summary:In this paper, a fixed-time safe control problem is investigated for an uncertain high-order nonlinear pure-feedback system with state constraints. A new nonlinear transformation function is firstly proposed to handle both the constrained and unconstrained cases in a unified way. Further, a radial basis function neural network is constructed to approximate the unknown dynamics in the system and a fixed-time dynamic surface control (FDSC) technique is developed to facilitate the fixed-time control design for the uncertain high-order pure-feedback system. Combined with the proposed unified transformation function and the FDSC technique, an adaptive fixed-time control strategy is proposed to guarantee the fixed-time tracking. The novel...
International audienceNon-overshooting stabilization is a form of safe control where the setpoint ch...
We propose a novel nonlinear control method for solving the problem of stabilization with guaranteed...
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback ...
This paper examines approximation-based fixed-time adaptive tracking control for a class of uncertai...
This article proposes a fixed-time adaptive fault-tolerant control methodology for a larger class of...
This paper deals with the tracking control problem for a class of unknown pure feedback system with ...
This paper is concerned with robust stabilization\ud problem for a class of nonaffine pure-feedback ...
This paper studies the fixed-time stabilization control for a class of second-order systems with unk...
Under U-model control design framework, a fixed-time neural networks adaptive backstepping control i...
Copyright © 2014 J. Humberto Pérez-Cruz et al.This is an open access article distributed under the ...
A new fixed-time adaptive neural network control strategy is designed for pure-feedback non-affine n...
Adaptive fuzzy control strategies are established to achieve global prescribed performance with pres...
This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee sta...
A new fixed-time adaptive neural network control strategy is designed for pure-feedback non-affine n...
The ubiquity of dynamical systems in society has brought the subject matter of their safety to the f...
International audienceNon-overshooting stabilization is a form of safe control where the setpoint ch...
We propose a novel nonlinear control method for solving the problem of stabilization with guaranteed...
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback ...
This paper examines approximation-based fixed-time adaptive tracking control for a class of uncertai...
This article proposes a fixed-time adaptive fault-tolerant control methodology for a larger class of...
This paper deals with the tracking control problem for a class of unknown pure feedback system with ...
This paper is concerned with robust stabilization\ud problem for a class of nonaffine pure-feedback ...
This paper studies the fixed-time stabilization control for a class of second-order systems with unk...
Under U-model control design framework, a fixed-time neural networks adaptive backstepping control i...
Copyright © 2014 J. Humberto Pérez-Cruz et al.This is an open access article distributed under the ...
A new fixed-time adaptive neural network control strategy is designed for pure-feedback non-affine n...
Adaptive fuzzy control strategies are established to achieve global prescribed performance with pres...
This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee sta...
A new fixed-time adaptive neural network control strategy is designed for pure-feedback non-affine n...
The ubiquity of dynamical systems in society has brought the subject matter of their safety to the f...
International audienceNon-overshooting stabilization is a form of safe control where the setpoint ch...
We propose a novel nonlinear control method for solving the problem of stabilization with guaranteed...
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback ...