Deterministic learning enhanced neutral network control of unmanned helicopter

dc.creatorJiang, Yiming
dc.creatorYang, Chenguang
dc.creatorDai, Shi Lu
dc.creatorRen, Beibei (TTU)
dc.description© SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses. cc-by
dc.description.abstractIn this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.
dc.identifier.citationJiang, Y., Yang, C., Dai, S.-L., & Ren, B.. 2016. Deterministic learning enhanced neutral network control of unmanned helicopter. International Journal of Advanced Robotic Systems, 13(6).
dc.subjectdeterministic learning
dc.subjectglobal neural control
dc.subjectUnmanned helicopter
dc.titleDeterministic learning enhanced neutral network control of unmanned helicopter
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