Decentralized Output Feedback Adaptive NN Tracking Control for Time-Delay Stochastic Nonlinear Systems With Prescribed Performance

IEEE Transactions on Neural Networks and Learning Systems
Chang-Chun HuaXin-Ping Guan

Abstract

This paper studies the dynamic output feedback tracking control problem for stochastic interconnected time-delay systems with the prescribed performance. The subsystems are in the form of triangular structure. First, we design a reduced-order observer independent of time delay to estimate the unmeasured state variables online instead of the traditional full-order observer. Then, a new state transformation is proposed in consideration of the prescribed performance requirement. Using neural network to approximate the composite unknown nonlinear function, the corresponding decentralized output tracking controller is designed. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of uniformly ultimately boundedness and that both transient-state and steady-state performances are preserved. Finally, a simulation example is given, and the result shows the effectiveness of the proposed control design method.

References

Mar 29, 2006·IEEE Transactions on Neural Networks·Hongbin DuPingjing Yao
Mar 28, 2007·IEEE Transactions on Neural Networks·Changchun HuaPeng Shi
Nov 26, 2009·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Weisheng ChenRuihong Li
Oct 7, 2010·IEEE Transactions on Neural Networks·Charalampos P BechlioulisGeorge A Rovithakis
Jun 29, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Artemis K Kostarigka, George A Rovithakis
Jan 1, 2013·IEEE Transactions on Neural Networks and Learning Systems·Qi ZhouHongyi Li
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Cheng-De ZhengZhanshan Wang

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Citations

Dec 20, 2015·IEEE Transactions on Neural Networks and Learning Systems·Li-Bing Wu, Guang-Hong Yang
Jul 6, 2017·IEEE Transactions on Neural Networks and Learning Systems·Yongming Li, Shaocheng Tong

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