Adaptive Iterative Method Research Articles

An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack.

An Adaptive Sliding-Mode Iterative Constant-force Control Method for Robotic Belt Grinding Based on a One-Dimensional Force Sensor.

To improve the processing quality and efficiency of robotic belt grinding, an adaptive sliding-mode iterative constant-force control method for a 6-DOF robotic belt grinding platform is proposed based on a one-dimension force sensor. In the investigation, first, the relationship between the normal and the tangential forces of the grinding contact force is revealed, and a simplified grinding force mapping relationship is presented for the application to one-dimension force sensors. Next, the relationship between the deformation and the grinding depth during the grinding is discussed, and a deformation-based dynamic model describing robotic belt grinding is established. Then, aiming at an application scene of robot belt grinding, an adaptive iterative learning method is put forward, which is combined with sliding mode control to overcome the uncertainty of the grinding force and improve the stability of the control system. Finally, some experiments were carried out and the results show that, after ten times iterations, the grinding force fluctuation becomes less than 2N, the mean value, standard deviation and variance of the grinding force error’s absolute value all significantly decrease, and that the surface quality of the machined parts significantly improves. All these demonstrate that the proposed force control method is effective and that the proposed algorithm is fast in convergence and strong in adaptability.

An irredundant G01 tool path generation method for five-axis machining considering tool tip and orientation errors

Five-axis machine tool is widely used in freeform surface machining. The freeform surface’s tool path is usually discretized as G01 segments and the freeform surface machined with linear interpolation in five-axis machine. Due to the two additional rotary axes, there are the nonlinear coupling relationship between the workpiece coordinate system (WCS) and machine coordinate system (MCS). Hence, there are many uncertain errors produced by the nonlinear coupling relationship, especially via linear interpolation. In order to represent the motion trajectory accurately and generate G01 tool path appropriately, this paper proposes an irredundant G01 tool path generation method for five-axis machine. Firstly, a new tool tip error (TTE) definition is given to reflect the influence of the rotation axis on the tool tip. And then, a new tool orientation error (TOE) definition is given to mirror the tool orientation error caused by rotary axis’ linear interpolation. Finally, an adaptive iterative method (AIM) is proposed to obtain irredundant G01 tool path. By using this method, the G01 tool path can be generated based on the given error limits and the proposed method is verified both in the simulation and experiment. The results show that the proposed method can significantly reduce the number of G01 segments and limit the actual error.

Fault-Tolerant Control of Double-Rope Winding Hoists Combining Neural-Based Adaptive Technique and Iterative Learning Method

Double-rope winding mine hoist (DRWMH) is a vital coal mining equipment whose performance directly impacts the safe and efficient exploitation of the deep earth resources. Unfortunately, the actuator faults and disturbances can greatly affect the effect of the DRWMH. This paper deals with the problem of tension coordination control against actuator faults and disturbances for a DRWMH driven by multiple permanent magnet synchronous motors (PMSMs) using the neural-based adaptive method and iterative learning scheme. To improve the control performance and to maintain the stability of the DRWMH under faulty conditions, in this approach, a novel neural-based adaptive control strategy is developed to implement the input control assignment of the actuators. An iterative learning controller is used to achieve the compensation of the faults and uncertainties. The stability of both control subsystems has been proven. A series of experimental results are illustrated to demonstrate the validity of the proposed fault tolerant control for the DRWMH, which also reveal that a superior control effect is achieved in contrast with traditional controllers. Furthermore, the strategy proposed in this paper can be applied to other mechanical systems driven by multiple PMSMs that require high safety.

Model Free Adaptive Iterative Learning Control for Tool Feed System in Noncircular Turning

The linear motor tool feed system is an important part in noncircular turning. In this paper, the compact form dynamic linearization based model-free adaptive iterative control scheme (CFDL-MFAILC) and the full form dynamic linearization based model-free adaptive iterative control method scheme (FFDL-MFAILC) are designed for a complex nonlinear tool feed system. Theoretical analysis shows that the proposed scheme guarantees the output tracking error monotonic convergence along the iteration axis, and the FFDL-MFAILC is a complement and improvement to the CFDL-MFAILC. The designed control schemes are compared with PID and iterative learning feedforward and model-free adaptive predictive control feedback combination scheme (ILC-MFAPC) by simulations and experiments. Simulation results show that the proposed scheme can greatly decrease linear motor position error as iteration time increase, and has better position control advantages then other algorithms, the FFDL-MFAILC has faster convergence speed and smaller steady-state error than the CFDL-MFAILC. Experiment results prove that the proposed scheme is effective in linear motor tool feed system position control.

