Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs

By Tianping Zhang, Shuzhi Sam Ge. at - Automatisierungstechnik, 2007

509 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/5382c58769050682e63268fd8a2520edc739b074

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In Computer Networks and Communications

In Control and Systems Engineering

Citations in the corpus

Observer-Based Adaptive Fuzzy Tracking Control of MIMO Stochastic Nonlinear Systems With Unknown Control Directions and Unknown Dead Zones (2015, background): “On the other hand, dead-zone is one of the most important nonsmooth nonlinearities (including hysteresis and saturation), and it often degrades system control performance and even results in instability of the control system [17], [18], [22]–[27].”
Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, background): “Assumption 1 [28], [29]: Functions and are smooth, and there exist positive constants , , , and such that (4) (5) According to (3)–(5), we know that .”
Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay (2019, methodology): “The authors further presented some significant control methods for different kinds of MIMO nonlinear systems with constant delays in [60] and time-varying delays in [61].”
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, unknown): “Assumption 1 (Zhang & Ge, 2007, 2008 ).”
Adaptive NN Backstepping Output-Feedback Control for Stochastic Nonlinear Strict-Feedback Systems With Time-Varying Delays (2010, methodology): “This modification method has widely been used in the existing work, for example, adaptive control for systems described by the ordinary differential equation [28]–[33], the discrete difference equation [34]–[37], the time-delay functional differential equation [38]–[42], or the Itó stochastic differ”
Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique (2011, unknown): “Further development was given in [32]– [36].”
Adaptive Fuzzy Output Feedback Control of MIMO Nonlinear Systems With Unknown Dead-Zone Inputs (2013, methodology): “The analysis and design where the states in system (1) are available for feedback have also been carried out in the work of Zhang and Ge [39], [40].”
Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation (2016, background): “The results in [22] and [23] are for the state feedback case, while [24] is for the output feedback case.”
Adaptive output-feedback control design with prescribed performance for switched nonlinear systems (2017, unknown): “…2014; Zhang & Ge, 2008; Zhang, Ge, & Hang, 2000; Zhou, Li, Wu, Wang, & Ahn, 2016), multi-input and multi-output (MIMO) nonlinear systems (Chen, Ge, & Ren, 2011; Ge & Tee, 2007; Zhang & Ge, 2007) and interconnected large-scale systems (Hamdy&Ghazaly, 2014; Tong, Liu, & Li, 2010; Yoo & Park, 2009).”
Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, unknown): “that for the systems with similar (or identical) structures to EL systems, the issue of full-state constraints is not easy to handle if the filtered error-based control method is utilized [25], [36], [37].”
Disturbance-observer-based robust control for time delay uncertain systems (2010, background): “In [ 8 ], an adaptive neural control scheme was proposed for a class of uncertain multi-input multi-output (MIMO) nonlinear state timevarying delay systems in a triangular control structure with unknown nonlinear dead-zones and gain signs.”