Redefined Output Model-Free Adaptive Control Method and Unmanned Surface Vehicle Heading Control

2020 ◽  
Vol 45 (3) ◽  
pp. 714-723 ◽  
Author(s):  
Yulei Liao ◽  
Quanquan Jiang ◽  
Tingpeng Du ◽  
Wen Jiang
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Unmanned Surface Vehicle ◽  
Model Free ◽  
Heading Control

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 Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Discrete Time ◽  
Control Method ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Free ◽  
Time Varying Parameters ◽  
Learning Machine ◽  
Linearized Model

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.

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