Direct adaptive NN control of a class of nonlinear systems

By Shuzhi Sam Ge, Cong Wang. IEEE Trans. Neural Networks, 2002

610 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f788cca40792296b0b1b609fcc63c6a4b8d7b4f9

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. T. Zhang
5. Peng Shi
6. Yuanxin Li
7. Qi Zhou
8. Yong-ming Li

In Computational Theory and Mathematics

1. Shuzhi Sam Ge
2. Peng Shi
3. T. Zhang
4. Shaocheng Tong
5. Mou Chen
6. Yuanxin Li
7. Chenguang Yang
8. Xiao Min

In Control and Systems Engineering

1. Shaocheng Tong
2. Mou Chen
3. Shuzhi Sam Ge
4. Peng Shi
5. Yong-ming Li
6. Chenguang Yang
7. Wei He
8. Qi Zhou

Citations in the corpus

• Observer-Based Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems With Unknown Control Gain Functions (2020, methodology): “It should be mentioned that since NNs and type-two FLSs also have the property of Lemma 1, the FLSs used in this article can be replaced by NNs [6], [7] or type-two FLSs [28].”
• Learning from neural control (2006, background): “Thus, effective learning capabilities are incorporated into the controller design to make learning control possible.”
• Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form (2008, methodology): “Though backstepping has become one of the most popular design methods for a large class of SISO nonlinear systems (Ge & Wang, 2002b; Kanellakipoulos et al., 1991; Krstiét al., 1995; Polycarpou, 1996; Polycarpou & Mears, 1998; Zhang et al., nonlinear dead-zone model, which is used in the controller…”
• Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, background): “Lemma 1 [38], [42]: For bounded initial conditions, if there exists a continuous and positive-definite Lyapunov func- tion satisfying , such that , where are class functions and is a positive constant, then the solution is uniformly bounded.”
• Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems (2011, background): “Many researchers have focused on the triangular nonlinear systems and developed some significant results such as [8]–[13] via a backstepping technique and neural networks methods.”
• Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle (2015, background): “In the direct design [15], [36], the new function g−1( f − ẋd) is defined and approximated by NN, so that there is no need to approximate the control gain function g.”
• Robust and adaptive backstepping control for nonlinear systems using RBF neural networks (2004, methodology): “So, the input vector of nonlinear function in literature [20] is 3-D at least, while those of nonlinear functions and are still 2-D.”
• 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).”
• Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters (2010, background): “In the literature [19], [21], [26], [31], several elegant schemes were developed to overcome this assumption.”