Abstract
This paper considers the neural adaptive dynamic surface control with partially constrained tracking errors and input saturation for a class of strict-feedback nonlinear systems with uncertain parameters. An error transformation method is utilized to guarantee the prescribed performance control of the partially constrained states, which restricts the partial states located in the prescribed bounds all through. Reduced-order interceptive signals are used to solve the problem of input saturation. Neural networks are utilized to online estimate the uncertainties of the system, and dynamic surface control technique is incorporated to circumvent the complexity explosion problem. The stability of the resulted system and all the signals in the system are proved by the Lyapunov stability theorem. At last, a simulation is presented to demonstrate the effectiveness of this control scheme.
This work is supported jointly by the Fundamental Research Funds for Central Universities (No. 2016RC054) and the State Key Laboratory of Rail Traffic Control and Safety (No. RCS2017ZQ001).
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Dong, H., Wang, X., Gao, S., Wang, Y. (2017). Neural Adaptive Dynamic Surface Control of Nonlinear Systems with Partially Constrained Tracking Errors and Input Saturation. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_3
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DOI: https://doi.org/10.1007/978-3-319-59081-3_3
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