Fuzzy-Adaptive Decentralized Output-Feedback Control for Large-Scale Nonlinear Systems With Dynamical Uncertainties

By Shaocheng Tong, Changliang Liu, Yong-ming Li. IEEE transactions on fuzzy systems, 2010

448 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d61cdfcb1a41ece17f950f969738d96cb6c88b01

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. Hongyi Li
3. Peng Shi
4. Yong-ming Li
5. Qi Zhou
6. Hak-Keung Lam
7. Huijun Gao
8. Shuai Sui

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Hongyi Li
4. Peng Shi
5. Qi Zhou
6. Mou Chen
7. Huijun Gao
8. Shengyuan Xu

In Statistical and Nonlinear Physics

1. Shaocheng Tong
2. Yong-ming Li
3. Huijun Gao
4. Hongyi Li
5. Qi Zhou
6. Mou Chen
7. Peng Shi
8. Zhengli Zhao

Citations in the corpus

• Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems (2012, background): “2183604 systems are available for measurement in the aforementioned control approaches, in [19]–[28], the problem of adaptive fuzzy or neural output feedback control for uncertain SISO, MIMO, or large-scale nonlinear system via state observers has been investigated, and the stability of the resultin”
• Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach (2012, unknown): “For instance, see [4], [9], [10], [12], [23], [26], [40], and [51].”
• Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay (2013, methodology): “On the other hand, it is well known that the method of fuzzy logic control or NN control is useful to approximate unknown nonlinear functions in systems (see [20]–[35] and the references therein).”
• Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching (2014, background): “Also, the fuzzy control concept was extended to other stability/control problems such as output feedback control [23], sampled-data control [26], control systems with time delay [8], [24], [25], tracking control [27], large-scale fuzzy systems [28], and even fuzzy neural networks [29].”
• Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone (2016, background): “In [9], a fuzzy adaptive decentralized output-feedback control design was proposed for large-scale nonlinear systems with dynamical uncertainties.”
• Saturated Adaptive Robust Control for Active Suspension Systems (2013, methodology): “To handle this situation, a number of control techniques have been proposed, such as robust H∞ or H2 control [19], [36], fuzzy control [21], [31], sliding-mode control [5], neural network control [32], [38], backstepping control [22], [26], and nonlinear robust control [18].”
• Adaptive Fuzzy Output Feedback Control of MIMO Nonlinear Systems With Unknown Dead-Zone Inputs (2013, methodology): “INTRODUCTION IN the past decade, many approximation-based adaptive fuzzy or neural backstepping controllers have been developed for uncertain nonlinear strict-feedback systems [1]–[26].”
• Adaptive Fuzzy Tracking Control Design for SISO Uncertain Nonstrict Feedback Nonlinear Systems (2016, background): “2540058 [22] is for a class of MIMO nonlinear systems, while the work in [23] and [24] is for a class of nonlinear large-scale systems.”