scholarly journals Unmanned surface vessel heading control of model-free adaptive method with variable integral separated and proportion control

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141983158
Author(s):  
Quanquan Jiang ◽  
Yulei Liao ◽  
Ye Li ◽  
Yugang Miao ◽  
Wen Jiang ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Separation Factor ◽  
Control Method ◽  
Compact Form ◽  
Proportional Control ◽  
Model Free ◽  
Heading Control ◽  
Unmanned Surface Vessel ◽  
Integral Separation ◽  
Surface Vessel

Based on model-free adaptive control theory, the heading control problem of unmanned surface vessels under uncertain influence is explored. Firstly, the problems of compact form dynamic linearization model-free adaptive control method applied to unmanned surface vessel heading control are analyzed. Secondly, by introducing proportional control and variable integral separation factor, an variable integral separation model-free adaptive control algorithm with proportional control is proposed. The introduction of proportional control and variable integral separation factor solves the problems of oscillation, instability, and integral saturation when rudder angle is controlled directly to control the heading of unmanned surface vessel with compact form dynamic linearization model-free adaptive control method. Finally, the effectiveness of the method is verified by the simulation and field experiments results of heading control with model perturbation and system time delay in unmanned surface vessel heading subsystem.

Anti-noise model-free adaptive control and its application in the circulating fluidized bed boiler

2020 ◽  
pp. 095965182097937
Author(s):  
Na Dong ◽  
Wenjin Lv ◽  
Shuo Zhu ◽  
Donghui Li
Keyword(s):  
Adaptive Control ◽  
Complete Convergence ◽  
Circulating Fluidized Bed ◽  
Control Method ◽  
Noise Model ◽  
Good Resistance ◽  
Industrial Systems ◽  
Noise Interference ◽  
Model Free ◽  
Tracking Differentiator

Model-free adaptive control has been developed greatly since it was proposed. Up to now, model-free adaptive control theory has become mature and tends to be an effective solution for complex unmodeled industrial systems. In practical industrial processes, most control systems are inevitably accompanied by noise that will result in indelible error and may further cause inaccurate feedback to the output. In order to solve this kind of problem with model-free technique, this article incorporates an improved tracking differentiator into model-free adaptive control. After that, the anti-noise model-free adaptive control method with complete convergence analysis is proposed. Meanwhile, numerical simulation proves that the improved control method can quickly track a given signal with good resistance to noise interference. Finally, the effectiveness and practicability of the proposed algorithm are verified by experiments through the control of drum water level of circulating fluidized.

scholarly journals Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Zhijian Huang ◽  
Xinze Liu ◽  
Jiayi Wen ◽  
Guichen Zhang ◽  
Yihua Liu
Keyword(s):  
Adaptive Control ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
System Stability ◽  
Lyapunov Stability Analysis ◽  
Action Network ◽  
Unmanned Surface Vessel ◽  
Control Character ◽  
Surface Vessel ◽  
Parallel Action

The feedback PID method was mainly used for the navigating control of an unmanned surface vessel (USV). However, when the intelligent control era is coming now, the USV can be navigated more effectively. According to the USV character in its navigating control, this paper presents a parallel action-network ADHDP method. This method connects an adaptive controller parallel to the action network of the ADHDP. The adaptive controller adopts a RBF neural network approximation based on the Lyapunov stability analysis to ensure the system stability. The simulation results show that the parallel action-network ADHDP method has an adaptive control character and can navigate the USV more accurately and rapidly. In addition, this method can also eliminate the overshoot of the ADHDP controller when navigating the USV in various situations. Therefore, the adaptive stability design can greatly improve the navigating control and effectively overcome the ADHDP algorithm limitation. Thus, this adaptive control can be one of the intelligent ADHDP control methods. Furthermore, this method will be a foundation for the development of an intelligent USV controller.

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