Abstract
In this paper, an adaptive neural networks control approach is proposed for a class of multi-input multi-output (MIMO) non-affine nonlinear dynamic systems in the presence of input saturation. The difficulty in controlling the saturated non-affine system is overcome by introducing a system transformation, so as the system can be reformulated as an affine of a canonical system. In the control design, neural networks are used in the online learning of the unknown dynamics and the input saturation is approximated to reduce the influence caused by the nonlinearities, and a robustifying control term is used to compensate for the approximation errors. Compared to the literature, in the proposed approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. The stability analysis of the closed-loop system is investigated by using Lyapunov theory. Numerical simulation illustrated the proposed control scheme with satisfactory results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boulkroune, A., M’Saad, M.: A fuzzy adaptive variable-structure control scheme for uncertain chaotic MIMO systems with sector nonlinearities and dead-zones. Expert Syst. Appl. 38(12), 1447–14750 (2011)
Boulkroune, A., Tadjine, M., M’Saad, M., Farza, M.: Fuzzy approximation based indirect adaptive controller for multi-input multi-output Non-affine systems with unknown control direction. IET Control Theory Appl. 6(17), 2619–2629 (2012)
Wen, C., Zhou, J., Liu, Z., Su, H.: Robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance. Autom. Control IEEE Trans. 56(7), 1672–1678 (2011)
Chen, C.-S.: Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems. Inf. Sci. 179(15), 2676–2688 (2009)
Su, C.-Y., Oya, M., Hong, H.: Stable adaptive fuzzy control of nonlinear systems preceded by unknown backlash-like hysteresis. IEEE Trans. Fuzzy Syst. 11(1), 1–8 (2003)
Doudou, S., Khaber, F.: Direct adaptive fuzzy control of a class of MIMO non- affine nonlinear systems. Int. J. Syst. Sci. 43(6), 1029–1038 (2012)
Du, H., Chen, X.: NN-based output feedback adaptive variable structure control for a class of non-affine nonlinear systems: a nonseparation principle design. Neurocomputing 72(7–9), 2009–2016 (2009)
Esfandiari, K., Abdollahi, F., Talebi, H.A.: Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks. IEEE Trans. Neural Netw. Learn. Syst. 26(10), 2311–2322 (2015)
Liu, Y., Wang, W.: Adaptive fuzzy control for a class of uncertain non-affine nonlinear systems. Inf. Sci. 177, 3901–3917 (2007)
Chen, M., Zou, J., Feng, X., Jiang, C.: Approximation-based tracking control of uncertain MIMO nonlinear systems with input saturation. In: Proceedings of the 29th Chinese Control Conference, pp. 6155–6160 (2010)
Gupta, M.M., Rao, D.H.: Neuro-Control Systems: Theory and Applications. IEEE Press, New York (1994)
Min, W., Cong, W., Siying, Z.: direct adaptive neural control of completely non-affine pure-feedback nonlinear systems with small-gain approach. In: Chinese Control and Decision Conference (CCDC) (2009)
Ioannou, P.A.: Robust Adaptive Control. Prentice-Hall, Upper Saddle River (1984)
He, P., Jagannathan, S.: Reinforcement learning-based output feedback control of nonlinear systems with input constraints. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(1), 150–154 (2005)
Shahnazi, R.: Observer-based adaptive interval type-2 fuzzy control of uncertain MIMO nonlinear systems with unknown asymmetric saturation actuators. Neurocomputing 171, 1053–1065 (2016)
Shahnazi, R.: Output feedback adaptive fuzzy control of uncertain MIMO nonlinear systems with unknown input nonlinearities. ISA Trans. 54, 39–51 (2015)
Shuzhi, S.G., Chenguang, Y., Tong, H.: Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time. IEEE Trans. Neural Netw. 19(9), 1599–1614 (2008)
Tong, S., Li, Y.: Adaptive fuzzy output feedback control of MIMO nonlinear systems with unknown dead-zone inputs. IEEE Trans. Fuzzy Syst. 21(1), 134–146 (2013)
Shenglin, W., Ye, Y.: Adaptive fuzzy neural network control for a class of uncertain MIMO nonlinear systems via sliding-mode design. In: Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2 (2014)
Wang, W., Chien, L., Li, I., Su, S.: MIMO Robust control via T-S fuzzy models for non-affine nonlinear systems. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1–6 (2007)
Wang, W.-J., Hong, C.-M., Kuo, M.-F., Leu, Y.-G., Lee, T.-T.: RBF neural network adaptive backstepping controllers for MIMO nonaffine nonlinear systems. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, USA, October 2009
Yang, Y., Yue, D., Xue, Y.: Decentralized adaptive neural output feedback control of a class of large-scale time-delay systems with input saturation. J. Frankl. Inst. 352(5), 2129–2151 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nassira, Z., Mohamed, C., Essounbouli, N. (2019). Adaptive Neural Control Design of MIMO Nonaffine Nonlinear Systems with Input Saturation. In: Chadli, M., Bououden, S., Ziani, S., Zelinka, I. (eds) Advanced Control Engineering Methods in Electrical Engineering Systems. ICEECA 2017. Lecture Notes in Electrical Engineering, vol 522. Springer, Cham. https://doi.org/10.1007/978-3-319-97816-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-97816-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97815-4
Online ISBN: 978-3-319-97816-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)