Abstract
Because the nonlinear uncertainty of the continuously variable transmission system operated by the synchronous reluctance motor is unknown, control performance obtained for classical linear controller is poor, with comparison to more complex, nonlinear control methods. Due to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization yields two varied learning rates for two parameters to find two optimal values, which helped improving convergence. Finally, various comparisons of the experimental results are demonstrated to confirm that the proposed control system can result better control performance.
Similar content being viewed by others
References
Xu, L., Yao, J.: A compensated vector control scheme of a synchronous reluctance motor including saturation and iron losses. IEEE Trans. Ind. Appl. 8(6), 1330–1338 (1992)
Betz, R.E., Lagerquist, R., Jovanovic, M., Miller, T.J.E., Middleton, R.H.: Control of synchronous reluctance machines. IEEE Trans. Ind. Appl. 9(6), 1110–1122 (1993)
Srivastava, N., Haque, I.: Transient dynamics of metal V-belt CVT: effects of bandpack slip and friction characteristic. Mech. Mach. Theory 43(4), 457–479 (2008)
Srivastava, N., Haque, I.: A review on belt and chain continuously variable transmissions (CVT): dynamics and control. Mech. Mach. Theory 44, 19–41 (2009)
Guzzella, L., Schmid, A.M.: Feedback linearization of spark-ignition engines with continuously variable transmissions. IEEE Trans. Control Syst. Technol. 3, 54–58 (1995)
Kim, W., Vachtsevanos, G.: Fuzzy logic ratio control for a CVT hydraulic module. In: Proceedings of the IEEE Symposium on Intelligent Control, pp. 151–156. Rio (2000)
Sun, G., Wang, D., Li, T., Peng, Z., Wang, H.: Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems. Nonlinear Dyn. 72(1), 175–184 (2013)
Wang, H., Chen, B., Lin, C.: Adaptive neural tracking control for a class of perturbed pure-feedback. Nonlinear Dyn. 72, 207–220 (2013)
Lin, C.H., Chen, J.K.: Nonlinear control design of LSM drive system using adaptive modified recurrent Laguerre orthogonal polynomial NN backstepping control. In: Proceedings of the 9th International Conference on Power Electronics and ECCE Asia, pp. 2110–2116. Seoul (2015)
Lin, C.H.: A backstepping control of LSM drive systems using adaptive modified recurrent Laguerre OPNNUO. J. Power Electron. 16(2), 598–609 (2016)
Lin, C.H.: PMSM servo drive for V-belt continuously variable transmission system using hybrid recurrent Chebyshev NN control system. J. Electr. Eng. Technol. 10(1), 408–421 (2015)
Belmehdi, S.: Generalized Gegenbauer orthogonal polynomials. J. Comput. Appl. Math. 133(1–2), 195–205 (2001)
Wu, C., Zhang, H., Fang, T.: Flutter analysis of an airfoil with bounded random parameters in incompressible flow via Gegenbauer polynomial approximation. Aerosp. Sci. Technol. 11(7–8), 518–526 (2007)
Zhang, Y., Li, W.: Gegenbauer neural network and its weights-direct determination method. IET Electron. Lett. 45(23), 1184–1185 (2009)
Lin, C.H.: Dynamic control for permanent magnet synchronous generator system using novel modified recurrent wavelet neural network. Nonlinear Dyn. 77(3), 1261–1284 (2014)
Duan, L., Huang, L., Guo, Z.: Stability and almost periodicity for delayed high-order Hopfield neural networks with discontinuous activations. Nonlinear Dyn. 77, 1469–1484 (2014)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. In: Technical Report TR06, Computer Engineering Department. Erciyes University, Turkey (2005)
Karaboga, D., Akay, B.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 3021–3031 (2011)
Subotic, M.: Artificial bee colony algorithm with multiple onlookers for constrained optimization problems. In: Proceeding of the European Computing Conference, pp. 251–256 (2011)
Mezura-Montes, E., Cetina-Domínguez, O.: Empirical analysis of a modified artificial bee colony for constrained numerical optimization. Appl. Math. Comput. 218(22), 10943–10973 (2012)
Stanarevic, N., Tuba, M., Bacanin, N.: Modified artificial bee colony algorithm for constrained problems optimization. Int. J. Math. Models Methods Appl. Sci. 5(3), 644–651 (2011)
Brajevic, I., Tuba, M., Subotic, M.: Performance of the improved artificial bee colony algorithm on standard engineering constrained problems. Int. J. Math. Comput. Simul. 5(2), 135–143 (2011)
Tsai, H.C.: Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Inf. Sci. 258, 80–93 (2014)
Tsai, P.W., Pan, J.S., Liao, B.Y., Chu, S.C.: Enhanced artificial bee colony optimization. Int. J. Innov. Comput. Inf. Control 5(12B), 5081–5092 (2009)
Kiran, M.S., Findik, O.: A directed artifical bee colony algorithm. Appl. Soft Comput. 26, 454–462 (2015)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)
Ahrari, A., Atai, A.A.: Grenade explosion method: a novel tool for optimization of multimodal functions. Appl. Soft Comput. 10, 1132–1140 (2010)
Mansouri, P., Asady, B., Gupta, N.: The Bisection-artificial bee colony algorithm to solve fixed point problems. Appl. Soft Comput. 26, 143–148 (2015)
Lin, C.H.: Adaptive recurrent Chebyshev neural network control for permanent magnet synchronous motor servo-drive electric scooter. Proc. IMechE I J. Syst. Control Eng. 228(9), 699–714 (2014)
Lin, C.H.: Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter. Int. J. Control Autom. Syst. 12(1), 77–187 (2014)
Lin, C.H.: Dynamic control of V-belt continuously variable transmission driven electric scooter using hybrid modified recurrent Legendre neural network control system. Nonlinear Dyn. 79(2), 787–808 (2015)
Lin, C.H.: Composite Recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization. Nonlinear Dyn. 81(3), 1219–1245 (2015)
Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64, 759–768 (1942)
Astrom, K.J., Hagglund, T.: PID Controller: Theory, Design, and Tuning. Instrument Society of America, Research Triangle Park (1995)
Hagglund, T., Astrom, K.J.: Revisiting the Ziegler–Nichols tuning rules for PI control—part II: the frequency response method. Asian J. Control 6(4), 469–482 (2004)
Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice Hall, Englewood Cliffs (1991)
Astrom, K.J., Wittenmark, B.: Adaptive Control. Addison-Wesley, New York (1995)
Lewis, F.L., Campos, J., Selmic, R.: Neuro-fuzzy control of industrial systems with actuator nonlinearities. Frontiers in Applied Mathematics. SIAM Publish Book, Philadelphia (2002). doi:10.1137/1.9780898717563
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lin, CH. Comparative dynamic control for continuously variable transmission with nonlinear uncertainty using blend amend recurrent Gegenbauer-functional-expansions neural network. Nonlinear Dyn 87, 1467–1493 (2017). https://doi.org/10.1007/s11071-016-3128-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-016-3128-z