A novel adaptive control algorithm for the rejection of harmonics in a standard vibrator

2020 ◽  
pp. 107754632098020
Author(s):  
Chao Li ◽  
Chentao Mao ◽  
Zhangwei Chen
Keyword(s):  
Adaptive Control ◽  
Control Algorithm ◽  
Harmonic Distortion ◽  
Low Frequency ◽  
Control Input ◽  
Least Mean Square ◽  
Harmonic Cancellation ◽  
Harmonic Components ◽  
Improved Design ◽  
Adaptive Control Algorithm

Because of the nonlinear factors in the standard vibrators, the output waveform of the vibrators is usually distorted, especially in the low-frequency band. Traditionally, the harmonic distortion is suppressed by improved design of the vibrator and feedback control. However, these approaches can just suppress the harmonics to some extent. There are still residual harmonic components which cannot be eliminated. In this article, an adaptive control algorithm is proposed to eliminate the harmonics of the vibration calibration system by injecting harmonic components into the control input signal. Weighting vectors of the harmonic components are adaptively updated based on a modified filtered-x least mean square (MFxLMS) algorithm, which has both strong robustness and much less computational complexity. The convergence properties of the proposed MFxLMS algorithm are investigated, and it is further implemented in a real-time controller. Experimental results on a standard vibrator show excellent performances for harmonic cancellation. The harmonic distortion is close to 0% after the implementation of the proposed algorithm.

scholarly journals Key Technology Optimization and Development of New Energy Enterprises of Photovoltaic Power Generation

2021 ◽  
Author(s):  
Jiangping Nan
Keyword(s):  
Adaptive Control ◽  
Control Algorithm ◽  
Direct Method ◽  
Estimation Error ◽  
Random Noise ◽  
Harmonic Distortion ◽  
Current Control ◽  
Harmonic Amplitude ◽  
Unknown Parameters ◽  
Adaptive Control Algorithm

In order to improve the algorithm of time-varying parameters and unknown parameters adaptability, avoid assuming the approximate part deviation caused by the algorithm, this paper proposes a adaptive control algorithm, the algorithm based on lyapunov direct method to predict the output voltage in the process of estimating each parameter in a reasonable manner to parameter estimation error with the actual output current and current automatic adjustment. The adaptive control of current tracking is realized and the error caused by assuming voltage or current and neglecting line resistance is avoided in the predictive current control algorithm. The simulation results show that the tracking current can track the target current with high precision from t = 0 in the presence of random noise, and the power factor is close to 1, showing a good steady-state performance. Frequency domain waveform, the calculated harmonic distortion rate is 2.2418%, waveform quality is good and each harmonic amplitude is small. Conclusion: adaptive control algorithm can quickly and accurately realize current tracking and greatly suppress the noise.

Adaptive Control Algorithm of the RF-537 Controller

1997 ◽  
Vol 30 (7) ◽  
pp. 201-206
Author(s):  
Zbigniew Świder ◽  
Leszek Trybus
Keyword(s):  
Adaptive Control ◽  
Control Algorithm ◽  
Adaptive Control Algorithm

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

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