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

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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Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2017.2711961 ◽

2017 ◽

Vol 47 (10) ◽

pp. 3136-3147 ◽

Cited By ~ 76

Author(s):

Wei He ◽

Haifeng Huang ◽

Shuzhi Sam Ge

Keyword(s):

Neural Network ◽

Robotic Manipulator ◽

Network Control ◽

Time Varying ◽

Neural Network Control ◽

Adaptive Neural Network ◽

Adaptive Neural Network Control ◽

Output Constraints

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Adaptive neural network control of uncertain robotic manipulators with external disturbance and time-varying output constraints

Neurocomputing ◽

10.1016/j.neucom.2018.09.072 ◽

2019 ◽

Vol 323 ◽

pp. 108-116 ◽

Cited By ~ 12

Author(s):

Yuxiang Wu ◽

Rui Huang ◽

Xian Li ◽

Song Liu

Keyword(s):

Neural Network ◽

External Disturbance ◽

Robotic Manipulators ◽

Network Control ◽

Time Varying ◽

Neural Network Control ◽

Adaptive Neural Network ◽

Adaptive Neural Network Control ◽

Output Constraints

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

2017 36th Chinese Control Conference (CCC) ◽

10.23919/chicc.2017.8027873 ◽

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

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Adaptive neural network control for maglev vehicle systems with time-varying mass and external disturbance

Neural Computing and Applications ◽

10.1007/s00521-021-05874-2 ◽

2021 ◽

Author(s):

Yougang Sun ◽

Junqi Xu ◽

Guobin Lin ◽

Ning Sun

Keyword(s):

Neural Network ◽

External Disturbance ◽

Network Control ◽

Time Varying ◽

Neural Network Control ◽

Adaptive Neural Network ◽

Maglev Vehicle ◽

Adaptive Neural Network Control ◽

Vehicle Systems ◽

Varying Mass

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Discrete-time adaptive neural network control for steer-by-wire systems with disturbance observer

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115395 ◽

2021 ◽

pp. 115395

Author(s):

Yunlong Wang ◽

Yongfu Wang

Keyword(s):

Neural Network ◽

Discrete Time ◽

Disturbance Observer ◽

Network Control ◽

Neural Network Control ◽

Adaptive Neural Network ◽

Adaptive Neural Network Control ◽

Steer By Wire

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Adaptive Neural Network Control for Nonstrict-Feedback Uncertain Nonlinear Systems with Input Delay and Asymmetric Time-Varying State Constraints

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2021.07.020 ◽

2021 ◽

Author(s):

Boqiang Cao ◽

Xiaobing Nie ◽

Zhongwen Wu ◽

Changfeng Xue ◽

Jinde Cao

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

State Constraints ◽

Network Control ◽

Input Delay ◽

Time Varying ◽

Uncertain Nonlinear Systems ◽

Neural Network Control ◽

Adaptive Neural Network ◽

Adaptive Neural Network Control

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

Mathematical Problems in Engineering ◽

10.1155/2014/420713 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 6

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.

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