Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/62461
DC Field | Value | |
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dc.title | Neural-based direct adaptive control for a class of general nonlinear systems | |
dc.contributor.author | Zhang, T. | |
dc.contributor.author | Ge, S.S. | |
dc.contributor.author | Hang, C.C. | |
dc.date.accessioned | 2014-06-17T06:51:17Z | |
dc.date.available | 2014-06-17T06:51:17Z | |
dc.date.issued | 1997 | |
dc.identifier.citation | Zhang, T.,Ge, S.S.,Hang, C.C. (1997). Neural-based direct adaptive control for a class of general nonlinear systems. International Journal of Systems Science 28 (10) : 1011-1020. ScholarBank@NUS Repository. | |
dc.identifier.issn | 00207721 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/62461 | |
dc.description.abstract | A direct adaptive controller based on high-order neural networks (HONNs) is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The plant is assumed to be a feedback linearizable and minimum-phase system. Firstly, an ideal implicit feedback linearization control (IFLC) is established using implicit function theory. Then a HONN is applied to construct this IFLC to realize approximate linearization. The proposed controller ensures that the closed-loop system is Lyapunov stable and that the tracking error converges to a small neighbourhood of the origin. The requirements of an off-line training phase and the persistant excitation condition are eliminated. Simulation results verify the effectiveness of the proposed controller and the theoretical discussion. | |
dc.source | Scopus | |
dc.type | Article | |
dc.contributor.department | ELECTRICAL ENGINEERING | |
dc.description.sourcetitle | International Journal of Systems Science | |
dc.description.volume | 28 | |
dc.description.issue | 10 | |
dc.description.page | 1011-1020 | |
dc.description.coden | IJSYA | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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