Adaptive Boundary Research Articles

A DSP-Based EIT System With Adaptive Boundary Voltage Acquisition

Electrical Impedance Tomography (EIT), a valuable technique modality for estimating electrical properties of an object by electrode measurements. During the measurement process, high-frequency excitation signal and the large dynamic range of boundary voltage both cause negative effects on system detection accuracy. Focusing on the induced signal detection, we proposed an adaptive boundary voltage acquisition method, which thoroughly modeled the EIT system by LTSPICE and MATLAB, dynamically adjusted the boundary voltage amplitude gain according to the induced signal voltage range, and changed sampling period for high frequency component of excitation signal. A DSP-based EIT hardware experimental platform under development was constructed, including a current source integrated direct digital synthesis and data acquisition circuit. The data acquisition circuit composed of programmable differential amplifier, low-pass filter and offset adjustment circuit is used for signal acquisition. Comparative experiments are systematically implemented for evaluating the proposed method. Quantitatively, the SNR difference of each measurement channel is reduced from 26.42dB to 6.33dB. The maximum SNR of the system can reach 60.67dB. The maximum image error (IE) is reduced from 28.17% to 15.60%. Accordingly, the proposed adaptive boundary voltage acquisition method is proved to be great beneficial to improve EIT system accuracy.

PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model

Abstract. During the Late Pleistocene and Holocene retreat of paleo-ice sheets in North America and Europe, vast proglacial lakes existed along the land terminating margins. These proglacial lakes impacted ice sheet dynamics by imposing boundary conditions analogous to a marine terminating margin. Such lacustrine boundary conditions cause changes in the ice sheet geometry, stress balance and frontal ablation and therefore affect the mass balance of the entire ice sheet. Despite this, dynamically evolving proglacial lakes have rarely been considered in detail in ice sheet modeling endeavors. In this study, we describe the implementation of an adaptive lake boundary in the Parallel Ice Sheet Model (PISM), which we call PISM-LakeCC. We test our model with a simplified glacial retreat setup of the Laurentide Ice Sheet (LIS). By comparing the experiments with lakes to control runs with no lakes, we show that the presence of proglacial lakes locally enhances the ice flow, which leads to a lowering of the ice sheet surface. In some cases, this also results in an advance of the ice margin and the emergence of ice lobes. In the warming climate, increased melting on the lowered ice surface drives the glacial retreat. For the LIS, the presence of lakes triggers a process similar to marine ice sheet instability, which caused the collapse of the ice saddle over Hudson Bay. In the control experiments without lakes, Hudson Bay is still glaciated when the climate reaches present-day (PD) conditions. The results of our study demonstrate that glacio-lacustrine interactions play a significant role in the retreat of land terminating ice sheet margins.

A New Approach to Space-Time Boundary Integral Equations for the Wave Equation

We present a new approach for boundary integral equations for the wave equation with zero initial conditions. Unlike previous attempts, our mathematical formulation allows us to prove that the associated boundary integral operators are continuous and satisfy inf-sup conditions in trace spaces of the same regularity, which are closely related to standard energy spaces with the expected regularity in space and time. This feature is crucial from a numerical perspective, as it provides the foundations to derive sharper error estimates and paves the way to devise efficient adaptive space-time boundary element methods, which will be tackled in future work. On the other hand, the proposed approach is compatible with current time dependent boundary element method's implementations and we predict that it explains many of the behaviours observed in practice but that were not understood with the existing theory.

An implicit method for slow-moving free-surface boundary of open channel flows

This paper outlines the implementation of an efficient implicit method for a free surface boundary for solving free surface flows with larger time steps. In this method, the governing equations are solved with dynamic conditions applied to the free surface boundary. The position and shape of the free surface are represented with adaptive boundary grid points. Unlike existing explicit methods, the proposed method solves the space conservation equation implicitly, making the calculations more stable and at a lower computational cost. The approach was implemented numerically by developing a new boundary field in OpenFOAM. Several test cases, including propagations of tidal wave, channel surge and solitary wave, were simulated to demonstrate the method’s accuracy. The proposed method accurately predicted the first two cases using larger time steps. The Courant number of the solitary wave case should be less than 1 to reduce numerical errors, which would damp the wave amplitude.

