Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks
作者： W.S. Chen
DOI: 10.1049/IET-CTA.2008.0322
关键词:
摘要: This paper addresses the adaptive neural network tracking control problem for a class of strict-feedback systems with unknown non-linearly parameterised and time-varying disturbed function known periods. Radial basis Fourier series expansion are combined into new approximator to model each suitable in systems. Dynamic surface approach is used solve ‘explosion complexity’ backstepping design procedure. The uniform boundedness all closed-loop signals guaranteed. error proved converge small residual set around origin. A simulation example provided illustrate effectiveness scheme designed.
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