Data-driven robust control of unknown MIMO nonlinear system subject to input saturations and disturbances
type: Item
instance of: scholarly article
title: Data-driven robust control of unknown MIMO nonlinear system subject to input saturations and disturbances
zbMATH Open document ID: 1426.93107
DOI: 10.1155/2017/5186025
author: Li Wang
author: Chunsheng Liu
author: Huajun Gong
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93B51
Mathematics Subject Classification ID: 93C40
zbMATH DE Number: 6972192
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/5186025
OpenAlex ID: W2751994802
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description: scientific article; zbMATH DE number 6972192