Neural Computing and Applications

, Volume 29, Issue 7, pp 565–576 | Cite as

Adaptive finite-time control of a class of non-triangular nonlinear systems with input saturation

  • Mingjie Cai
  • Zhengrong Xiang
Original Article


This paper studies the problem of adaptive neural network finite-time control for a class of non-triangular nonlinear systems with input saturation. Under the assumption that the nonlinearities have strict increasing smooth bounding functions, the backstepping technique can be used to design the state feedback controller and adaptive laws. Neural networks are adopted to approximate some unknown nonlinear functions. With the help of the finite-time Lyapunov stability theorem, it can be proved that the state of the closed-loop system can converge to an arbitrarily small neighborhood of the origin in a finite time. Finally, a numerical simulation example is given to show the effectiveness of the proposed design method.


Finite-time control Non-triangular nonlinear systems Input saturation Adaptive control Backstepping 



This work was supported by the National Natural Science Foundation of China under Grant No. 61273120.

Compliance with ethical standards

Conflict of interest

No potential conflict of interest was reported by the authors.


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.School of AutomationNanjing University of Science and TechnologyNanjingChina

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