Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form

By Dan Wang, Jie Huang. at - Automatisierungstechnik, 2002

366 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ebb0235f6fad252f3a35d92adedd4935758ae9f0

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. Shuzhi Sam Ge
4. Peng Shi
5. Xiaoping Liu
6. T. Zhang
7. Chong Lin
8. Bing Chen

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Peng Shi
4. Shuzhi Sam Ge
5. Hongyi Li
6. Xiaoping Liu
7. Mou Chen
8. Dan Wang

Citations in the corpus

• Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, methodology): “Theoretically, the control methods proposed in [31], [32], [47], and [48] cannot be applied to solve the control problem of the more general systems (66) and (68).”
• Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics (2013, background): “On the condition that and are unknown, is not available; in what follows, can be approximated by a single hidden layer (SHL) NN [25], [28] as”
• Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach (2012, methodology): “In [43], a backstepping-based procedure was developed for the design of adaptive network controller for affine-in-control purefeedback systems.”
• Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form (2008, methodology): “As an alternative, adaptive NN control based on quadratic Lyapunov function was proposed for a class of nonlinear systems (Ge & Wang, 2002b).”
• Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach (2008, methodology): “Wang and Huang investigated the following class of pure-feedback systems [8]”
• Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters (2010, methodology): “Various feedback strategies presented in the literature [14], [18], [25], [29] can guarantee the stability of the corresponding closed-loop system.”
• An ISS-modular approach for adaptive neural control of pure-feedback systems (2006, methodology): “Compared with existing results for affine-in-control pure-feedback systems (Ge & Wang, 2002b; Wang & Huang, 2002 ), this paper presents yet another method in controlling non-affine pure-feedback system (1) with less restrictive assumptions.”
• Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems (2012, background): “Adaptive neural control schemes are proposed in [38] and [39] for a class of pure-feedback nonlinear systems, where the controlled system is assumed to be an affine nonlinear to avoid the algebraic loop problem.”
• A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems (2014, neutral): “…tackled (Chien,Wang, Leu, & Lee, 2011;Ge&Wang, 2002; Ren, Ge, Su, & Lee, 2009; Wang, Chien, Leu, & Lee, 2010; Wang, Ge, & Hong, 2010;Wang, Hill, Ge, & Chen, 2006;Wang & Huang, 2002; Wang, Liu, & Shi, 2011; Zhang&Ge, 2008; Zhang,Wen, &Zhu, 2010; Zhang, Zhu, & Yang, 2012; Zou, Hou, & Tan, 2008).”
• Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form (2005, background): “A commonly used basis function is the so-called Gaussian Function of the following form: (2) where , , are constant vectors called the center of the basis function, and is a real number called the width of the basis function.”
• Adaptive Fuzzy Control of Nonlinear Systems With Unmodeled Dynamics and Input Saturation Using Small-Gain Approach (2017, methodology): “Wang and Huang [41] employed NN to identify unknown nonlinearities and proposed an adaptive NN control scheme for uncertain pure-feedback systems.”
• Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique (2011, background): “By combining adaptive neural control with backstepping, further results have been obtained in [16], [19], and [20] for a class of unknown pure-feedback nonlinear systems, where at least an actual control u(t) in affine form ẋn(t) = fn(x̄n(t)) + gn(x̄n(t))u(t) exists.”