Robust backstepping control of nonlinear systems using neural networks

By C. Kwan, Frank L. Lewis. IEEE Trans. Syst. Man Cybern. Part A, 2000

456 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/4afc27ea6e50a31cb6c203edec4050d24d94e8a7

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. Shuzhi Sam Ge
3. Mou Chen
4. Yong-ming Li
5. David John Hill
6. Yan-Jun Liu
7. Wei Wang
8. Yisha Liu

In Control and Systems Engineering

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

Citations in the corpus

• An ISS-modular approach for adaptive neural control of pure-feedback systems (2006, methodology): “…systems with unknown parameters (Kokotovic & Arcak, 2001; Krstic et al., 1995) and with unknown non-linearities (Choi & Farrell , 2001; Ge, Hang, Lee, & Zhang, 2001; Ge & Wang, 2002a; Kwan & Lewis, 2000; Lewis, Jagannathan, & Yeildirek, 1999; Polycarpou & Mears, 1998; Zhang, Peng, & Jiang, 2000).”
• Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach (2008, methodology): “The first two are typical nonlinear strict-feedback systems studied in the literature [1], [10], [12], [14], [21], and the third one is in the pure-feedback form.”
• Robust and adaptive backstepping control for nonlinear systems using RBF neural networks (2004, background): “Digital Object Identifier 10.1109/TNN.2004.826215 However, in spite of the merits explained, there are some problems in the backstepping design method.”
• A novel adaptive high-order sliding mode control based on integral sliding mode (2014, background): “In the modern control theory, one of the main problems originate from heavy uncertainty conditions and many approaches have been reported to deal with this severe problem such as backstepping control [1,2] and robust H ∞ output control [3,4], etc.”
• Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer (2013, background): “Many remarkable results in this area have been obtained, including robust adaptive control [1]–[8], sliding mode control [9]–[11], backstepping control [12]–[16], and intelligent control [17]–[22].”
• Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form (2002, methodology): “More recently, Kwan and Lewis developed an adaptive control method for a more general class of multi-input, and multi-output nonlinear systems (Kwan & Lewis, 2000).”
• Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, background): “In [6], robust backstepping control was developed for a class of general nonlinear systems using NNs which requires that the control coefficient matrix of each subsystem be known and invertible.”
• Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters (2010, methodology): “Some significant adaptive control schemes for uncertainty MIMO nonlinear systems have been obtained by adjusting the estimation vectors of the optimal fuzzy parameter vectors [16], [27], [28], [30], [34] or the optimal neural network weight vectors [17], [20], [22], [33].”
• Adaptive Fuzzy Backstepping Control of Fractional-Order Nonlinear Systems (2017, background): “It is well known that backstepping control provides an important approach for integer-order nonlinear systems [19]–[21].”
• Command filtered adaptive backstepping (2010, unknown): “10) in [3].”
• Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, background): “Existing research results of adaptive control techniques for the MIMO nonlinear system mostly assume that the control coefficient matrix is known and nonsingular (Kwan & Lewis, 2000).”
• Direct adaptive fuzzy control of nonlinear strict-feedback systems (2009, unknown): “Such restrictions have been removed by using adaptive neural network control (Ge, Hang, & Zhang, 2000; Ge, Lee, & Harris, 1998; Ge & Wang, 2002; Kwan & Lewis, 2000; Lewis, Yesildirek, & Liu, 2000; Zhang, Ge, & Hang, 2000; Zhang, Peng, & Jiang, 2000), or adaptive fuzzy control (Wang & Mendel, 1992)…”