Adaptive Backstepping Stabilization of Nonlinear Uncertain Systems With Quantized Input Signal

By Jing Zhou, C. Wen, Guanghong Yang. IEEE Transactions on Automatic Control, 2014

474 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Adaptive asymptotic tracking control of uncertain nonlinear systems with input quantization and actuator faults (2016, methodology): “In order to propose a suitable control scheme, an important property that the hysteretic quantizer q(uj) can be decomposed into a linear part and a nonlinear part as follows (Zhou et al., 2014): q(uj) = uj + fj (3) where fj = q(uj) − uj ∈ R.”
Adaptive Neural Network Finite-Time Output Feedback Control of Quantized Nonlinear Systems (2018, methodology): “u ∈ R is the controller to be designed, the system input Q(u) under the quantization action is defined as follows [14], [15]:”
Robust control for a class of uncertain nonlinear systems with input quantization (2016, methodology): “In [20], a backstepping-based adaptive control scheme is presented for uncertain strict-feedback nonlinear systems with input quantized by hysteresis quantizers.”
Fuzzy Adaptive Quantized Control for a Class of Stochastic Nonlinear Uncertain Systems (2016, methodology): “The hysteretic quantizer in [14] and [15] can be regarded as a switched combination of two asymmetric logarithmic quantizers, the map of q(u(t)) for u > 0 is displayed in Fig.”
Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, methodology): “As for the system uncertainties, adaptive control is a method to cope with it due to its ability of providing online estimations of unknown system parameters [14]–[19].”