Adaptive NN control of uncertain nonlinear pure-feedback systems

By Shuzhi Sam Ge, Cong Wang. at - Automatisierungstechnik, 2002

504 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d19bc569189dfc24c1fe898b163a2439fff1bb2a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Shuzhi Sam Ge
4. Tie-shan Li
5. Peng Shi
6. Gang Feng
7. Xiaoping Liu
8. Bing Chen

Citations in the corpus

• A DSC Approach to Robust Adaptive NN Tracking Control for Strict-Feedback Nonlinear Systems (2008, background): “This assumption is very common in the existing literature, such as [11], [12], [15], [18], [21], [24], [32], etc.”
• Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems (2012, background): “The control objective of this paper is to utilize FLSs, the reference signal yj,r (t), (which has up to the mj th derivatives), the output of the system yj (t), and the state estimates of x̂j,i j to determine a fuzzy controller uj (t) and parameter update laws such that all the signals that are invo”
• 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).”
• A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems (2014, methodology): “In Hovakimyan et al. (2002), Yang and Calise (2007), instead of seeking a direct solution to the inverse problem, an analytically invertible model was introduced and a neural network was designed to compensate for the inversion error.Moreover, inHovakimyan et al. (2008), singular perturbation theory”
• A global Implicit Function Theorem without initial point and its applications to control of non-affine systems of high dimensions (2006, methodology): “These difficulties were overcome using a result of onedimensional global implicit functions as given in [10] without assuming the existence of a point (x0, u0) which satisfies f (x0, u0)= 0.”
• 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…”
• Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach (2012, methodology): “Obviously, it is also different from pure-feedback systems [6], [42], [50].”
• Robust adaptive tracking for time-varying uncertain nonlinear systems with unknown control coefficients (2003, background): “With the aid of neural networks [12], [13] adaptive control is expanded to much larger class of systems, uncertain strict-feedback and pure-feedback nonlinear systems, where the unknown virtual control coefficients gi ’s are functions of states and the signs of gi as well as the upper bounds of gi a”
• Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique (2011, methodology): “One reason is that pure-feedback nonlinear systems (1) contain nonaffine functions fi(x̄i(t), xi+1(t)), which have no affine appearance of the variables to be used as virtual controls αi(t) and the actual control u(t) [16], and the other reason is that, when universal function approximators, such as”
• 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).”