Adaptive Iterative Method Research Articles

Efficient magnetic forward modeling for strongly magnetized bodies: An iterative integral equation approach

Magnetic surveying encounters challenges in high-precision detection and inversion on strongly magnetized bodies. Nonuniform magnetization caused by self-demagnetization and mutual magnetization makes magnetic field calculation using analytical formulas impractical. The previous discrete numerical methods have suffered from computational inefficiency or low accuracy. To resolve this issue, we develop an algorithm to solve the magnetic field integral equation for calculating the magnetization state of high-susceptibility bodies, combining the improved spatial convolution forward approach with the adaptive relaxation iteration method. To address the excessive computational burden in the magnetization field between 3D voxel arrays of the discrete model, we construct the circular convolution kernel matrix directly when the subsurface space is divided with an equidistant grid, significantly reducing redundant computations. Then, the fast Fourier transform is used to accelerate the forward discrete circular convolution operation. In addition, we derive an iterative computation scheme that adaptively adjusts the relaxation factor based on magnetic field changes to correct the effective magnetization for algorithm convergence. From a pragmatic perspective, equating the cuboid to equal-volume sphere subdivisions is developed to enhance computational efficiency further by approximately 50% despite sacrificing slight accuracy. The sphere and spherical shell models validate the algorithm accuracy, efficiency, and convergence. Furthermore, we analyze the characteristics of the mutual magnetization effect among magnetic bodies under different magnetization conditions. Finally, the applicability of the algorithm is demonstrated with a realistic example. Our algorithm shows promise for precisely exploring highly magnetized minerals, underground pipelines, and unexploded ordnance.

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Locating the informative frequency band of rolling bearing fault signals is of great significance for feature extraction and fault diagnosis. Benefiting from the adjustable center frequency and bandwidth as well as the similarity to impulse-like characteristics induced by bearing failures, Morlet wavelets are commonly used in resonance demodulation. However, fault impulses are highly susceptible to contamination by strong noise, which impedes the efficacy of existing wavelet parameter selection strategies and frequency band optimization methods. In this paper, an adaptive Morlet wavelet-based iterative filtering (AMIF) method is proposed for frequency band optimization under strong noise. The resonance frequency band is pinpointed based on adaptive Morlet wavelet filter banks, with off-band noise being canceled and fault features being refined during the level-by-level filtering process. Additional iterative operations are leveraged to enhance fault features of in-band signals to facilitate the optimization of the filtering parameters. Effectiveness of the proposed AMIF method and its superiority over the wavelet packet transform-based kurtogram and minimum entropy deconvolution are verified through simulation and experimental analysis. The results demonstrate that AMIF can accurately localize the informative frequency band, thereby extracting high-quality fault features, making it suitable for bearing fault diagnosis under strong noise condition with different fault types.

Distributed Optimization of Multi-Microgrid Integrated Energy System with Coordinated Control of Energy Storage and Carbon Emissions

The mutual optimization of a multi-microgrid integrated energy system (MMIES) can effectively improve the overall economic and environmental benefits, contributing to sustainability. Targeting a scenario in which an MMIES is connected to the same node, an energy storage coordination control strategy and carbon emissions management strategy are proposed, and an adaptive step-size method is applied to improve the distributed optimization of MMIESs based on the alternating direction multiplier method (ADMM). Firstly, the basic framework of MMIESs is established, and a coordinated control strategy limiting the time of charge and the discharge of the battery storage system (BSS) is proposed. Then a multi-objective optimization model based on operating and environmental cost is formulated. Considering that different microgrids may be managed by different operators and a different convergence speed of multi-objective optimization iteration, an adaptive step-size distributed iterative optimization method based on ADMM is used, which can effectively reduce the cost and protect the privacy of each microgrid. Finally, a system composed of three microgrids is taken as an example for simulation analysis. The results of distributed optimization are accurate, and the proposed coordinated control strategy can effectively enhance the revenue of ESS, which verifies the effectiveness of the proposed method.

The defect feature extraction of ultrasonic phased array detection based on adaptive region growth.

