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Adaptive Neural Backstepping Control of Nonlinear Fractional-Order Systems with Input Quantization
[post]
2021
unpublished
This article addresses the tracking control problem of uncertain fractional-order nonlinear systems in the presence of input quantization and external disturbance by combining with radial basis function(RBF) neural networks(NNs), fractional-order disturbance observer(FODO) and backstepping method. The unknown nonlinearities of fractional-order systems is approximated by RBF NNs. The design of hysteretic quantizer achieves quantification of input signal and avoids chattering. The FODO is
doi:10.21203/rs.3.rs-719634/v1
fatcat:qo67qgzvzvbkbowlo2bgufvukm