Adaptive Neural Network Control in Bilateral Teleoperation Systems with Full-state Constraints
Yuan,Shuo1; Yu,Xinbo2; Yang,Yongliang3; Silvestre,Carlos3; He,Wei1
Source PublicationProceedings - 2020 Chinese Automation Congress, CAC 2020
AbstractThis paper aims at proposing an adaptive neural networks control architecture for constrained bilateral teleoperation systems with delay. To achieve full-state constrained conditions, a cot-type barrier Lyapunov functions (BLFs) is built to analyze the stability of systems. The radial basis function neural networks (RBFNNs) are applied to estimate uncertainties in dynamic parameters and improve robustness of systems. Under the proposed control strategy, the stability of systems is proved, and the tracking performance is guaranteed considering full-state constraints and uncertainties. The simulation verifies the effectiveness of designed control scheme.
Keywordbarrier lyapunov function Bilateral teleoperation systems full-state constraints neural networks time delay uncertainties
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Document TypeConference paper
Affiliation1.University of Science and Technology Beijing,School of Automation and Electrical Engineering,Beijing,100083,China
2.Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing,100083,China
3.University of Macau,Department of Electrical and Computer Engineering,Faculty of Science and Technology,Macao
Recommended Citation
GB/T 7714
Yuan,Shuo,Yu,Xinbo,Yang,Yongliang,et al. Adaptive Neural Network Control in Bilateral Teleoperation Systems with Full-state Constraints[C],2020:6794-6799.
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