Adaptive inverse control of random vibration based on the filtered-X LMS algorithm

Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.

Simplified p-norm-like Constraint LMS Algorithm for Efficient Estimation of Underwater Acoustic Channels

Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint LMS algorithms, the p-norm-like （Ip） constraint LMS algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint LMS algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint LMS was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint LMS algorithms.

An improved WPM interference rejection system using an adaptive LMS algorithm

Based on the good time-frequency localization of the wavelet packet modulation （WPM） system and the analysis of the muhitone （MT） interference, an improved WPM narrow band interference （NBI） rejection system using an adaptive least mean square （LMS） algorithm is discussed in this paper. In the WPM interference rejection system, according to the structure of the wavelet packet tree （WFF） and the characteristic of the MT interference, the demodulated WPM signal which contains the interference component is filtered using an adaptive LMS filtering implementation. Theoretical analysis and simulation resuits show that the proposed method improves the bit error rate （BER） performance of the WPM system in the presence of MT interference.

Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Convergence analysis of filtered-X LMS algorithm with secondary path modeling error

A more relaxed sufficient condition for the convergence of filtered-X LMS (FXLMS) algorithm is presented. It is pointed out that if some positive real condition for secondary path transfer function and its estimates is satisfied within all the frequency bands, FXLMS algorithm converges whatever the reference signal is like. But if the above positive real condition is satisfied only within some frequency bands, the convergence of FXLMS algorithm is dependent on the distribution of power spectral density of the reference signal, and the convergence step size is determined by the distribution of some specific correlation matrix eigenvalues.Applying the conclusion above to the Delayed LMS (DLMS) algorithm, it is shown that DLMS algorithm with some error of time delay estimation converges in certain discrete frequency bands, and the width of which are determined only by the 'time-delay estimation error frequency' which is equal to one fourth of the inverse of estimated error of the time delay.

Study of Variable Step Size LMS Adaptive Algorithm Based on Variable Region

This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,

Parallel delayed LMS algorithm

A novel parallel delayed least-mean-square (PDLMS) algorithm is proposed by introduc-ing the parallel processing method into delayed LMS (DLMS) algorithm. Compared with DLMS, the al-gorithm presented has the property of smaller delay, higher throughput rate and faster convergence speed, while it also exhibits some de-correlation effect for the correlated input sequence. These prop-erties make it more suitable for the cases of high order filter with high convergence speed. At the same time, it can be mapped onto the high-speed and/or high-pipelined hardware structure directly.

Adaptive Controller for Vehicle Active Suspension Generated Through LMS Filter Algorithms

The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.

The Algorithms of Adaptive Active Noise Control Systems in a Duct

On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.

ADAPTIVE EXPONENT SMOOTHING GRADIENT ALGORITHM

A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

Levenberg-Marquardt(LM)algorithm is applied for the optimization of the heat transfer of a batch reactor.The validity of the approach is verified through comparison with experimental results.It is found that the mathematical model can properly describe the heat transfer relationships characterizing the considered system,with the error being kept within±2℃.Indeed,the difference between the actual measured values and the model calculated value curve is within±1.5℃,which is in agreement with the model assumptions and demonstrates the reliability and effectiveness of the algorithm applied to the batch reactor heat transfer model.Therefore,the present work provides a theoretical reference for the conversion of practical problems in the field of chemical production into mathematical models.

Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.

COMPENSATION CONTROL OF REAL-TIME UNBALANCE FORCE FOR ACTIVE MAGNETIC BEARING SYSTEM

Aiming at the stability and others properties of active magnetic bearing （AMB） system influenced by the periodic unbalance stimulation synchronous with rotor rotational speed, a new real-time adaptive feed-forward unbalance force compensation scheme is proposed based on variable step-size least mean square（LMS） algorithm as the feed-forward compensation controller. The controller can provide some suitable sinusoidal signals to com- pensate the feedback unbalance response signals synchronous with the rotary frequency, then reduce the fluctua- tion of the control currents and weaken the active control of AMB system. The variable step-size proportional to the rotational frequency is deduced by analyzing the principle of normal LMS algorithm and its deficiency in the application of real-time filtering of AMB system. Experimental results show that the new method can implement real-time unbalance force compensation in a wide frequency band, reduce the effect of unbalance stimulant force on the housing of AMB system, and provide convenience to improve rotational speed.

