Observer-Based Adaptive Fuzzy Backstepping Control for a Class of Stochastic Nonlinear Strict-Feedback Systems

By Shaocheng Tong, Yue Li, Y. Li, Yanjun Liu. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2011

590 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ffc7beb473bd7cd5ed54414d1a456afb9da15e40

How researchers see this paper

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

1. Shaocheng Tong
2. Yong-ming Li
3. Mou Chen
4. Jinpeng Yu
5. Yong‐Hua Liu
6. Qi Zhou
7. Bing Chen
8. Hongyi Li

Citations in the corpus

• Composite Adaptive Fuzzy Output Feedback Control Design for Uncertain Nonlinear Strict-Feedback Systems With Input Saturation (2015, unknown): “References [21]–[23] investigated the output-feedback control problem for uncertain nonlinear systems with immeasurable states based on DSC technique.”
• Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics (2017, methodology): “We assume that y is only available and the states of the considered stochastic switched system (1) do not jump at the switching instant, which is a standard assumption in the existing switched systems [16]–[28].”
• Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation (2015, methodology): “In [29], an observer-based adaptive fuzzy backstepping control scheme was proposed for a class of stochastic nonlinear strict-feedback systems.”
• Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems (2015, background): “However, the drawback of explosion of complexity caused by the repeated differentiations of the virtual input [4], [5] for the adaptive backstepping is not resolved by approximation-based adaptive NN or fuzzy backstepping control [28].”
• Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, methodology): “To remove the matching condition, based on the backstepping design method [1], the fuzzy or neural control-based adaptive technique was presented in [22]–[24], [58], [59], and [66] for nonlinear single input single output (SISO) systems with unknown functions.”
• Adaptive dynamic surface asymptotic tracking for a class of uncertain nonlinear systems (2018, background): “It is clearly shown that, under the proposed scheme, the control performance of the considered system (44) is improved significantly.”
• Fuzzy Adaptive Quantized Control for a Class of Stochastic Nonlinear Uncertain Systems (2016, unknown): “Lemma 1 [26]: Suppose that there exist a function V(x, t) ∈ C2,1, two positive constants c and b, κ∞−functions α1 and α2, such that { α1(‖x‖) ≤ V(x, t) ≤ α2(‖x‖) LV ≤ −cV(x, t)+ b (3)”
• Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis (2014, methodology): “Lemma 1 [33]: Suppose that there exists a C2,1 function V (x, t) : Rn × R+ → R+, two constants c1 > 0 and c2 > 0, class K∞−functions ᾱ1 and ᾱ2 such that { ᾱ1(|x |) ≤ V (x, t) ≤ ᾱ2(|x |) LV (x, t) ≤ −c1V (x, t)+ c2 for all x ∈ Rn and t > t0.”
• Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, methodology): “In [27]–[29], neural network-based output-feedback adaptive control approaches were developed for a class of stochastic nonlinear strict-feedback systems.”
• Fuzzy Adaptive Actuator Failure Compensation Control of Uncertain Stochastic Nonlinear Systems With Unmodeled Dynamics (2014, background): “In [18]–[20], adaptive fuzzy or NN state feedback control approaches for a class of SISO stochastic nonlinear systems with or without time delays are proposed; in [21]–[23], adaptive output feedback controllers for SISO stochastic nonlinear systems without unmeasured states are developed, while in [”