Command filter‐based event‐triggered adaptive neural network control for uncertain nonlinear time‐delay systems

2020 ◽  
Vol 30 (16) ◽  
pp. 6363-6382
Author(s):  
Baomin Li ◽  
Jianwei Xia ◽  
Wei Sun ◽  
Ju H. Park ◽  
Zong‐Yao Sun
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Network Control ◽  
Delay Systems ◽  
Neural Network Control ◽  
Time Delay Systems ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Event Triggered

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

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