VARIABLE STRUCTURE CONTROL APPLIED IN ELECTRO-HYDRAULIC SERVO SYSTEM WITH ANN

The variable structure control （VSC） theory is applied to the electro-hydraulic servo system here. The VSC control law is achieved using Lyapunov method and pole placement. To eliminate the chattering phenomena, a saturation function is adopted. The proposed VSC approach is fairly robust to load disturbance and system parameter variation. Since the distortion. including phase lag and amplitude attenuation occurs in the system sinusoid response, the amplitude and phase control （APC） algorithm, based on Adaline neural network and using LMS algorithm, is developed for distortion cancellation. The APC controller is simple and can on-line adjust, thus it gives accurate tracking.

Design and Application of a DSP Controller for Giant Magnetostrictive Smart Structure

It is proposed a digital signal processing （DSP） controller used in the control system of six degree-of-freedom （DOF） giant magnetostrictive smart structure （GMSS）. Also are presented the controller design and its implementation, the embedded adaptive least mean-square （LMS） control algorithm and the real-time experiments of GMSS. Three programs are embedded in this controller. Of them are the adaptive LMS control algorithm and the other two which are used to search for the optimal values for the initial weight vectors. Moreover, the work flow and the control approach of the GMSS with the DSP controller are introduced. The experimental results indicate that the DSP controller has better control precision and real-time property, and the damping effect is up to 20 dB-30 dB.

THE DESIGN OF DYNAMIC SIMULATION SYSTEM ON EARTHQUAKE SURROUNDINGS

Objective To design a system that can simulate earthquake surroundings. In the surroundings, people can be familiar with the omen, strong shock and aftershock of earthquake, thus make right choices and get away when the disaster occurs. Methods The system consists of an electro hydraulic servo system, a whole information sound system and some lighting device; By using the adaptive inverse control method and LMS algorithms, the inverse model (i.e. the controller) is convergent rapidly; The software based on LabVIEW makes the parameters can be modified easily; There is a double closed loop structure in the system: an analog closed loop and a digital closed loop, and their parameters can be inspected in real time. Results The system is of very high reliability, and the desired vibration signal can be tracked exactly by output. Conclusion Earthquake surroundings is simulated vividly. Through the system, people can be familiar with earthquake phenomena, and know lots of knowledge of earthquake.

Improved adaptive filter and its application in acoustic emission signals

In order to de-noise and filter the acoustic emission(AE) signal, the adaptive filtering technology is applied to AE signal processing in view of the special attenuation characteristics of burst AE signal. According to the contradiction between the convergence speed and steady-state error of the traditional least mean square(LMS) adaptive filter, an improved LMS adaptive filtering algorithm with variable iteration step is proposed on the basis of the existing algorithms. Based on the Sigmoid function, an expression with three parameters is constructed by function translation and symmetric transformation.As for the error mutation, e(k) and e(k-1) are combined to control the change of the iteration step. The selection and adjustment process of each parameter is described in detail, and the MSE is used to evaluate the performance. The simulation results show that the proposed algorithm significantly increases the convergence speed, reduces the steady-state error, and improves the performance of the adaptive filter. The improved algorithm is applied to the AE signal processing, and the experimental signal is demodulated by an empirical mode decomposition(EMD) envelope to obtain the upper and lower envelopes. Then, the expected function related to the AE signal is established. Finally, the improved algorithm is substituted into the adaptive filter to filter the AE signal. A good result is achieved, which proves the feasibility of adaptive filtering technology in AE signal processing.

Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons:A Combined PCA and BP Neural Network Model

This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.

Synthesis and Design of 5G Duplexer Based on Optimization Method

A new optimization method is proposed to realize the synthesis of duplexers.The traditional optimization method takes all the variables of the duplexer into account,resulting in too many variables to be optimized when the order of the duplexer is too high,so it is not easy to fall into the local solution.In order to solve this problem,a new optimization strategy is proposed in this paper,that is,two-channel filters are optimized separately,which can reduce the number of optimization variables and greatly reduce the probability of results falling into local solutions.The optimization method combines the self-adaptive differential evolution algorithm(SADE)with the Levenberg-Marquardt(LM)algorithm to get a global solution more easily and accelerate the optimization speed.To verify its practical value,we design a 5 G duplexer based on the proposed method.The duplexer has a large external coupling,and how to achieve a feed structure with a large coupling bandwidth at the source is also discussed.The experimental results show that the proposed optimization method can realize the synthesis of higher-order duplexers compared with the traditional methods.

Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.