A boundary division guiding synchrosqueezed wave packet transform method for rolling bearing fault diagnosis

Synchrosqueezed wave packet transform (SSWPT) can effectively reconstruct the band-limited components of the signal by inputting the specific reconstructed boundaries, and it provides an alternative bearing fault diagnosis method. However, the selection of reconstructed boundaries can significantly affect the fault feature extraction performance of SSWPT. Accordingly, this paper presents a boundary division guiding SSWPT (BD-SSWPT) method. In this method, an adaptive boundary division method is developed to effectively determine the reconstructed boundaries of SSWPT. Firstly, the marginal spectrum of SSWPT, more robust to noise than the Fourier spectrum, is defined for the scale-space division to obtain the initial boundaries. Secondly, the inverse transform of SSWPT is conducted based on the initial boundaries to obtain the initial reconstructed components. Thirdly, a boundary redefinition scheme, composed of clustering and combination, is conducted to redefine the boundaries. Finally, the potential components are extracted by the inverse transform of SSWPT based on the redefined boundaries. The validity of BD-SSWPT is verified by simulated and experimental analysis, and the superiority of BD-SSWPT is highlighted through comparison with singular spectrum decomposition (SSD) and an adaptive parameter optimized variational mode decomposition (AVMD). The results demonstrate that BD-SSWPT identifies more significant fault features and has higher computational efficiency than SSD and AVMD.

Droplet evaporation in finite-size systems: Theoretical analysis and mesoscopic modeling.

The classical D^{2}-Law states that the square of the droplet diameter decreases linearly with time during its evaporation process, i.e., D^{2}(t)=D_{0}^{2}-Kt, where D_{0} is the droplet initial diameter and K is the evaporation constant. Though the law has been widely verified by experiments, considerable deviations are observed in many cases. In this work, a revised theoretical analysis of the single droplet evaporation in finite-size open systems is presented for both two-dimensional (2D) and 3D cases. Our analysis shows that the classical D^{2}-Law is only applicable for 3D large systems (L≫D_{0}, L is the system size), while significant deviations occur for small (L≤5D_{0}) and/or 2D systems. Theoretical solution for the temperature field is also derived. Moreover, we discuss in detail the proper numerical implementation of droplet evaporation in finite-size open systems by the mesoscopic lattice Boltzmann method (LBM). Taking into consideration shrinkage effects and an adaptive pressure boundary condition, droplet evaporation in finite-size 2D/3D systems with density ratio up to 328 within a wide parameter range (K=[0.003,0.18] in lattice units) is simulated, and remarkable agreement with the theoretical solution is achieved, in contrast to previous simulations. The present work provides insights into realistic droplet evaporation phenomena and their numerical modeling using diffuse-interface methods.

Adaptive Boundary Control for the Dynamics of the Generalized Burgers-Huxley Equation

This paper deals with the boundary control problem of the unforced generalized Burgers-Huxley equation with high order nonlinearity when the spatial domain is [0, 1]. We show that this type of equations are globally exponential stable in L2 [0, 1] under zero Dirichlet boundary conditions. We use an adaptive nonlinear boundary controller to show the convergence of the solution to the trivial solution and to show that it achieves global asymptotic stability in time. We introduce numerical simulation for the controlled equation using the Adomian decomposition method (ADM) in order to illustrate the performance of the controller.

Adaptive Fuzzy Control for a Spatial Flexible Hose System with Dynamic Event-triggered Mechanism

In this study, we develop an adaptive boundary control strategy with a dynamic event-triggered mechanism (dETM) for an aerial refueling hose system involving certain performance specifications. The controller uses the barrier Lyapunov function method, where the introduction of two boundary deflection transformations, an asymmetric scaling function and a behavior-shaping function, reduces the complexity of the stability analysis and guarantees that the system meets the required performance description. Fuzzy logic systems are developed to strengthen the adaptivity in other unmodeled dynamics. Particularly in this work, the dETM is constructed to decrease the unnecessary signal transmission between the controllers and actuators while achieving vibration suppression. The stability of the controlled systems is proved via Lyapunov analysis. Finally, we establish numerical simulations to illustrate the validity of the designed controllers.

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

Accurate regional wind power prediction plays an important role in the security and reliability of power systems. For the performance improvement of very short-term prediction intervals (PIs), a novel probabilistic prediction method based on composite conditional nonlinear quantile regression (CCNQR) is proposed. First, the hierarchical clustering method based on weighted multivariate time series motifs (WMTSM) is studied to consider the static difference, dynamic difference, and meteorological difference of wind power time series. Then, the correlations are used as sample weights for the conditional linear programming (CLP) of CCNQR. To optimize the performance of PIs, a composite evaluation including the accuracy of PI coverage probability (PICP), the average width (AW), and the offsets of points outside PIs (OPOPI) is used to quantify the appropriate upper and lower bounds. Moreover, the adaptive boundary quantiles (ABQs) are quantified for the optimal performance of PIs. Finally, based on the real wind farm data, the superiority of the proposed method is verified by adequate comparisons with the conventional methods.

