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Neural Computing and Applications

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01.12.2014 | Original Article

Backstepping control for output-constrained nonlinear systems based on nonlinear mapping

verfasst von: Tao Guo, Xiaowei Wu

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

In this paper, nonlinear mapping (NM)-based backstepping control design is presented for a class of strict-feedback nonlinear systems with output constraint. By mapping output value set onto the set of all real numbers, the constrained system is transformed into a new strict-feedback unconstrained system to employ the traditional backstepping control while simultaneously preventing the constraint from being violated. It is proved that the original system has the similar convergence and bounded properties with the new one. Besides the nominal case where full knowledge of the plant is available, we also tackle scenarios wherein parametric uncertainties are present. Furthermore, the comparison with barrier Lyapunov function-based algorithm reveals the advantages of NM algorithm. The closed-loop system is guaranteed to be stable in the sense that all signals involved remain bounded, and the tracking error converges to zero asymptotically. Simulation studies illustrate the performance of the proposed control.

Vorheriger Artikel Mean square input-to-state stability of a general class of stochastic recurrent neural networks with Markovian switching

Nächster Artikel An improved PROMETHEE II method based on Axiomatic Fuzzy Sets

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Titel: Backstepping control for output-constrained nonlinear systems based on nonlinear mapping
verfasst von: Tao Guo
Xiaowei Wu
Publikationsdatum: 01.12.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1650-9

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