Variable Step-size LMS Research Articles

Improved performance of a shunt hybrid active power filter by a robust exponential functional link network-based nonlinear adaptive filter control to enhance power quality

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved.

A novel approach to geometric algebra-based variable step-size LMS adaptive filtering algorithm

A novel approach to geometric algebra-based variable step-size LMS adaptive filtering algorithm

Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function

Contrasting the dilemma that the traditional time-domain least mean square (LMS) algorithm in the existing satellite navigation receiver anti-interference system cannot satisfy the convergence time is short and maintain a low level of steady-state error at the same time, an inverse cosine variable step size LMS algorithm (ICVS-LMS) is proposed. To begin with, the LMS algorithm, with a fixed step size focuses on its effectiveness in attenuating and suppressing interference signals, is analyzed, and then the proposed ICVS-LMS algorithm is analyzed. In conclusion, both the ICVS-LMS algorithm and the traditional algorithm are simulated and compared in terms of their effectiveness in suppressing interference in satellite navigation signals. The experimental results demonstrate that the improved algorithm significantly reduces convergence time while maintaining a small steady-state error. The improved algorithm demonstrates high robustness and an obvious suppression effect on interference signals. The anti-interference performance is 8.41–12.22% higher than that of the proposed algorithm.

An incremental noise constrained LMS algorithm

While a lot of research has focused on diffusion-based algorithms for distributed estimation over adaptive networks, incremental algorithms have not been studied to that extent. Here we present an incremental scheme-based distributed least mean square algorithm that uses the variance of the additive noise as a constraint. The proposed algorithm is derived and then its theoretical performance analysis is presented. Simulation results for different scenarios are then presented and it is shown that the simulation results corroborate very well the theoretical findings. More importantly, our algorithm outperforms, a recently proposed algorithm, the variable step-size incremental-based LMS (VSSILMS) algorithm.

Robust DOA Estimation Using VSS-LMS With Low-Rank Matrix Approximation

Direction-of-arrival (DOA) estimation performance of adaptive filtering-based methods, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , least mean square (LMS), degrades significantly in adverse conditions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> at low signal-to-noise ratio (SNR), small number of antenna array elements and snapshots, and in presence of two closely spaced signal sources). Moreover, these estimation methods require many regulating parameters to efficiently update the step size, which are very difficult to tune manually in practical scenarios. Although the subspace decomposition-based methods, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., multiple signal classification (MUSIC), provide somewhat better performance in adverse conditions, they are based on the statistical properties of the signal such as spatial covariance matrix (SCM) and its eigenvalue decomposition (EVD), resulting in high computational complexity. In this paper, a robust DOA estimation method is proposed based on a variable step size LMS algorithm with low rank matrix approximation (LRMA), where the received signal de-noising problem is formulated first as a LRMA problem directly using the received signal observations instead of the SCM, and then the variable step size is calculated from the predicted instantaneous error and the estimated powers of reference and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de-noised</i> signals. The output spatial spectrum is determined by the reciprocal of the antenna array pattern, and the high peaks in the output spatial spectrum give the received signals estimated directions. The proposed method updates the step size efficiently without choosing any regulating parameter, and achieves improved performance even in adverse conditions due to the de-noising feature of LRMA with low computational complexity. Further, we also conduct the convergence and stability analyses of the proposed iterative estimation method with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de-noised</i> signal. Numerical results demonstrate that the proposed method outperforms state-of-the-art methods especially those, which yielded based on adaptive filtering.

Control Study for Compensating Rotor Vibration of Four-DOF Six-Pole Hybrid Magnetic Bearings Based on Variable Step Size LMS Algorithm

In order to solve the problem that the unbalance vibration caused by rotor mass eccentricity of the four-degree-of-freedom (four-DOF) six-pole hybrid magnetic bearings (HMBs), a feed-forward compensation control strategy based on variable step size least-mean-square (LMS) adaptive filter is proposed. First, the structure, operation principle and mathematical model of the four-DOF six-pole HMBs are introduced, the motion law of rotor vibration is analyzed, and the dynamic equation is derived. Second, the variable step size LMS adaptive filter is improved by using the Versoria function. The step size of LMS algorithm is adjusted adaptively by using Versoria function model and Takagi–Sugeno (TS) fuzzy inference machine. The LMS adaptive filter can generate a signal with the same frequency, amplitude, and phase as the rotating speed to offset the vibration signal, so as to realize the vibration compensation control of the four-DOF six-pole HMB rotor. Finally, the simulations and experiments not only prove the effectiveness of this method but also show that the method has good compensation accuracy. The result shows that the Versoria function used in variable step size LMS adaptive filter for vibration compensation is better than the traditional variable step size filtering algorithm based on Sigmoid function.