An ultrasonic phased array defect extraction method based on adaptive region growth is proposed, aiming at problems such as difficulty in defect identification and extraction caused by noise interference and complex structure of the detected object during ultrasonic phased array detection. First, bilateral filtering and grayscale processing techniques are employed for the purpose of noise reduction and initial data processing. Following this, the maximum sound pressure within the designated focusing region serves as the seed point. An adaptive region iteration method is subsequently employed to execute automatic threshold capture and region growth. In addition, mathematical morphology is applied to extract the processed defect features. In the final stage, two sets of B-scan images depicting hole defects of varying sizes are utilized for experimental validation of the proposed algorithm's effectiveness and applicability. The defect features extracted through this algorithm are then compared and analyzed alongside the histogram threshold method, Otsu method, K-means clustering algorithm, and a modified iterative method. The results reveal that the margin of error between the measured results and the actual defect sizes is less than 13%, representing a significant enhancement in the precision of defect feature extraction. Consequently, this method establishes a dependable foundation of data for subsequent tasks, such as defect localization and quantitative and qualitative analysis.

Adaptive iterative optimization method for spectral calibration based on deep learning

The miniature fiber optic spectrometer is smaller, cheaper and has a wide range of applications. However, the measurement error is larger. In order to solve this problem, the adaptive iterative optimization method for spectral calibration is proposed. In this study, a trinity neural network model is built based on spectral wavelength segmentation to improve the calibration degree. Based on the ‘pseudo-label’, a self-optimization method for spectral calibration is proposed to reduce the amount of data required. This study optimizes the measurement accuracy without changing the structure of the spectrometer. And the self-optimization of calibration model in practical application is realized. After experiment, the calibration degree of the calibration model can reach 75.72%. After a self-optimization, it can be increased to 87.45%. The calibration time of 401 spectral values (380 nm–780 nm) is less than 0.01 s. The results show that the operator can use this method to calibrate spectral data without having optical knowledge. This method has low cost, high calibration speed, good reliability and application value.

A fully adaptive method for variational inequalities with quasi-monotonicity

This paper presents a fully adaptive iterative method without linesearch procedure to solve variational inequalities in the setting of quasi-monotone which is locally Lipschitz. Our method involves one functional evaluation alongside one projection per iteration and our stepsizes are not restricted to be nonincreasing as opposed to relevant methods without linesearch procedure but with nonincreasing stepsizes. We give weak and strong convergence analysis of the sequences generated by our proposed method under some mild conditions. We also give R-linear rate convergence under an error bound condition and give some numerical implementations of our method. Numerical results show that our proposed method is competitive with other related methods in the literature.

Fast and accurate adaptive collocation iteration method for orbit dynamic problems

For over half a century, numerical integration methods based on finite difference, such as the Runge-Kutta method and the Euler method, have been popular and widely used for solving orbit dynamic problems. In general, a small integration step size is always required to suppress the increase of the accumulated computation error, which leads to a relatively slow computation speed. Recently, a collocation iteration method, approximating the solutions of orbit dynamic problems iteratively, has been developed. This method achieves high computation accuracy with extremely large step size. Although efficient, the collocation iteration method suffers from two limitations: (A) the computational error limit of the approximate solution is not clear; (B) extensive trials and errors are always required in tuning parameters. To overcome these problems, the influence mechanism of how the dynamic problems and parameters affect the error limit of the collocation iteration method is explored. On this basis, a parameter adjustment method known as the “polishing method” is proposed to improve the computation speed. The method proposed is demonstrated in three typical orbit dynamic problems in aerospace engineering: a low Earth orbit propagation problem, a Molniya orbit propagation problem, and a geostationary orbit propagation problem. Numerical simulations show that the proposed polishing method is faster and more accurate than the finite-difference-based method and the most advanced collocation iteration method.

Mixed thermo-elasto-hydrodynamic lubrication and wear coupling simulation analysis for dynamical load journal bearings

In the crankshaft-connecting rod system of the diesel engine, the complicated lubrication and wear mechanisms of journal bearings are comprehensively affected by the elastic deformation effect, the asperity contact effect, the thermal effect as well as the lubricating oil viscosity-temperature effect. This paper proposes an adaptive iterative method based on a mixed thermo-elasto-hydrodynamic (TEHD) lubrication and wear coupling model to investigate the lubrication and wear behaviors of journal bearings in the mixed lubrication. The evolution laws of the oil film thickness, the hydrodynamic pressure, the asperity contact pressure, the friction power loss, and the wear distribution are discussed and compared under different running times, oil temperatures, external loads, and shaft rotational speeds. The three-dimensional surface models based on the asperity contact power loss and the maximum wear depth with the variation of the running time and the oil temperature are fitted under specific external loads and shaft rotational speeds. The simulation results demonstrate that the bearing wear process includes the initial wear stage and the stable wear stage, which has a significant influence on the changing trend and distribution of lubricating parameters. The three-dimensional surface fitting models can provide theoretical guidance for the future performance prediction of journal bearings under time-varying working conditions.

Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables

The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was analyzed. Based on the real optimization theory and the mapping relationship between real vector space and complex vector space, the complex Broyden optimization iterative algorithm was proposed, and its stability and convergence was analyzed. To improve the stability and accelerate the convergence of the proposed algorithm, the complex steepest descent algorithm was proposed to cooperate with the complex Broyden optimization algorithm, which can adaptively optimize the complex steepest gradient iterative gain and update the system impedance in real time during the control process. The shaking tables experiment system was designed, applying xPC target rapid prototype control technology, and a series of experimental tests were performed. The results indicated that the proposed control method can quickly and stably converge to the optimal solution no matter whether the system identification error is small or large, and, thus, verified that validity and feasibility of the proposed adaptive iterative learning method.

Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration

This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.

Chattering-free adaptive iterative learning for attitude tracking control of uncertain spacecraft

This paper aims at developing chattering-free adaptive iterative learning for repetitive attitude tracking tasks of spacecraft subject to initial state errors. Thanks to the proposed reduction mechanism in the parametric learning law, iteration-varying initial state errors, as well as the unknown inertia matrix and external disturbances, can be addressed effectively. Moreover, to avoid the chattering phenomenon of the control signals, we introduce an approximation of the sign function. Of note is that this approximation leads to a class of non-negative definite problems. A new analytical method is consequently exploited with a Lyapunov-like theory based on contraction-mapping and composite-energy-function, which rigorously shows the boundedness and convergence of the iterative learning process in the presence of initial state errors and non-negative definite problems. These observations are validated by implementing the proposed chattering-free adaptive iterative learning method to achieve the specific attitude tracking task of an uncertain spacecraft.

An Adaptive Local Iterative Method for Fast Calculation of Electrostatic Interactions between Charged Polymers in Dielectric Inhomogeneous System

AbstractThe motion of charged polymers in the 3D dielectric inhomogeneity system is investigated using a hybrid simulation approach that combines a finite difference method with a Brownian dynamics model (FD‐BD) for the polymer chain. The interface difference scheme of the two‐dielectric model is first derived to achieve a smooth extension of the electrostatic potential values and it is verified that it has a second‐order convergence order. In addition, it is displayed that the local iteration method greatly accelerates the efficiency of calculating the electrostatic interaction forces. However, it is found that the computational efficiency improvement of the traditional local iteration is less significant (about three times) for polymer simulations such as 3D diagonal type. Therefore, a new strategy, the banded local iteration method, is proposed and the simulation results demonstrate that it can better adapt the effects of the deformation of the polymer during its motion, where the computational efficiency is increased by 29 times at most, while the relative error is controlled to less than 5.15e−4. The work can be helpful for accelerating the solution of electrostatic forces under dielectric inhomogeneity, and confer new insights into the optimization of iteration for polymer chains with different geometries in large electrostatic systems.

An Adaptive Nonlinear Iterative Method for Predicting Seafloor Topography From Altimetry‐Derived Gravity Data

AbstractThe ocean covers 71% of the Earth's surface. At present, only about 20% of the seafloor topography (ST) has been directly measured by ships, and most areas are predicted from satellite altimetry‐derived gravity products. In this study, an adaptive nonlinear iterative (ANI) method is proposed to address two major problems in gravity ST inversion: linear approximation and empirical seafloor density contrast (SDC). In ANI, the SDC is adaptively estimated as an output, while higher‐order Parker expansion and modified Bott's iteration are combined to recover nonlinear topography. We apply our new method using the DTU21GRA altimetric gravity model and single‐beam bathymetry to predict the ST in a part of the South China Sea. Results reveal that the average SDC in the study area is 1.24 g/cm3, which compares well to CRUST1.0. The root‐mean‐square (RMS) error between the nonlinear model and single‐beam checkpoints is 102.1 m, which is improved by 34.5%, 29.2%, and 18.3% compared with the non‐gravity model, topo_24.1, and linear model, respectively. The RMS error between the nonlinear model and multibeam bathymetry is 91.0 m, which is better than the linear model. Analysis of two‐dimensional profiles shows that the nonlinear model reveals more terrain details than the linear model.

