Model-free adaptive control method with variable forgetting factor for unmanned surface vehicle control

2019 ◽  
Vol 93 ◽  
pp. 101945
Author(s):  
Yulei Liao ◽  
Tingpeng Du ◽  
Quanquan Jiang
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Vehicle Control ◽  
Forgetting Factor ◽  
Unmanned Surface Vehicle ◽  
Model Free ◽  
Variable Forgetting Factor

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.

Constant Turning Force Adaptive Control Via Sliding Mode Control Design

1990 ◽  
Vol 112 (2) ◽  
pp. 308-312 ◽  
Author(s):  
Chih-Lyang Hwang ◽  
Bor-Sen Chen
Keyword(s):  
Adaptive Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Open Loop ◽  
Forgetting Factor ◽  
Mode Control ◽  
Variable Forgetting Factor ◽  
The Stability ◽  
Turning System ◽  
Time Form

In the constant turning force adaptive control (CTFAC) system, the open-loop gain will vary and the stability cannot be assured when a cutting tool cuts a workpiece at various cutting depths or spindle operates in different speeds. In this paper, the spirit of sliding mode control is extended into discrete-time form to combine with parameter estimation having variable forgetting factor to stabilize the turning system against the variable gain and unmodeled dynamics, such as nonlinear perturbations, inaccurate measurements etc.

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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