scholarly journals Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fengxia Xu ◽  
Yao Cheng ◽  
Hongliang Ren ◽  
Shili Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Delay System ◽  
Network Control ◽  
Identification Algorithm ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Time Varying Delay ◽  
Adaptive Neural Network Control ◽  
Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

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.

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