Enhanced fractional adaptive processing paradigm for power signal estimation

Fractional calculus tools have been exploited to effectively model variety of engineering, physics and applied sciences problems. The concept of fractional derivative has been incorporated in the optimization process of least mean square (LMS) iterative adaptive method. This study exploits the recently introduced enhanced fractional derivative based LMS (EFDLMS) for parameter estimation of power signal formed by the combination of different sinusoids. The EFDLMS addresses the issue of fractional extreme points and provides faster convergence speed. The performance of EFDLMS is evaluated in detail by taking different levels of noise in the composite sinusoidal signal as well as considering various fractional orders in the EFDLMS. Simulation results reveal that the EDFLMS is faster in convergence speed than the conventional LMS (i.e., EFDLMS for unity fractional order).

Direction Finding for Passive Bistatic Radar in the Presence of Multipath Propagation

In the case of multipath propagation for passive bistatic radar (PBR) using uncooperative frequency agile-phased array radar as an illuminator, a new direction-finding method is proposed to deal with the scenario where the coherent and uncorrelated signals are closely spaced or in the same direction. Firstly, spatial difference technique is used to eliminate uncorrelated signals. Then, in order to avoid the cross-terms effect and improve the resolution of coherent signal, the iterative adaptive method (IAA) is adopted for the rearranged spatial difference matrix. Finally, the direction of arrival (DOA) of the target signal is obtained by the reconstruction of the interference-plus-noise covariance matrix. Compared with previous studies, this method has better performance in the case of low signal-to-noise ratio (SNR) and limited number of snapshots.

Adaptive Iterative Learning Control-Based Rotor Position Harmonic Error Suppression Method for Sensorless PMSM Drives

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection-based schemes, an adaptive iterative learning control-based online suppression method is proposed in this article. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform.

Speech Signal Analysis With a Refined Iterative Adaptive Method

A new speech signal analysis method referred to as a refined iterative adaptive method (RIAM) is introduced in this paper. Based on time-varying adaptive sinusoidal modeling, the RIAM method tries to determine in an iterative adaptive manner the instantaneous components of time-varying quasi-periodic multi-component signals such as voiced speech. The proposed method can adjust the current analysis parameters to the time-varying characteristics of the speech signal. This is done using a refined iterative sinusoidal parameter estimation algorithm based on a frequency correction mechanism combined with an adaptive scheme. The experiments on voiced speech demonstrate that the proposed RIAM algorithm outperforms some well-known state-of-the-art approaches. The RIAM algorithm provides a higher signal to reconstruction error ratio (SRER) of 42.267 dB with an improvement of 19.752 dB, 5.054 dB, and 2.552 dB compared to the conventional sinusoidal model (SM), adaptive harmonic model (aHM), and extended adaptive quasi harmonic model (eaQHM), respectively.

Design of Adaptive Iterative Reconstruction Method for Music Curriculum Integration and Reconstruction

Today, when music education generally attaches great importance to the construction of the subject curriculum, it is of practical significance to increase the research and implementation of curriculum integration and reconstruction. Curriculum integration and reconstruction not only provide a scientific concept guide for the formation of a good educational joint force in music education but also play an important role in the educational reform that focuses on human harmony and subsequent development. For the integration and reconstruction of music courses, this paper evaluates the teaching quality after integration and reconstruction through the neural network and then iterates the method of integration and reconstruction, so as to achieve the optimal effect. This paper introduces the development status of curriculum integration and reconstruction at home and abroad and provides a theoretical basis for the construction of the corresponding index system in the following sections. The related principles of Convolutional Neural Network (CNN) and Generalized Regression Neural Network (GRNN) are introduced, and an education quality evaluation system for music courses is constructed. We constructed a custom data set and introduced the design ideas and the specific parameter settings of the CNN-GRNN model. In addition, the CNN-GRNN model and the CNN-BP (CNN backpropagation) model are compared and analyzed in terms of the quality assessment accuracy of music courses. The results show that the CNN-GRNN model proposed in this paper outperforms the other methods. The mean squared error (MSE) of the model using one-dimensional convolution is lower than that of the CNN-GRNN model using two-dimensional convolution. As compared to CNN-BP model, the CNN-GRNN model outputs results that are closer to the expert evaluation results and the error is small.

Adaptive Iterative Method to Improve the Robustness of PID Regulators

A new approach is proposed to improve the robustness of simple PID controls. This scheme widens the critical medium frequency domain and increases the achievable bandwidth using adaptive-iterative method. Recently not only the optimality but the robustness is also important for the industry.