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Politecnico di Milano
This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.
Kangkang Sun, Jianbin Qiu, Hamid Reza Karimi, Huijun Gao (2019). A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems With Tracking Error Constraint. IEEE Transactions on Systems Man and Cybernetics Systems, 51(6), pp. 3968-3979, DOI: 10.1109/tsmc.2019.2958072.
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Type
Article
Year
2019
Authors
4
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Systems Man and Cybernetics Systems
DOI
10.1109/tsmc.2019.2958072
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