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- W2364651377 abstract "In the proposed synergetic control,the synergetic convergence of states can be realized,and the invariance against the system parameter variation and external perturbation can also be achieved.By using the search variables from the extremum-seeking control as the inputs,multilayer neural networks(MNN) are applied to approximate the differential of the state extrema as well as unknown parameters and functions.The problem of the unknown control gain is well solved by using Nussbaum gain function.At the same time,an adaptive parameter is adopted to compensate for the influence of MNN approximation errors.The stability analysis shows that tracking errors of states,errors between the output and its extrema,tracking errors of search variables,and estimation errors of MNN parameters,all converge exponentially to a small neighborhood of the origin by appropriately choosing design parameters.Theoretical analysis and simulation results show the effectiveness of the proposed control method." @default.
- W2364651377 created "2016-06-24" @default.
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- W2364651377 date "2013-01-01" @default.
- W2364651377 modified "2023-09-25" @default.
- W2364651377 title "Neural network adaptive synergetic control for multivariable extremum seeking system with unknown control gain" @default.
- W2364651377 hasPublicationYear "2013" @default.
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