Adaptive NN Backstepping Output-Feedback Control for Stochastic Nonlinear Strict-Feedback Systems With Time-Varying Delays

By Weisheng Chen, L. Jiao, Jing Li, Ruihong Li. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2010

424 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/76a2b61cf192e383a8e1e59bd3278923436aae88

How researchers see this paper

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

In Artificial Intelligence

1. Shaocheng Tong
2. Yong-ming Li
3. Qi Zhou
4. Peng Shi
5. Hongyi Li
6. Tie-shan Li
7. Yanjun Liu
8. Shengyuan Xu

In Computer Networks and Communications

1. Shaocheng Tong
2. Hongyi Li
3. Yong-ming Li
4. Yanjun Liu
5. Peng Shi
6. Chenguang Yang
7. Tie-shan Li
8. Jinpeng Yu

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Hongyi Li
4. Qi Zhou
5. Peng Shi
6. Tie-shan Li
7. Yanjun Liu
8. Tong Wang

Citations in the corpus

• Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis (2014, methodology): “Recently, approximation-based adaptive neural (or fuzzy) control schemes have been found to be particularly useful for the control of highly uncertain, nonlinear and complex systems, and many interesting research results have been obtained in [18]–[30] for deterministic nonlinear systems and in [31]”
• Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems (2015, background): “As an alternative, neural networks (NNs) [12]–[15] or fuzzy logic system (FLS) [16]–[22] approximator-based adaptive control approaches have been developed to deal with nonlinear systems with unstructured uncertainties.”
• Observer-Based Adaptive Fuzzy Backstepping Control for a Class of Stochastic Nonlinear Strict-Feedback Systems (2011, methodology): “The main advantages of the proposed control schemes are as follows: (i) by designing a new nonlinear fuzzy state observer, not the linear state observer like [26]–[30], the proposed two adaptive control methods do not require that all the states of the system are measured directly, which is a common”
• Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form (2014, background): “The problem of adaptive output-feedback control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays was studied in [23].”
• Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay (2013, background): “In addition, compared with [48] and [49], the time delays considered in this paper exist not only in the system output but also in the state variables.”
• Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems (2018, background): “Lemma 5 ([21]): If function f(x) is continuous on a compact set Ξ, then for any given ε > 0, there is a fuzzy logic system (10) such that”
• Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, methodology): “The main differences between our result and the ones in [29], [39], and [47] are as follows: 1) From the controlled systems, the results are obtained in [39] and [47] for deterministic systems and in [29] for the strict-feedback stochastic system without input saturation, and in this paper, we consi”
• 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 [”
• Observer-Based Adaptive Fuzzy Tracking Control of MIMO Stochastic Nonlinear Systems With Unknown Control Directions and Unknown Dead Zones (2015, background): “…the limitations existing in the aforementioned adaptive backstepping con-trol approaches, many adaptive fuzzy and neural network (NN) backstepping control design methods have been developed for uncertain stochastic nonlinear systems via fuzzy logic systems (FLS) or NNs; for example, see [5]–[11].”