DSTATCOM using model predictive control associated with LMS control

ABSTRACT In this paper, a type of variable step size LMS named weighted zero attracting variable step size (WZAVSSLMS) is equipped with the control algorithm for governing DSTATCOM. In WZAVSSLMS, the ability of zero attracting nature to a variable step size (VSSLMS) for improving convergence rate is observed. WZAVSSLMS algorithm-based fundamental extractor is developed to identify the fundamental quantity of the load current for a three-phase four-wire DSTATCOM with non-linear load in this work. This type of variable step size LMS has provided a concession for quick convergence rate and steady-state error. Precision and convergence of responses are accomplished by the learning rate control. As a result of these features over conventional LMS, the authors choose WZAVSSLMS based approach in well-known DSTATCOM system to compensate power quality issues. The fundamental extraction from the distorted load current constitutes the main divergences in the proposed study. Further, the Finite control set model predictive control (FCS MPC) of the distribution static compensator (DSTATCOM) using Weighted Aggregated Sum Product Assessment Solution (WASPAS) method is implemented.

Zero-Drift Error Compensation Method for Electronic Balance Based on Adaptive Filter With Autostep LMS

This article presents a new approach of compensating the zero-drift error of electronic balance based on automatic step adjustment and least mean square method (Autostep-LMS), and the detailed algorithm of Autostep-LMS is given. Unlike the classical variable step-size LMS algorithms, such as Aboulnasr-LMS algorithm, Versoria-function-LMS algorithm, and Gradient-LMS algorithm, the step control parameters of Autostep-LMS algorithm are automatically adjusted, which is helpful to reduce the complexity of how to adjust these algorithm parameters. By comparing with these classical variable step-size LMS algorithms, the simulation results show that the Autostep-LMS algorithm has faster convergence speed and smaller steady-state error. This Autostep-LMS algorithm is applied to the electronic balance, which range is 200 g and the resolution is 1 mg, and the experimental results show that the zero-drift error, repeatability error, and nonlinear error of the electronic balance are all less than 5 mg, which is superior to the recommendation of the class II electronic balance.

Performance optimization of the active noise control algorithm based on logarithmic function and its application in secondary channel online identification ANC system

In this paper, several methods about the active noise control (ANC) system are studied. The common adaptive recognition system based on the variable step filter x least mean square (FxLMS) algorithm are elaborated. By introducing the momentum term into the variable step algorithm, the value range of the variable step is increased, so as to speed up the convergence of the algorithm and improve the effect of noise reduction. The weight of the control filter based on the improved logarithmic function variable step size least mean square (VSSLMS) algorithm is updated in the improved second channel online recognition algorithm. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results show that the improved algorithm structure could improve the convergence speed and reduce the noises of the whole system at three different primary noises. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results under three different primary noises show that the improved algorithm structure could improve the convergence speed and reduce the noise of the whole system.

Music Genre Classification Algorithm Based on Multihead Attention Mechanism

Retrieving music information is indispensable and divided into multiple genres. Music genres can be attributed to set categories, which are the indispensable functions of intelligent music recommendation systems. To improve the effect of music genre classification and model construction, combined with the music genre classification algorithm, this paper combines the multihead attention mechanism to study the music genre classification algorithm model, and it analyzes the key technology of music beamforming. Moreover, this paper has made a detailed description and derivation of the array antenna model, the principle of music beamforming, and the performance evaluation criteria of music adaptive beamforming. In the second half, the nonblind classical LMS algorithm, RLS algorithm, and variable step size LMS algorithm of adaptive beamforming are studied in detail. A music genre classification algorithm model based on the multihead attention mechanism is constructed. It can be seen from the experimental research that the music genre classification algorithm based on the multihead attention mechanism proposed in this paper has obvious advantages compared with the traditional algorithm, and it has a certain role in music genre classification.

