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Volume 2017, Article ID 6893521, 11 pages
Research Article

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

1College of Science, Liaoning University of Technology, Jinzhou, Liaoning 121001, China
2School of Chemical and Environmental Engineering, Liaoning University of Technology, Jinzhou, Liaoning 121001, China

Correspondence should be addressed to Dong-Juan Li; moc.evil@naujgnodil

Received 7 July 2017; Accepted 29 August 2017; Published 18 October 2017

Academic Editor: Junpei Zhong

Copyright © 2017 Shu-Min Lu and Dong-Juan Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.