Paper Info
Title
Observer-Based NN Control for Nonlinear Systems With Full-State Constraints and External Disturbances
Abstract
For full-state constrained nonlinear systems with input saturation, this article studies the output-feedback tracking control under the condition that the states and external disturbances are both unmeasurable. A novel composite observer consisting of state observer and disturbance observer is designed to deal with the unmeasurable states and disturbances simultaneously. Distinct from the related literature, an auxiliary system with approximate coordinate transformation is used to attenuate the effects generated by input saturation. Then, using radial basis function neural networks (RBF NNs) and the barrier Lyapunov function (BLF), an opportune backstepping design procedure is given with employing the dynamic surface control (DSC) to avoid the problem of “explosion of complexity.” Based on the given design procedure, an output-feedback controller is constructed and guarantees all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. It is shown that the tracking error is regulated by the saturated input error and design parameters without the violation of the state constraints. Finally, a simulation example of a robot arm is given to demonstrate the effectiveness of the proposed controller.
Year
DOI
Venue
2022
10.1109/TNNLS.2021.3056524
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
External disturbances,full-state constraints,input saturation,nonlinear systems,radial basis function neural network (RBF NN)
Journal
33
Issue
ISSN
Citations 
9
2162-237X
2
PageRank 
References 
Authors
0.35
34
4
Name
Order
Citations
PageRank
Huifang Min1479.39
Shengyuan Xu23541251.22
Shu-Min Fei3115096.93
Xin Yu4874.23