Three-way multi-granularity learning towards open topic classification

Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incrementally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular computing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of proposed models and the efficiency of knowledge accumulation with classic models.

Traffic flow modeling and feedback control for future Low-Altitude Air city Transport: An MFD-based approach

The imminent penetration of low-altitude passenger and delivery aircraft into the urban airspace will give rise to new urban air transport systems, which we call low-altitude air city transport (LAAT) systems. As the urban mobility revolution approaches, we must investigate (i) the collective behavior of LAAT aircraft in cities, and (ii) ways of controlling LAAT systems. Future LAAT systems exemplify a new class of modern large scale engineering systems — networked control systems. They are spatially distributed, consist of many interconnected elements with control loops through digital communication networks such that the system signals can be exchanged among all components through a common network. Therefore, a decentralized controller design in framework of the unilateral event-driven paradigm is considered. Inspired by controlled urban road networks, in this paper we first establish the concept of Macroscopic Fundamental Diagram (MFD) for LAAT systems and develop a collective and aggregate aircraft traffic flow model. Then, based on that, we design an adaptive boundary feedback flow control which is robust to various anomalies in technical devices and network communication links for LAAT systems.

Distributed fault‐tolerant time‐varying formation control of heterogeneous multi‐agent systems

AbstractThis article investigates the cooperative time‐varying formation control for heterogeneous multi‐agent systems (HMASs) with unknown actuator faults and external disturbances. The HMASs consist of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In order to overcome the difficulty of coordinated control caused by the different structures of UAVs and UGVs, the comprehensive dynamics models of HMASs are derived, of which the model uncertainties as well as actuator faults and external disturbances of the models are considered. Subsequently, combining the radial basis function neural networks (RBFNNs) with adaptive technology and boundary layer theory, a distributed fault‐tolerant time‐varying formation control method is designed. The proposed control method is totally distributed. Finally, the effectiveness of the controller is verified by several simulations.

Понижение порядка сложных моделей с помощью инструментов Robust Control Toolbox

The city of Berezniki, Perm region is located on an underworked mine area. For several years, the city has been experiencing active subsidence of the soil, which provoke the destruction of buil-dings. Therefore, for several years now, the city's buildings and structures have been monitored, which makes it possible to analyze the degree of subsidence. Models of a sufficiently high order are used for an accurate analysis of the situation and forecasting. The article is about a possibility of modeling the deformation of buildings associated with soil subsidence, as a result of mine workings in the city of Berezniki. The purpose of the study is to consider the capabilities of the ‘Robust Control Toolbox’ for reducing the order of complexity of models. An example of an eight-story building included in the collection of reference examples for reducing models of linear dynamic systems is used. Materials and methods. Typical steps for solving the problem of model reduction are presented, commands and tools used to solve this problem are described. The parameters of the model in the state space are determined, which has 48 states, which are displacements or rates of change. The singular values of Hankel are used to select states that can be neglected. The model is reduced using an adaptive error boundary. Reduction using the multiplicative error bound is considered. Comparison of the results of reduction of the model by all described methods is carried out, the choice of the best method of reduction of the model is substantiated. Results. An analysis of the approximation error was performed for all the methods. The maximum relative error has been calculated. An example of calculating the order of the model for a given error value of 5% is given. The order of the result model is 34 states with the error less is then 1%, which is less than the original model. As a result, the AFCs of the original and reduced models, as well as the transient processes of the models, were constructed. The plots in the frequency domain of the models practically coincide, which indicates an adequate description of the system. Conclusions. As a result, it was shown that it is possible to reduce the size of the model by 14 orders of magnitude, goal achieved.

Computational Fluid Dynamics Using the Adaptive Wavelet-Collocation Method

Advancements to the adaptive wavelet-collocation method over the last decade have opened up a number of new possible areas for active research. Volume penalization techniques allow complex immersed boundary conditions to be used with high efficiency for both internal and external flows. Anisotropic methods make it possible to use body-fitted meshes while still taking advantage of the dynamic adaptability properties wavelet-based methods provide. The parallelization of the approach has made it possible to perform large high-resolution simulations of detonation initiation and fluid instabilities to uncover new physical insights that would otherwise be difficult to discover. Other developments include space-time adaptive methods and nonreflecting boundary conditions. This article summarizes the work performed using the adaptive wavelet-collocation method developed by Vasilyev and coworkers over the past decade.