A 2-D Predistortion Based on Profile Inversion for Fully Digital Cartesian Transmitter

A novel 2-D digital predistortion (DPD) algorithm for fully digital Cartesian transmitter is proposed. The nonlinearity of the digital power amplifier in the digital transmitter necessitates DPD for linearization. In order to shorten the setup time of predistortion coefficients in the lookup table (LUT), a profile inversion-based method is adopted. Compared to the traditional adaptive iterative method, the profile inversion method for DPD in this paper shortens the setup time to $15~\mu \text{s}$ , which is possible for Long Term Evolution (LTE) time division duplex digital transmitter system to execute calibration within every frame. The algorithm is experimentally verified in a fully digital transmitter implemented in 65-nm general process CMOS technology with LUT size of 0.19 mm2, chip size of 2.7 mm2. The digital transmitter has achieved −35- and −32-dBc adjacent channel leakage ratio with 10-MHz LTE and 20-MHz 802.11g WiFi baseband feed in signals, respectively. The DPD improves the linearity of the transmitter by 21 dB for a 2-D constellation signal.

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis.

The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed.

An adaptive chebyshev iterative method

An adaptive chebyshev iterative method

A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height.

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.

Analysis and optimization of beamforming methods for high frequency mobile broadband communication system

The explosive growth of wireless data services demands higher capacity of future wireless communication systems to meet this trend. One efficient solution to this problem is to look for more spectral resource. To fully exploit the high potential rates of high frequency in mobile networks, in this paper, we propose a novel beamforming method based on iterative adaptive method, pre-defined selection and compressed sensing, effectively reducing the pilot and feedback overhead of mobile broadband communication with high-frequency band, and suppressing inter-cell interference and extending system coverage.

Iterative denoising of ghost imaging based on adaptive threshold method

Ghost imaging (GI) is an important technique in the fields of quantum imaging and classical optical imaging, and it can solve the problems which are difficult to solve by the traditional imaging techniques in the optically harsh environments. In this paper, we present the iterative denoising of GI based on an adaptive threshold method. This method is abbreviated as IDGI-AT, which takes the advantages of adaptive threshold, differential, binarization and iterative operation method, and can enhance image quality in GI. In addition, this method can reduce the number of measurements. As is well known, the enormous number of measurements and poor reconstruction quality are obstacles to the engineering application of GI. The correlation noise leads to low signal-to-noise ratio and low imaging efficiency in GI as well. Therefore, we establish a denoising model, which can reduce correlation noise and improve reconstruction quality. We first analyze the iterative denoising of ghost imaging (IDGI) theory, and use the adaptive threshold technique to calculate the ideal threshold associated with the correlation noise. It should be noted that the threshold can be obtained by this method under the condition without requiring prior knowledge of the object. Afterwards, we can construct the correlation noise in this denoising model. In the IDGI, the differential ghost imaging (DGI) image is taken as the initial iteration value. We use the adaptive threshold method, which is different from IDGI, to binarize the initial value of each iteration to make it closer to the original object's transmission coefficient. After three iterations, we can obtain a higher-quality reconstruction image. In order to demonstrate that the IDGI-AT is available, a GI experimental system with a pseudo-thermal light source is set up. The considerable simulation and experimental results show the advantage of our scheme in terms of removing reconstruction image background noise. Especially, the visual effects and peak signal-to-noise ratio values are improved in comparison with those from the traditional GI, DGI and IDGI. Besides, we demonstrate the role of binarization in our scheme. For a binary object, the iterative value binarization can achieve better image quality than that in the case without binarizing the iterative initial value. Therefore, this novel method is likely to provide an alternative mean for GI and further pave the way for the application fields of GI, such as lidar, biomedical engineering, etc.

