An ISS-modular approach for adaptive neural control of pure-feedback systems

By Cong Wang, David John Hill, Shuzhi Sam Ge, Guanrong Chen. at - Automatisierungstechnik, 2006

497 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/445a8ca2ae9aa166a381d40370c0be8f5961b4a0

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Citations in the corpus

Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique (2011, background): “More recently, nonaffine pure-feedback systems (1) have been considered in [21]–[24] using mean value and small-gain theorems.”
A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems (2014, background): “…et al., 2011; Ge & Wang, 2002; Ge & Zhang, 2003; Labiod & Guerra, 2007; Ren et al., 2009; Wang, Chien et al., 2011, 2010; Wang, Ge et al., 2010; Wang et al., 2006; Wang, Liu et al., 2011; Zhang & Ge, 2008; Zhang et al., 2010, 2012), the Taylor series expansion (Leu et al., 2005), and the…”
Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints (2013, methodology): “In [33], an “input-to-state stability modular” approach combined with the small-gain theorem was presented for adaptive neural control of completely nonaffine pure-feedback systems.”
Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form (2008, methodology): “(100) (Ge & Wang, 2002a), and the dimensions of variables Zi are reduced by introducing the first-order filtering in approximating unknown functions, compared with the existing results (Ge & Wang, 2002a; Wang & Huang, 2002; Wang et al., 2006).”
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.”
Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems (2018, background): “Remark 2: This assumption is usually used in control design for pure-feedback systems, for examples, see [38]–[41].”
Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach (2008, methodology): “and the following class of nonlinear pure-feedback systems [9]:”
Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis (2014, background): “It has been proven in [23] that the constant s in Lemma 2 is a limited value and is independent of the variable Z and l, which is the dimension of neural network weight W = [w1, w2, .”
Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach (2012, background): “Since it is not guaranteed that ∂f1 ∂x2 > 0 for all [x1 , x2 ] ∈ R2 , the adaptive neural control methodology for pure-feedback systems [6], [42] cannot also be used to control this system.”
Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback (2017, unknown): “Choosing κio1(s) = λm in (Pi o ) 2 s2 and κio2(s) = λm a x (Pi o ) 2 s 2 withPio = diag{Pi, ρ−1 i }, it follows that there exists class KL function βi1 such that [42] ‖Ei‖ ≤ βi1(‖Ei(0)‖, t) + κi io (‖Wi‖) + κi io (‖εi‖) (23)”
Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems (2012, background): “Direct adaptive fuzzy or neural control approaches in [40]–[43] are developed for uncertain pure-feedback nonlinear systems without or with time delays, where an implicit theorem is exploited to assert the existence of continuous desired feedback controllers, and neural networks or FLSs are utilized”
Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation (2018, unknown): “As ‖E1i‖ ≥ 2(‖εi‖ + ki‖ i‖F )/λmin(k1i) renders V̇1 ≤ λmin(k1i )‖E1i‖2/2, the subsystem (19) is ISS and there exists a KL function 1i such that ‖E1i (t)‖ ≤ 1i (‖E1i (0)‖, t) + φi i (‖εi‖)+ φ i i (‖ i‖) where φi i (s) = (λmax(P1i )/λmin(P1i ))(2s/λmin(k1i )) and φ i i (s) = (λmax(P1i )/λmin(P1i ))(2k”