Abstract
This paper presents adaptive neural control (ANC) design for a class of nonaffine nonlinear systems with full state constraints. A novel transformed function is presented to convert the origin system into an equivalent nonaffine systems with new unconstrained states. By combining dynamic surface control, the explosion of complexity is avoided in the backstepping design. Subsequently, a novel ANC control scheme is proposed by Lyapunov synthesis. The proposed adaptive control guarantees that all closed-loop signals are uniformly ultimately bounded and all system states do not violate the predefined constraints. Simulation studies are performed to show the effectiveness of the proposed control scheme.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.: Constrained model predictive control: stability and optimality. Automatica 36(6), 789–814 (2000)
DeHaan, D., Guay, M.: Extremum-seeking control of state-constrained nonlinear systems. Automatica 41(9), 1567–1574 (2005)
Krstic, M., Bement, M.: Non-overshooting control of strict-feedback nonlinear systems. In: American Control Conference, pp. 4494–4499. IEEE Press (2007)
Do, K.D.: Control of nonlinear systems with output tracking error constraints and its application to magnetic bearings. Int. J. Control 83(6), 1199–1216 (2010)
Wang, M., Wang, C., Shi, P., Liu, X.: Dynamic learning from neural control for strict-feedback systems with guaranteed predefined performance. IEEE Trans. Neural Netw. Learn. Syst. 27(12), 2564–2576 (2016)
Dai, S.-L., Wang, M., Wang, C.: Neural learning control of marine surface vessels with guaranteed transient tracking performance. IEEE Trans. Industr. Electron. 63(3), 1717–1727 (2016)
Tee, K.P., Ge, S.S., Tay, E.H.: Barrier Lyapunov functions for the control of output-constrained nonlinear systems. Automatica 45(4), 918–927 (2009)
Li, J., Liu, Y.J.: Control of nonlinear systems with full state constraints using integral Barrier Lyapunov Functionals. In: International Conference on IEEE Informative and Cybernetics for Computational Social Systems, pp. 66–71 (2015)
Kanellakopoulos, I., Kokotovic, P.V., Morse, A.S.: Systematic design of adaptive controllers for feedback linearizable systems. In: American Control Conference, pp. 649–654. IEEE (1991)
Yip, P.P., Hedrick, J.K.: Adaptive dynamic surface control: a simplified algorithm for adaptive backstepping control of nonlinear systems. Int. J. Control 71(5), 959–979 (1998)
Swaroop, D., Hedrick, J.K., Yip, P.P., Gerdes, J.C.: Dynamic surface control for a class of nonlinear systems. IEEE Trans. Autom. Control 45(10), 1893–1899 (2000)
Ge, S.S., Wang, C.: Adaptive NN control of uncertain nonlinear pure-feedback systems. Automatica 38(4), 671–682 (2002)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China under Grants 61773169, 61374119, 61473121, and 61611130214, by the Guangdong Natural Science Foundation under Grant 2014A030312005, and by the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wang, M., Zhang, Y. (2017). Guaranteeing Predefined Full State Constraints for Non-Affine Nonlinear Systems Using Neural Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-70136-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70135-6
Online ISBN: 978-3-319-70136-3
eBook Packages: Computer ScienceComputer Science (R0)