Adaptive Filter based on Absolute Average Error Adaptive Algorithm for Modeling System

Adaptive identification of the bandpass finite impulse response (FIR) filtering system is proposed through this paper using variable step-size least mean square (VSS-LMS) algorithm called absolute average error-based adjusted step-size LMS as an adapted algorithm. This algorithm used to design an adaptive FIR filter by calculating the absolute averaged value for the recently assessed error with the previous one. Then, the step size has been attuned accordingly with consideration of the slick transition of the step size from bigger to smaller to score an achievement through high convergence rate and low steady-state misadjustment over the other algorithms used for the same purpose. The simulation results through the computer demonstrate remarkable performance compared to the traditional algorithm of LMS and another VSS-LMS algorithm (normalized LMS) which used in this paper for the designed filter. The powerful of the filter has been served in the identification system, bandpass filter has been chosen to be identified in the proposed adaptive system identification. It reports conceivable enhancements in the modeling system concerning the time of convergence, which is well-defined as a fast and steady-state adjustment defined with a low level. The designed filter identified the indefinite system with less than 10 samples; meanwhile, other algorithms were taking more than 20 samples for identification. Alongside the fine behavior of preserving the tradeoff between miss adjustment and the capability of tracking, the fewer calculations and computations regarding the algorithm requirement make the applied real-time striking.

Improved Variable Step Size Least Mean Square Algorithm for Pipeline Noise

In this study, we employ the active noise control (ANC) method to eliminate the low-frequency part of the noise generated by the rotation of the axial fan in heating, ventilation, and air-conditioning (HVAC) pipelines. Because the traditional variable step size least mean square (VSS-LMS) algorithm has poor tracking performance, we propose a variable step size filtered-X least mean square (FXLMS) algorithm based on the arctangent function to improve the adaptive filtering method of the convergence speed and noise cancellation effect. The step size of the proposed algorithm can be adjusted according to the error. When the error signal is significant, a larger step can be obtained, and when the error is small, the step size smoothness of the algorithm can be optimized. Compared with the traditional VSS-LMS algorithm, the convergence speed of the proposed algorithm is increased by 29%, the noise reduction effect is enhanced by 19%, and the mean square error (MSE) is reduced by 23% (0.0084). In addition, we developed a hardware experimental platform based on noise characteristics. In the noise reduction test using a GB/T 5836.2-06 standard PVC pipeline, the system reduced the noise by 12–17 dB.

Sound source localization based on improved adaptive beamforming

In adaptive systems, the least mean square error (LMS) adaptive algorithm is widely used. The variable step size LMS algorithm (SVSLMS) based on the Sigmoid function has greatly improved the convergence speed, and the normalized LMS algorithm is stable Sexual performance has certain advantages. Therefore, improvement is made on this basis, the SVSLMS algorithm is normalized, and the iteration speed is increased by changing the iteration factor, and the stability of the algorithm is also improved. This paper uses MATLAB simulation tools to verify the superiority of the new algorithm. Finally, the improved algorithm is applied to beamforming to estimate the azimuth of arrival of the simulated sound source signal at the partial discharge point of the submarine power cable.

Control of DSTATCOM using nonparametric variable step-size NLMS: An experimental study

The development of distribution static compensator (DSTATCOM) using nonparametric variable step-size normalized least mean square (NPVSS-NLMS) based control technique is presented in this paper. Numerous features of DSTATCOM such as harmonic alleviation, reactive power compensation and load balancing are demonstrated using NPVSS-NLMS controller. The laboratory prototype of shunt compensator is developed using a dSPACE DS1104 R&D controller having TMS320F240 as a slave DSP. Both MATLAB/SIMULINK based simulations and experimental test results are presented for demonstrating the steady state as well as dynamic performance of DSTATCOM. The developed control technique is compared with variable step size LMS (VSS-LMS) and modern control techniques viz. sliding mode controller (SMC) and model predictive control (MPC) in terms of filter type, weight convergence, complexity, harmonic compensation and sampling time taken. The NPVSS-NLMS based control algorithm for shunt compensator is performing satisfactory in all the above mentioned terms over conventional VSS-LMS controller. The scheme is found to be effective in reducing the total harmonic distortion (THD) of supply current as per IEEE-519 standard.

