Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint

By Wei He, David Ofosu Amoateng, Zhao Yin, Changyin Sun. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2016

376 citations recorded by the source corpus.

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Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, methodology): “In [11], an adaptive NN controller is introduced to tackle the issue with the output constraint and input deadzone.”
Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, background): “Lemma 4 [79], [80]: Consider the basis function of Gaussian RBF NN with Z being the input vector, if Ẑ = Z − ςψ̄ where ψ̄ is a bounded vector and constant ς > 0, then we have”
Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone (2017, background): “2608739 deadzone, which is insensitive to small signals, is a common nonlinearity we have to cope with [1].”
Teleoperation Control Based on Combination of Wave Variable and Neural Networks (2017, methodology): “Adaptive RBF NN-based control has been investigated in [36] and [37] to deal with the deadzone and uncertain robotic model.”
Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, neutral): “For nonlinear systems with unknown functions and input nonlinearities, several adaptive fuzzy or neural control methods were given in [19]–[21], [63], [64], [67], [68], and [76].”