scholarly journals Error Signal Differential Term Feedback Enhanced Variable Step Size FxLMS Algorithm for Piezoelectric Active Vibration Control

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weiguang Li ◽  
Wei Wang ◽  
Bin Li ◽  
Zhichun Yang
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Active Vibration Control ◽  
Variable Step Size ◽  
Error Signal ◽  
Step Size ◽  
Fxlms Algorithm ◽  
Variable Step ◽  
Active Vibration ◽  
State Error

FxLMS (Filtered-x Least Mean Square) algorithm is widely used in the field of AVC (active vibration control) for its good convergence and strong adaptability. However, the convergence rate and steady-state error are mutually restricted for the fixed step FxLMS algorithm. Increasing step size μ to accelerate the convergence rate will result in larger steady-state error and even cause control divergence. In this paper, a new DVSFxLMS (error signal Differential term feedback Variable Step size FxLMS) algorithm is proposed by establishing nonlinear function between μ and error signal, while using differential term of the error signal as the feedback control function. Subsequently, a DVSFxLMS controller is designed to carry out the AVC simulation and experiments on cantilever beam with PSA (piezoelectric stack actuator). Simulation and experimental results show that the proposed DVSFxLMS algorithm has faster convergence rate and smaller steady-state error than the traditional FxLMS algorithm, which also has strong antinoise ability and adaptive control ability to quickly track the variable external disturbance.

A New Variable Step Size LMS Adaptive Filtering Algorithm and its Simulations

2014 ◽  
Vol 513-517 ◽  
pp. 3736-3739 ◽  
Author(s):  
Xue Li Wu ◽  
Zi Zhong Tan ◽  
Liang Gao
Keyword(s):  
Steady State ◽  
Adaptive Filtering ◽  
Functional Relationship ◽  
Convergence Speed ◽  
Variable Step Size ◽  
Error Signal ◽  
Step Size ◽  
Variable Step ◽  
The Absolute ◽  
State Error

. Aiming at the disadvantage of the variable step size LMS adaptive filtering algorithms' convergence speed contradicting its steady-state error, a novel non-liner functional relationship between μ (n) and error signal e (n) was established. On the basis of the functional relationship, a new algorithm of variable step size LMS adaptive filtering was presented. The step size factor of the new algorithm is adjusted by the absolute value of the product of the current and former errors. It also uses the absolute estimation error compensation terms disturbance to speed up the convergence of adaptive filter tap weight vector. At the same time, the algorithm considers the relationship between step length of the last iteration and the former M error signal. As a result the algorithm has higher convergence characteristic and small steady state error. The theoretical analysis and simulation results show that the new algorithm has faster convergence speed, lower steady state error and better performance of noise suppression, also show the overall performance of this algorithm exceeds some others condition.

scholarly journals New feedforward filtered-x least mean square algorithm with variable step size for active vibration control

2018 ◽  
Vol 38 (1) ◽  
pp. 187-198 ◽  
Author(s):  
Yubin Fang ◽  
Xiaojin Zhu ◽  
Zhiyuan Gao ◽  
Jiaming Hu ◽  
Jian Wu
Keyword(s):  
Vibration Control ◽  
Active Vibration Control ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Large Step ◽  
Variable Step ◽  
Active Vibration

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

Variable Step Size P&O MPPT Algorithm on 250 W Interleaved Flyback Converter

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Y. Munandar K. ◽  
Eka Firmansyah ◽  
Suharyanto Suharyanto
Keyword(s):  
Steady State ◽  
Dynamic Performance ◽  
Solar Irradiation ◽  
Variable Step Size ◽  
Step Size ◽  
Photovoltaic Panel ◽  
Point Tracking ◽  
Variable Step ◽  
Power Point ◽  
State Error

Maximum power point tracking (MPPT) algorithm seek the MPP to maximize delivered the power of a photovoltaic panel. From several MPPT algorithms, the perturb and observe (P&O) algorithm is commonly used algorithm because of its easy implementation. However, it is not the most efficient algorithm due to the perturbation step is fixed. By using the high step size, the MPP tracking became fast but there would be a high steady state error and by using the low step size, there would be less steady state error but the MPP tracking became slow. Resulting in a waste of energy in steady-state conditions when the working point passes through the MPP and poorly dynamic performance indicated when the setpoint changes due to solar irradiation changes. In this paper, a modification variable step-size of the P&O algorithm has been proposed with setting the step-size automatically at each point of work. To validate the concept of modification variable step-size, simulation using PSIM has been carried out. Compared with the conventional P&O method with fixed step-size, the proposed modified P&O method can increase tracking speed and efficiency in the system.

A Variable Step Size LMS Based on Sparsity for System Identification

2013 ◽  
Vol 475-476 ◽  
pp. 1060-1066
Author(s):  
X.Q. Chen ◽  
Hua Ju ◽  
Wei Fan ◽  
W.G. Huang ◽  
Z.K. Zhu
Keyword(s):  
Steady State ◽  
System Identification ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Practical Applications ◽  
Variable Step ◽  
Size Adjustment ◽  
State Error

In many practical applications, the impulse responses of the unknown system are sparse. However, the standard Least Mean Square (LMS) algorithm does not make full use of the sparsity, and the general sparse LMS algorithms increase steady-state error because of giving much large attraction to the small factor. In order to improve the performance of sparse system identification, we propose a new algorithm which introduces a variable step size method into the Reweighted Zero-Attracting LMS (RZALMS) algorithm. The improved algorithm, whose step size adjustment is controlled by the instantaneous error, is called Variable step size RZALMS (V-RZALMS). The variable step size leads to yielding smaller steady-state error on the premise of higher convergent speed. Moreover, the sparser the system is, the better the V-RZALMS performs. Three different experiments are implemented to validate the effectiveness of our new algorithm.

scholarly journals Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm

2016 ◽  
Vol 6 (2) ◽  
pp. 923-926
Author(s):  
S. Radhika ◽  
A. Sivabalan
Keyword(s):  
Steady State ◽  
Adaptive Filters ◽  
Impulsive System ◽  
Variable Step Size ◽  
Step Size ◽  
Impulsive Interference ◽  
Variable Step ◽  
Improved Performance ◽  
Maximum Correntropy Criterion ◽  
State Error

Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD) error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.

scholarly journals A Variable Step Size for Acoustic Echo Cancellation Using Normalized Sub band Adaptive Filter

2013 ◽  
Vol 6 (3) ◽  
pp. 449-455
Author(s):  
Harjeet Kaur Ojhla ◽  
Dr. Rajneesh Talwar ◽  
Jyoti Darekar
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Adaptive Filter ◽  
Weight Vector ◽  
Variable Step Size ◽  
Mean Square ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter ◽  
Normalized Subband Adaptive Filter

Numerous various step size normalized least mean square (VSS-NLMS)Algorithms have been derived to solve the problem of fast convergence rate and low mean square error.Here we find out the ways to control the step size. A normalized subband adaptive filter algorithm uses a fixed and variable step size, which is chosen as a trade-off between the steady-state error and the convergence rate. A variable step size for normalized subband adaptive filter is derived by minimizing the mean-square deviation between the optimal weight vector and the estimated weight vector at each instant of time. The variable step size is presented in terms of error variance. Therefore, we verify thedifferent  algorithmseither they are capable of tracking in stationary and non-stationary environments. The results show good tracking ability and low misalignment of the algorithm in system identification. Parameters are tracking, steady state errors, and misalignment, environment, step size.

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

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