. (2008). Nonlinear systems time-varying parameter estimation: Application to induction motors. Electric Power Systems Research, 78, 1881–1888.
   Web of Science ®Google Scholar
• Kenné, G., & Lamnabhi-Lagarrigue, F. (2012). Comparative study of two robust online rotor resistance estimators for induction machine adaptive control. Proceeding of the IEEE international conference on industrial technology, special session paper, Athens, Greece, 19–21 March (pp. 437–442).
   Google Scholar
• Kenne, G., Simo, R.S., Lamnabhi-Lagarrigue, F., Arzande, A., & Vannier, J.C. (2010). An online simplified rotor resistance estimator for induction motors. IEEE Transaction on Control Systems Technology, 18, 1188–1194.
   Web of Science ®Google Scholar
• Koubaa, Y. (2006a). Application of least-squares techniques for induction motor parameters estimation. Mathematical and Computer Modelling of Dynamical Systems, 12, 363–375.
   Web of Science ®Google Scholar
• Koubaa, Y. (2006b). Asynchronous machine parameters estimation using recursive method. Simulation Modelling Practice and Theory, 14, 1010–1021.
   Web of Science ®Google Scholar
• Leonhard, W. (1984). Control of electric drives. New York: Springer Verlag.
   Google Scholar
• Levant, A. (1998). Robust exact differentiation via sliding mode technique. Automatica, 34, 379–384.
   Web of Science ®Google Scholar
• Levant, A., & Alelishvili, L. (2007). Integral high-order sliding modes. IEEE Transactions on Automatic Control, 52, 1278–1282.
   Google Scholar
• Liu, J. (2013). Radial basis function (RBF) neural network control for mechanical systems, design, analysis and matlab simulation. Heidelberg: Springer.
   Google Scholar
• Louze, L., Nemmour, A.L., Khezzar, A., Hacil, M.E., & Boucherma, M. (2009). Cascade sliding mode controller for self-excited induction generator. Revue des Energies Renouvelables, 12, 617–626.
   Google Scholar
• Lu, Y.S., & Chen, J.S. (1995). Design of a perturbation estimator using the theory of variable-structure systems and its application to magnetic levitation systems. IEEE Transactions on Industrial Electronics, 42, 281–289.
   Web of Science ®Google Scholar
• Luenberger, D.G. (1984). Linear and nonlinear programming. Boston, MA: Addison-Wesley.
   Google Scholar
• Mansour, M., Nejib Mansouri, M., & Faouzi Mimouni, M. (2011). Comparative performance of fixed-speed and variable-speed wind turbine generator systems. Journal of Mechanics Engineering and Automation, 1, 74–81.
   Google Scholar
• Marino, R., Peresada, S., & Tomei, P. (1995). Exponentially convergent rotor resistance estimation for induction motors. IEEE Transaction on Industrial Electronics, 42, 508–515.
   Web of Science ®Google Scholar
• Marino, R., Peresada, S., & Tomei, P. (2000). On-line stator and rotor resistance estimation for induction motors. IEEE Transactions on Control Systems Technology, 8, 570–579.
   Web of Science ®Google Scholar
• Marino, R., Peresada, S., & Verrelli, C.M. (2005). Adaptive control for speed-sensorless induction motors with uncertain load torque and rotor resistance. International Journal of Adaptive Control and Signal Processing, 19, 661–685.
   Web of Science ®Google Scholar
• Marino, R., & Tomei, P. (1995). Nonlinear adaptive design: Geometric, adaptive, and robust. London: Prentice-Hall.
   Google Scholar
• Mezouar, A., Fellah, M.K., & Hadjeri, S. (2008). Adaptive sliding-mode-observer for sensorless induction motor drive using two-time-scale approach. Simulation Modelling Practice and Theory, 16, 1323–1336.
   Web of Science ®Google Scholar
• Mezouar, A., Fellah, M.K., Hadjeri, S., & Sahali, Y. (2006). Adaptive speed sensorless vector control of induction motor using singularly perturbed sliding mode observer. IEEE Industrial Electronics-IECON 2006 (pp. 932–939), Paris, France.
   Google Scholar
• Pai, M.C. (2014). Dynamic output feedback RBF neural network sliding mode control for robust tracking and model following. Nonlinear Dynamics, 79, 1023–1033.
   Web of Science ®Google Scholar
• Picardi, C., & Scibilia, F. (2006). Sliding-mode observer with resistances or speed adaptation for field-oriented induction motor drives. IEEE Industrial Electronics-IECON 2006 (pp. 1481–1486), Paris, France.
   Google Scholar
• Roncero-Sánchez, P., García-Cerraba, A., & Feliu-Batlle, V. (2007). Rotor resistance estimation for induction machines with indirect field-orientation. Control Engineering Practice, 15, 1119–1133.
   Web of Science ®Google Scholar
• Seixas, V.M.M., & Melício, R. (2014). Fifth harmonic and sag impact on PMSG wind turbines with a balancing strategy for capacitor voltages. Energy Conversion and Management, 79, 721–730.
   Web of Science ®Google Scholar
• Seshagiri, S., & Khalil, H. (2000). Output feedback control of nonlinear systems using RBF neural networks. IEEE Trasaction on Neural Networks, 11, 69–79.
   PubMed Web of Science ®Google Scholar
• Seyoum, D., Rahman, M.F., & Grantham, C. (2003). Inverter supplied voltage control system for an isolated induction generator driven by a wind turbine. 38th IAS annual meeting conference record of the industry applications (Vol. 1, pp. 568–575). IEEE Xplore.
   Google Scholar
• Sivakami, P., Karthigaivel, R., & Selvakumaran, S. (2013). Voltage control of variable speed induction generator using PWM converter. International Journal of Engineering and Advanced Technology, 2, 2249–8958.
   Google Scholar
• Slootweg, J., de Haan, S., Polinder, H., & Kling, W. (2003). General model for representing variable speed wind turbines in power system dynamics simulations. IEEE Transactions on Power Systems, 18, 144–151.
   Web of Science ®Google Scholar
• Thumati, B., & Jagannathan, S. (2009). Neural network control of a class of nonlinear discrete time systems with asymptotic stability guarantees. American control conference, Hyatt Regency Riverfront, St. Louis, MO, USA, 10–12 June (pp. 2934–2939).
   Google Scholar
• Wang, K., Chiasson, J., Bodson, M., & Tolbert, L.M. (2007). An online rotor time constant estimator for the induction machine. IEEE Transactions on Control Systems Technology, 15, 339–348.
   Web of Science ®Google Scholar
• Yoo, D.S., & Chung, M.J. (1992). A variable structure control with simple adaptation laws for upper bounds on the norm of the uncertainties. IEEE Transaction on Automatic Control, 37, 860–864.
   Web of Science ®Google Scholar
• Zhu, Q., Fei, S., Zhang, T., & Li, T. (2008). Adaptive RBF neural-networks control for a class of time-delay nonlinear systems. Neurocomputing, 71, 3617–3624.
   Web of Science ®Google Scholar