Adaptive re-parameterization based on arbitrary scalar fields for shape optimization and surface fitting

This paper presents a method for re-parameterization based on an arbitrary scalar field named the relaxation field. The relaxation field is applied to re-distribute the control-points of a parametric surface towards the desired areas. The proposed method was developed for possible application in an intelligent shape optimization procedure where a sensitivity field with respect to an objective function (or some other physical field) would be used for constructing the relaxation field. It could hence contribute to the concentrating the control-points at areas where significant changes in the geometry are expected. The method can easily be used in shape optimization since it keeps the number of variables constant during the redistribution of control-points as opposed to adaptive insertion of control points when using T-spline and similar methods.The same method can also be used in surface fitting by choosing the relaxation field based on the geometric error. This leads to an adaptive iterative fitting method. The method was validated by fitting a single patch B-spline surface to triangulated point clouds. The point-clouds were obtained by 3D scanning or from a CAD model. Examples include several complex engineering objects. The proposed method uses a parameterization method based on a combination of harmonic mapping and a mapping method based on a spring mesh. By relaxation using a spring mesh, the method allocates more parametric space to the regions of interest, thus assigning them more control points. The combination of these two mapping methods provides for increased local control while keeping the global smoothness of the parameterization.

Assessment of USM3D Hierarchical Adaptive Nonlinear Method Preconditioners for Three-Dimensional Cases

Enhancements to the previously reported hierarchical adaptive nonlinear iteration methods implemented in a mixed-element cell-centered framework have been made to improve robustness, efficiency, and accuracy of computational-fluid-dynamic simulations. The key enhancements include a multicolor line-implicit preconditioner, a discretely consistent symmetry boundary condition, and a line-mapping method for the turbulence source term discretization. The Reynolds-averaged Navier–Stokes solutions have been computed using a Spalart–Allmaras turbulence model and families of uniformly refined nested grids on two three-dimensional configurations, namely, a bump in a channel and a hemisphere cylinder. Iterative convergence of two hierarchical adaptive nonlinear iteration method approaches using line- and point-implicit preconditioners has been compared with the iterative convergence of the point-implicit preconditioner-alone method that broadly represents the baseline solver technology. The line-implicit hierarchical adaptive nonlinear iteration method shows superior iterative convergence with progressively increasing benefits on finer grids.

A Novel Complex-Coefficient In-Band Interference Suppression Algorithm for Cognitive Ultra-Wide Band Wireless Sensors Networks.

With the rapid development of wireless communication systems and electronic techniques, the limited frequency spectrum resources are shared with various wireless devices, leading to a crowded and challenging coexistence circumstance. Cognitive radio (CR) and ultra-wide band (UWB), as sophisticated wireless techniques, have been considered as significant solutions to solve the harmonious coexistence issues. UWB wireless sensors can share the spectrum with primary user (PU) systems without harmful interference. The in-band interference of UWB systems should be considered because such interference can severely affect the transmissions of UWB wireless systems. In order to solve the in-band interference issues for UWB wireless sensor networks (WSN), a novel in-band narrow band interferences (NBIs) elimination scheme is proposed in this paper. The proposed narrow band interferences suppression scheme is based on a novel complex-coefficient adaptive notch filter unit with a single constrained zero-pole pair. Moreover, in order to reduce the computation complexity of the proposed scheme, an adaptive complex-coefficient iterative method based on two-order Taylor series is designed. To cope with multiple narrow band interferences, a linear cascaded high order adaptive filter and a cyclic cascaded high order matrix adaptive filter (CCHOMAF) interference suppression algorithm based on the basic adaptive notch filter unit are also presented. The theoretical analysis and numerical simulation results indicate that the proposed CCHOMAF algorithm can achieve better performance in terms of average bit error rate for UWB WSNs. The proposed in-band NBIs elimination scheme can significantly improve the reception performance of low-cost and low-power UWB wireless systems.

