scholarly journals A Smoothed Algorithm with Convergence Analysis under Generalized Maximum Correntropy Criteria in Impulsive Interference

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1099
Author(s):  
Hua Qu ◽  
Youwei Shi ◽  
Jihong Zhao
Keyword(s):  
Steady State ◽  
Impulsive Noise ◽  
Weighted Average ◽  
Variable Step Size ◽  
Gradient Vector ◽  
Step Size ◽  
State Behavior ◽  
Mean Square Stability ◽  
Variable Step ◽  
Steady State Behavior

The generalized maximum correntropy criterion (GMCC) algorithm is computationally simple and robust against impulsive noise but it suffers from slow convergence speed as it is derived and based on stochastic gradient, which only use the current data sample. In order to deal with this issue, a smoothed GMCC algorithm (SGMCC) is proposed. In the SGMCC algorithm, instead of taking the exponential weighted average of gradient vector to approximate the expectation of the gradient vector, we take the exponential weighted average of the variable step-size so that the SGMCC algorithm can be viewed as a sign GMCC algorithm with smoothed variable step-size. Moreover, convergence performance analyses are derived in terms of variable step-size, mean-square stability and steady-state behavior to demonstrate the robustness of the proposed algorithm. At last, simulation comparisons show that the proposed algorithm is robust against impulsive noise and converges fast with lower computational complexity. Also, for the steady-state behavior, simulation results verify that the simulated value matches well with the theoretical one.

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.

scholarly journals Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4153 ◽  
Author(s):  
Adeel Feroz Mirza ◽  
Majad Mansoor ◽  
Qiang Ling ◽  
Muhammad Imran Khan ◽  
Omar M. Aldossary
Keyword(s):  
Steady State ◽  
Pid Controller ◽  
Superior Performance ◽  
Variable Step Size ◽  
Step Size ◽  
Pv System ◽  
Fast Tracking ◽  
Point Tracking ◽  
Incremental Conductance ◽  
Variable Step

In this article, a novel maximum power point tracking (MPPT) controller for the fast-changing irradiance of photovoltaic (PV) systems is introduced. Our technique utilizes a modified incremental conductance (IC) algorithm for the efficient and fast tracking of MPP. The proposed system has a simple implementation, fast tracking, and achieved steady-state oscillation. Traditional MPPT techniques use a tradeoff between steady-state and transition-state parameters. The shortfalls of various techniques are studied. A comprehensive comparative study is done to test various existing techniques against the proposed technique. The common parameters discussed in this study are fast convergence, efficiency, and reduced oscillations. The proposed method successfully addresses these issues and improves the results significantly by using a proportional integral deferential (PID) controller with a genetic algorithm (GA) to predict the variable step size of the IC-based MPPT technique. The system is designed and tested against the perturbation and observation (P&O)-based MPPT technique. Our technique effectively detects global maxima (GM) for fast-changing irradiance due to the adopted GA-based tuning of the controller. A comparative analysis of the results proves the superior performance and capabilities to track GM in fewer iterations.

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 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.

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