Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

2017 ◽  
Vol 20 (6) ◽  
pp. 2101-2115 ◽  
Author(s):  
Chih‐Lyang Hwang ◽  
John Y. Hung
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Mimo Systems ◽  
Variable Structure Control ◽  
Variable Structure ◽  
Structure Control

scholarly journals Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hongwei Wang ◽  
Shufeng Lu ◽  
Xiaojuan Song
Keyword(s):  
Neural Network ◽  
Input Saturation ◽  
Variable Structure Control ◽  
Parametric Uncertainty ◽  
Variable Structure ◽  
Control Input ◽  
State Variables ◽  
Adaptive Neural Network ◽  
Structure Control ◽  
Network Variable

This study addresses the problem of attitude maneuver control for a three-axis stabilized liquid-filled spacecraft using an adaptive neural network variable structure control algorithm in the presence of parametric uncertainty, external disturbances, and control input saturation. The liquid fuel is equivalent to a spherical pendulum model, and the coupled dynamic model of liquid-filled spacecraft is derived using the conservation law of angular momentum moment. Then, adaptive variable structure control technique is designed, which contains hyperbolic tangent function that preserves control smoothness at all times. The proposed control algorithm has the properties that state variables converge to the origin asymptotically under parametric uncertainty and external disturbance. Furthermore, the controller derived here is extended by adding a feed-forward saturation compensation scheme to reduce the influence of unknown control input saturation on the system. Also, the saturation compensation scheme is derived by using a radial basis function neural network to approximate the unknown saturation nonlinearity. The associated stability proof of the resulting closed-loop system is presented based on Lyapunov analysis, and asymptotic convergence of the state variables is guaranteed via the Barbalat lemma. Numerical simulations are presented to illustrate the spacecraft performance obtained by using the proposed controllers.

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