Deep unfolding based hyper‐parameter optimisation for self‐interference cancellation in LTE‐A/5G‐transceivers

Deep unfolding is a very promising concept that allows to combine the advantages of traditional estimation techniques, such as adaptive filters, and machine learning approaches, like artificial neural networks. Focusing on a challenging self-interference problem occurring in frequency-division duplex radio frequency transceivers, namely modulated spurs, it is shown that deep unfolding enables remarkable performance gains. Based on the hyper-parameter optimisation of several least-mean squares (LMS) variants and the recursive-least squares algorithm, the importance of a well-chosen loss function are highlighted. Especially the variable step-size LMS and the transform-domain LMS vastly benefit without increased runtime complexity.

Adaptive nonlinear ANC system based on time-domain signal reconstruction technology

When the primary reference signal obtained by the existing Active noise control (ANC) system is not accurate, the control effect of interior noise will be reduced or even fail. Considering the fault-tolerate and robustness of the system, the study proposes an adaptive nonlinear ANC system for interior noise, which contains noise signal decomposition, multi-network reconstruction model and Variable step-size LMS (VSS-LMS) algorithm. The noise signal decomposition method is used to address the non-stationary of the interior noise; Based on the signal components, the multi-network model for the noise signal reconstruction of passenger ear-sides is designed, which is pre-trained by a restricted Boltzmann machine for improved reconstruction accuracy and realize the adaptive extraction of signal features; And then based on the reconstruction signal components, the controller weights of corresponding components are adaptively updated by the VSS-LMS algorithm to control the passenger ear-sides noise. The effectiveness of the proposed adaptive nonlinear ANC system is validated using noise signal sources collected from a vehicle. Compared with the different ANC systems, the proposed system is superior in terms of fault-tolerant and robustness, which can guarantee stable work of the interior noise control.

Robust three-Phase distribution system frequency measurement using a variable step-size LMS

Frequency estimation is an important power quality parameter that is also useful for the protection and control of distributed generators. This paper proposes a robust three-phase distribution system frequency estimation algorithm based on the widely linear complex least mean square using a variable step-size to provide accurate frequency estimation and fast convergence during islanding of distributed generation (DG) and faults. The performance of the proposed method was evaluated in islanding events of DG. Besides, unbalanced analytical signals in amplitude and phase were evaluated as well as the impacts of the sampling rate and noise. The proposed algorithm was compared to other least mean square-based methods and good results were achieved.

A Comprehensive Study of Adaptive LNA Nonlinearity Compensation Methods in Direct RF Sampling Receivers

This paper studies the effects of nonlinear distortion of Low Noise Amplifier (LNA) for the multichannel direct-RF sampling receiver (DRF). The main focus of our work is to study and compare the effectiveness of the different adaptive compensation algorithms, including the inverse-based and subtract-based Least Mean Square (LMS) algorithm with a fixed and variable step size. The models for the compensation circuits have been analytically derived. As the major improvements, the effectiveness of the compensation circuits under the ADC quantization noise effect is evaluated. The bit-error-rates (BER) in dynamic signal-to-noise ratio (SNR) scenarios are calculated. We have proposed the use of variable step-size LMS (VLMS) to shorten the convergence time and to improve the compensation effect in general.&#x0D; To evaluate and compare different compensation methods, a complex Matlab model of the Ultra high frequency (UHF) DRF with 4-QPSK channels was implemented. The simulation results show that all compensation methods significantly improve the receiver performance, with the convergence time of the VLMS algorithm does not exceed 5.104 samples, the adjacent channel power ratios (ACPR) are reduced more than 30 dBc, and the BERs decrease by 2­–3 orders of magnitude, compared with the non-compensated results. The simulation results also indicate that the subtraction method in general has better performance than the inversion method.

Adaptive Processing for EM Telemetry Signal Recovery: Field Data from Sichuan Province

This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals.

A novel sign variable step size LMS (SiVSS-LMS) algorithm for adaptive beamforming

Beamforming algorithms are designed for generating main beams in the direction of interest and place nulls in the direction of interferences. Adaptive beamforming can be achieved by various optimization criteria. The criteria for an efficient design of algorithm is the speed of convergence, deeper null placement, better signal to noise ratio, and robustness. Motivated by the reduction in the computational complexity a new adaptive beamforming algorithm based on variable step size is proposed in this paper called sign variable step size LMS algorithm. The proposed algorithm is applied to the uniform linear array antenna. Simulation results show that it gives a better null placement in the direction of interference and the signal to noise ratio is improved upon the existing algorithms with less computational complexity.