Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation

By Yong-Yan Cao, Zongli Lin. Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2002

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/4bd06c1ab3dfa40c58891f1fec04a802dd32fd77

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. Mou Chen
3. Shuzhi Sam Ge
4. Beibei Ren
5. Xiaoping Liu
6. Bernard Voon Ee How
7. Chong Lin
8. Bing Chen

In Control and Systems Engineering

1. Shaocheng Tong
2. Mou Chen
3. Yong-ming Li
4. Shuzhi Sam Ge
5. Xiaoping Liu
6. Bing Chen
7. Gang Tao
8. Beibei Ren

Citations in the corpus

• Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, background): “Correspondingly, the problem for controlling nonlinear systems with input saturation is an important topic in the control community and has attracted increasing attention in recent years [45]–[50].”
• Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems. (2013, methodology): “In [44], robust stability analysis and fuzzy-scheduling control were developed for nonlinear systems subject to actuator saturation.”
• Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, methodology): “Analysis and design of control systems with input saturation constraints have been studied in Cao and Lin (2003), Chen, Ge, and Choo (2009), Chen, Ge, and How (2010), Hu, Ma, and Xie (2008), Gao and Selmic (2006) and Zhong (2005). To handle the physical limitation, constrained adaptive backstepping ”
• Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer (2013, neutral): “In [68], robust stability analysis and fuzzy-scheduling control were developed for nonlinear systems with actuator saturation.”
• Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation (2015, background): “In recent years, there have been extensive studies on various systems with input saturation in [1]–[3].”
• Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, methodology): “The Takagi–Sugeno (T–S) fuzzy modeling approach was utilized to control the nonlinear systems with actuator saturation [30].”