Online modeling of the CNC engraving system with dead-zone input nonlinearity

2021 ◽  
pp. 1-1
Author(s):  
Qi Wu ◽  
Shijian Dong ◽  
Wen-An Zhang ◽  
Li Yu
Keyword(s):  
Dead Zone ◽  
Input Nonlinearity ◽  
Online Modeling

Global fixed-time stabilization for a class of uncertain high-order nonlinear systems with dead-zone input nonlinearity

2018 ◽  
Vol 41 (7) ◽  
pp. 1888-1895
Author(s):  
Fangzheng Gao ◽  
Yanling Shang ◽  
Yuqiang Wu ◽  
Yanhong Liu
Keyword(s):  
Nonlinear Systems ◽  
State Feedback ◽  
Closed Loop ◽  
Dead Zone ◽  
Fixed Time ◽  
High Order ◽  
Closed Loop System ◽  
Sign Function ◽  
Input Nonlinearity ◽  
Power Integrator

This paper considers the problem of global fixed-time stabilization for a class of uncertain high-order nonlinear systems. One distinct characteristic of this work is that the system under consideration possesses the dead-zone input nonlinearity. By delicately combining the sign function with a power integrator technique, a state feedback controller is designed such that the states of the resulting closed-loop system converge to the origin within a fixed time. A simulation example is provided to illustrate the effectiveness of the proposed approach.

scholarly journals Adaptive NN Control for Multisteering Plane Aircraft with Dead Zone or Backlash Input Nonlinearity

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiang-fei Meng ◽  
Ying Wang ◽  
Mao-long Lv
Keyword(s):  
Neural Network ◽  
Dead Zone ◽  
External Disturbance ◽  
Tracking Error ◽  
Network Control ◽  
Control Allocation ◽  
Input Nonlinearity ◽  
Adaptive Neural Network Control ◽  
Robust Adaptive ◽  
Backlash Nonlinearity

Considering that many factors such as actuator input dead zone, backlash, and external disturbance could affect the exactness of trajectory tracking, therewith a robust adaptive neural network control scheme on the basis of control allocation is proposed for the sake of tracking control of multisteering plane aircraft with actuator input dead zone or backlash nonlinearity. First of all, an actuator input dead zone or backlash nonlinearity control assignment model is established and the control allocation equation is derived. Secondly, the system nonlinear uncertainty is compensated by means of radial basis function neural network, and a robust term is introduced to achieve robustness against external disturbance and system errors. Finally, by utilizing Lyapunov stability theorem, it has been proved that all the signals in the closed-loop system are bounded, and the tracking error converges to a small residual set asymptotically. Simulation results on ICE101 multisteering plane aircraft demonstrate the outstanding tracking performance and strong robustness as well as effectiveness of the proposed approach, which can effectively overcome the adverse influence of dead zone, backlash nonlinearity, and external disturbance on the system.

Adaptive Fast Terminal Sliding Mode Control for a Class of Uncertain Systems with Input Nonlinearity

2017 ◽  
Vol 21 (3) ◽  
pp. 518-526
Author(s):  
Linjie Xin ◽  
◽  
Qinglin Wang ◽  
Yuan Li ◽  
Jinhua She ◽  
...  
Keyword(s):  
Uncertain Systems ◽  
Control Method ◽  
Sliding Mode ◽  
Mimo System ◽  
Dead Zone ◽  
Lyapunov Stability Theory ◽  
Control Law ◽  
Terminal Sliding Mode ◽  
Input Nonlinearity ◽  
Single Output

This study investigates the terminal sliding mode (TSM) control for a class of first-order uncertain systems with dead-zone and saturation. First, a new adaptive TSM control law was proposed for the single-input and single-output (SISO) systems by employing an integral fast TSM. It achieves rejection for both system uncertainty and input nonlinearity. The global reaching condition of the sliding mode is guaranteed by the Lyapunov stability theory. The new control law possesses faster convergence than the linear sliding mode method, and the singularity problem of TSM is avoided. Then, the control law was extended for tracking control of a dynamic model of spacecraft which was a multi-input and multi-output (MIMO) system. Finally, the simulation results confirmed the effectiveness of the proposed control method.

scholarly journals An Adaptive Dynamic Surface Controller for Ultralow Altitude Airdrop Flight Path Angle with Actuator Input Nonlinearity

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mao-long Lv ◽  
Xiu-xia Sun ◽  
Shu-guang Liu ◽  
Dong Wang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Dead Zone ◽  
Tracking Error ◽  
Flight Path ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
Input Nonlinearity ◽  
Atmospheric Disturbance ◽  
Flight Path Angle

In the process of ultralow altitude airdrop, many factors such as actuator input dead-zone, backlash, uncertain external atmospheric disturbance, and model unknown nonlinearity affect the precision of trajectory tracking. In response, a robust adaptive neural network dynamic surface controller is developed. As a result, the aircraft longitudinal dynamics with actuator input nonlinearity is derived; the unknown nonlinear model functions are approximated by means of the RBF neural network. Also, an adaption strategy is used to achieve robustness against model uncertainties. Finally, it has been proved that all the signals in the closed-loop system are bounded and the tracking error converges to a small residual set asymptotically. Simulation results demonstrate the perfect tracking performance and strong robustness of the proposed method, which is not only applicable to the actuator with input dead-zone but also suitable for the backlash nonlinearity. At the same time, it can effectively overcome the effects of dead-zone and the atmospheric disturbance on the system and ensure the fast track of the desired flight path angle instruction, which overthrows the assumption that system functions must be known.

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