Adaptive Mesh Iteration Method for Trajectory Optimization Based on Hermite-Pseudospectral Direct Transcription

An adaptive mesh iteration method based on Hermite-Pseudospectral is described for trajectory optimization. The method uses the Legendre-Gauss-Lobatto points as interpolation points; then the state equations are approximated by Hermite interpolating polynomials. The method allows for changes in both number of mesh points and the number of mesh intervals and produces significantly smaller mesh sizes with a higher accuracy tolerance solution. The derived relative error estimate is then used to trade the number of mesh points with the number of mesh intervals. The adaptive mesh iteration method is applied successfully to the examples of trajectory optimization of Maneuverable Reentry Research Vehicle, and the simulation experiment results show that the adaptive mesh iteration method has many advantages.

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

Several improvements to the mixed-element USM3D discretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier–Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

WBI Suppression for SAR Using Iterative Adaptive Method

Under the condition of wideband interference (WBI) with the characteristics of good time–frequency concentration but high nonstationarity, the limited time width of the window function in short-time Fourier transform (STFT) causes limited instantaneous frequency resolution and leads to great performance degradation of the conventional WBI suppression algorithm based on time–frequency filtering (TFF) method. A novel WBI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR). Dispensing with parametric search and model order estimation, the proposed method improves the instantaneous frequency resolution in STFT by means of the IAA method and filters the WBI based on the OSP method, meanwhile, obtains time–frequency distribution (TFD) with 2-D high resolution and no cross-terms. Both the simulation and experimental results are provided to illustrate the performance of the proposed method.

Adaptive frame-based color image denoising

In this paper we study image denoising to restore color images contaminated by additive white Gaussian noise. For a color noisy image, block matching groups similar image patches together to form fourth order tensors. Using higher order singular value decomposition for tensors, we construct adaptive frames and propose an iterative thresholding algorithm to suppress additive white Gaussian noise in corrupted color images. The proposed algorithm can be viewed as an adaptive frame-based iterative hard thresholding method. We provide convergence analysis of the Nash equilibrium process raised in our proposed algorithm and any patch-based image denoising methods. Our experimental results show that the overall performance of our proposed algorithm is comparable to or often better than two well-known state-of-the-art color image denoising algorithms.

An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data

Satellite-derived greenness vegetation indices provide a valuable data source for characterizing spring vegetation phenology over regional or global scales. A logistic function has been widely used to fit time series of vegetation indices to estimate green-up date (GUD), which is currently being used for generating the global phenological product from the Enhanced Vegetation Index (EVI) time-series data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we address a violation of the basic assumption of the logistic fitting method that arises from the fact that vegetation growth under natural conditions is controlled by multiple environmental factors and often does not follow a well-defined S-shaped logistic temporal profile. We developed the adaptive local iterative logistic fitting method (ALILF) to analyze the “local range” (i.e., the range of data points where the values in the time series begin to increase rapidly) in the MODIS EVI profile in which GUD is found. The new method adopts an iterative procedure and an adaptive temporal window to properly simulate the trajectory of EVI time series in the local range, and can determine GUD more accurately. GUD estimated by ALILF almost match the date of the onset of the greenness increase well while the traditional logistic fitting method shows errors of even more than 1 month in the same cases. ALILF is a more general form of the logistic fitting method that can estimate GUD both from well-defined S-shaped time series and from non-logistic ones. Besides, it is resistant to a range of noise levels added on the time-series data (Gaussian noise with a mean value of zero and standard deviations ranging from 0% to 15% of the EVI value). These advantages mean ALILF may be widely used for monitoring spring vegetation phenology from greenness vegetation indices.

Iterative adaptive dynamic programming approach to power optimal control for smart grid with energy storage devices

The smart grid is a new generation of power grids, and also the common choice of the international power industry. This paper aims to establish a new adaptive optimal control theory and a set of methods based on adaptive dynamic programming(ADP) for a smart grid with energy storage devices. The intelligent interactions between the power generation side and the clients can be finally achieved to create a new way of optimal matching of the supply and demand for the smart grid. First, the optimality principle of dynamic programming for the grid with energy storage devices is given, and optimization objective is then presented. Then a new iterative adaptive dynamic programming method is developed to achieve the optimal control of energy storage devices, and convergence of the adaptive dynamic programming method is also proved. Finally, a simulation example is given to show the effectiveness of the method.