scholarly journals Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7443
Author(s):  
Yaxiang Wang ◽  
Jiawei Tian ◽  
Yan Liu ◽  
Bo Yang ◽  
Shan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Internal Friction ◽  
Force Feedback ◽  
External Disturbance ◽  
Network Control ◽  
Constant Time Delay ◽  
Constant Time ◽  
Model Approximation ◽  
Adaptive Neural Network Control

A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.

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.

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