Robust and adaptive backstepping control for nonlinear systems using RBF neural networks

By Yahui Li, Sheng Qiang, Xianyi Zhuang, Okyay Kaynak. IEEE Transactions on Neural Networks, 2004

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Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone (2017, background): “[24] show two different NN control approaches developed for a class of affine nonlinear systems with unknown nonlinearities.”
Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form (2014, methodology): “Remark 2: The δ-modification [15] is introduced to improve the robustness and to avoid the weight parameters to drift to very large values.”
Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, methodology): “[31] put forward adaptive back-stepping control through applying radial basis function (RBF) NNs.”
Sliding mode control for a class of uncertain nonlinear system based on disturbance observer (2009, background): “[6] proposed two different backstepping NN control approaches for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities.”
Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters (2010, methodology): “In the literature [19], [21], [26], [31], several elegant schemes were developed to overcome this assumption.”