Adaptive event‐triggered neural control for nonlinear uncertain system with input constraint

2020 ◽  
Vol 30 (10) ◽  
pp. 3801-3815
Author(s):  
Wenli Chen ◽  
Jianhui Wang ◽  
Kemao Ma ◽  
Tao Wang
Keyword(s):  
Neural Control ◽  
Uncertain System ◽  
Input Constraint ◽  
Nonlinear Uncertain System ◽  
Event Triggered

A Universal Adaptive Neural Control Scheme for Nonlinear Uncertain System

2013 ◽  
Vol 462-463 ◽  
pp. 553-557
Author(s):  
Huai Qing Ren ◽  
Yan Chun Liang ◽  
Xiao Hu Shi
Keyword(s):  
Neural Control ◽  
Uncertain System ◽  
Lyapunov Theory ◽  
Bp Algorithm ◽  
Adaptive Neural Control ◽  
Proposed Model ◽  
Control Scheme ◽  
Model Reference Adaptive ◽  
Novel Model ◽  
Nonlinear Uncertain System

The traditional BP algorithm is modified and a kind of novel model reference adaptive control (MRAC) scheme is proposed based on generalized BP (GBP) algorithm. The convergence of the control scheme is also analyzed according to Lyapunov theory. To test its effectiveness, the proposed model is used to control a nonlinear uncertain system. Simulation results indicate that the controller is effective in controlling a general class of nonlinear systems.

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

2021 ◽  
pp. 095965182110131
Author(s):  
Guoqing Zhang ◽  
Shen Gao ◽  
Jiqiang Li ◽  
Weidong Zhang
Keyword(s):  
Neural Control ◽  
Fault Tolerant ◽  
Lyapunov Stability Theory ◽  
Actuator Faults ◽  
Closed Loop System ◽  
External Disturbances ◽  
Unmanned Surface Vehicle ◽  
Adaptive Parameters ◽  
Control Gain ◽  
Event Triggered

This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

Event-triggered multi-rate fusion estimation for uncertain system with stochastic nonlinearities and colored measurement noises

2017 ◽  
Vol 36 ◽  
pp. 313-320 ◽  
Author(s):  
Hailong Tan ◽  
Bo Shen ◽  
Yurong Liu ◽  
Ahmed Alsaedi ◽  
Bashir Ahmad
Keyword(s):  
Uncertain System ◽  
Measurement Noises ◽  
Event Triggered

Secure Adaptive-Event-Triggered Filter Design With Input Constraint and Hybrid Cyber Attack

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jinliang Liu ◽  
Yuda Wang ◽  
Jinde Cao ◽  
Dong Yue ◽  
Xiangpeng Xie
Keyword(s):  
Filter Design ◽  
Cyber Attack ◽  
Input Constraint ◽  
Event Triggered

scholarly journals Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
J. Humberto Pérez-Cruz ◽  
José de Jesús Rubio ◽  
Rodrigo Encinas ◽  
Ricardo Balcazar
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Neural Control ◽  
Tracking Error ◽  
Uncertain System ◽  
Reference Trajectory ◽  
System A ◽  
Multiple Input ◽  
Feedback Linearization Control

The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

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