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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 5186025, 11 pages
Research Article

Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances

1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2Nanhang Jincheng College, Nanjing 211156, China

Correspondence should be addressed to Li Wang

Received 15 June 2017; Revised 8 August 2017; Accepted 9 August 2017; Published 10 September 2017

Academic Editor: Asier Ibeas

Copyright © 2017 Li Wang et al. 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.


This paper presented a new data-driven robust control scheme for unknown nonlinear systems in the presence of input saturation and external disturbances. According to the input and output data of the nonlinear system, a recurrent neural network (RNN) data-driven model is established to reconstruct the dynamics of the nonlinear system. An adaptive output-feedback controller is developed to approximate the unknown disturbances and a novel input saturation compensation method is used to attenuate the effect of the input saturation. Under the proposed adaptive control scheme, the uniformly ultimately bounded convergence of all the signals of the closed-loop nonlinear system is guaranteed via Lyapunov analysis. The simulation results are given to show the effectiveness of the proposed data-driven robust controller.