Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems

By Shaocheng Tong, Yong-ming Li, Peng Shi. IEEE transactions on fuzzy systems, 2012

340 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2be51cae969f56e6e84eba41c30cbc6333401144

How researchers see this paper

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

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Peng Shi
4. Bing Chen
5. Chong Lin
6. Xiaoping Liu
7. Jinpeng Yu
8. Haisheng Yu

In Statistical and Nonlinear Physics

1. Shaocheng Tong
2. Yong-ming Li
3. Peng Shi
4. Chong Lin
5. Bing Chen
6. Xiaoping Liu
7. Fang Wang
8. Kefu Liu

Citations in the corpus

• 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].”
• Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems (2018, methodology): “By combining the backstepping technique and adaptive fuzzy/neural control approaches, many meaningful results were obtained, for instance, see [5]–[17].”
• Robust iterative learning control for systems with norm‐bounded uncertainties (2016, background): “consider the second type of nonparametric uncertainties, such as adaptive fuzzy output feedback control [16, 17], and sliding mode control [18].”
• Observer-Based Adaptive Decentralized Fuzzy Fault-Tolerant Control of Nonlinear Large-Scale Systems With Actuator Failures (2014, background): “INTRODUCTION IN the past decade, fuzzy logic systems (FLSs) and neural networks (NNs) have been extensively used for the modelling and controller design for uncertain nonlinear systems [1-7] due to their approximation ability to the nonlinear smooth functions, and various adaptive fuzzy and NN decen”
• Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, background): “further considered other kinds of nonlinear systems in nonaffine form, such as nonaffine pure-feedback systems with unknown control directions [36], with dead zone [39], and with time delay [41], nonaffine pure-feedback stochastic nonlinear systems [37], [38], discrete-time pure-feedback systems [40”