Adaptive neural network control of flexible robotic with unmodeled dynamics and time-varying output constraints

2017 ◽  
Author(s):  
Xue Bei ◽  
Zhang Tianping ◽  
Yang Yuequan
Keyword(s):  
Neural Network ◽  
Network Control ◽  
Unmodeled Dynamics ◽  
Time Varying ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Output Constraints

scholarly journals Disturbance Observer-Based Adaptive Neural Network Control of Marine Vessel Systems with Time-Varying Output Constraints

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Zhao ◽  
Li Tang ◽  
Yan-Jun Liu
Keyword(s):  
Neural Network ◽  
Disturbance Observer ◽  
Continuous Functions ◽  
Network Control ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
External Disturbances ◽  
Adaptive Neural Network Control ◽  
Output Constraints ◽  
Novel Strategy

This article investigates an adaptive neural network (NN) control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation (NSDT) is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function (BLF) is used to achieve time-varying output constraints. As an important approximation tool, the NN is employed to approximate uncertain and continuous functions. Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances. The novel strategy can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, the simulation results verify the benefit of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document