Constraining the chronology and ecology of Late Acheulean and Middle Palaeolithic occupations at the margins of the monsoon

South Asia hosts the world’s youngest Acheulean sites, with dated records typically restricted to sub-humid landscapes. The Thar Desert marks a major adaptive boundary between monsoonal Asia to the east and the Saharo-Arabian desert belt to the west, making it a key threshold to examine patterns of hominin ecological adaptation and its impacts on patterns of behaviour, demography and dispersal. Here, we investigate Palaeolithic occupations at the western margin of the South Asian monsoon at Singi Talav, undertaking new chronometric, sedimentological and palaeoecological studies of Acheulean and Middle Palaeolithic occupation horizons. We constrain occupations of the site between 248 and 65 thousand years ago. This presents the first direct palaeoecological evidence for landscapes occupied by South Asian Acheulean-producing populations, most notably in the main occupation horizon dating to 177 thousand years ago. Our results illustrate the potential role of the Thar Desert as an ecological, and demographic, frontier to Palaeolithic populations.

Global fuzzy adaptive asymptotic tracking control for nonlinear reaction-diffusion equations with time-varying coefficients

This study focuses on the research of the globally asymptotic tracking problem of unknown nonlinear reaction-diffusion equations with time-varying coefficients and uncertain external disturbance. Firstly, fuzzy logic systems and adaptive bounding technique are used to deal with nonlinear reaction-diffusion equations with time-varying coefficients and uncertain external disturbance. Secondly, a novel global state feedback adaptive fuzzy control algorithm is proposed to make the nonlinear reaction-diffusion equations track the target systems globally and asymptotically. In addition, the globally asymptotic tracking condition can be obtained, which overcomes the semi-global results in the existing literatures. Finally, three simulation examples are given to illustrate the feasibility and effectiveness of the proposed control protocols.

Adaptive boundary control of reaction–diffusion PDEs with unknown input delay

We design an adaptive full-state feedback controller to stabilize a one-dimensional reaction–diffusion equation with unknown boundary input delay. An infinite-dimensional representation of the actuator delay is utilized to transform the system into a transport PDE cascading with a reaction–diffusion PDE. A suitable parameter update law is designed to establish local boundedness of the system trajectories and asymptotic convergence stability result using the well-known PDE backstepping technique and a Lyapunov argument. Consistent simulation results are provided to support the theoretical results.

Adaptive boundary control of a flexible-link flexible-joint manipulator under uncertainties and unknown disturbances

In this article, we consider the trajectory tracking and vibration suppression of a flexible-link flexible-joint manipulator under uncertainties and external time-varying unknown disturbances. The coupled ordinary differential equation and partial differential equation model dynamic of the system is presented by employing the Hamilton principle. Using the singular perturbation theory, the dynamic is decomposed into a no-underactuated slow ordinary differential equation and fast partial differential equation subsystem, which solves the problem of the underactuated ordinary differential equation subsystem of the ordinary differential equation and partial differential equation cascade and reduces the analytical complexity. For the slow subsystem, to guarantee the trajectory tracking of the joint, an adaptive global sliding mode controller without gain overestimation is designed, which can guarantee the global stability of the slow system and reduce the chattering of the sliding mode control. For the fast subsystem, an adaptive boundary controller is developed to suppress the elastic vibration of the flexible link during the trajectory tracking. The stability of the whole closed-loop system is rigorously proved via the Lyapunov analysis method. Simulation results show the effectiveness of the proposed controller.

Adaptive boundary sine cosine optimizer with population reduction for robustness analysis of finite time horizon systems

This paper proposes an adaptive boundary sine cosine optimizer with population reduction. It is designed specifically to calculate an upper bound on the worst-case gain of a known finite horizon Linear Time Varying (LTV) system and a perturbation. The input/output behavior of the perturbation is described by a time domain Integral Quadratic Constraint (IQC). The analysis condition is formulated as a parametric Riccati differential equation which depends on the IQC representation. A nonlinear optimization problem is posed that minimizes the upper bound on the worst-case gain over the IQC parameterization. For industrial size applications like a space launcher, the number of decision variables can grow arbitrarily large depending on the number of considered perturbations as well as the type and representation of the IQC. This is aggravated by nonlinear constraints on the decision variables imposed by the Riccati differential equation making it challenging to solve. Several established Meta-Heuristics (MHs) along with the proposed algorithm are applied to an industry size worst case analysis of a space launcher during its atmospheric ascend. Their respective performances are evaluated to emphasize the advantages of the developed optimizer. This work builds the foundation of applying MHs to IQC based robustness analysis of finite horizon LTV systems.

Adaptive Boundary Control for a Certain Class of Reaction–Advection–Diffusion System

Several phenomena in nature are subjected to the interaction of various physical parameters, which, if these latter are well known, allow us to predict the behavior of such phenomena. In most cases, these physical parameters are not exactly known, or even more these are unknown, so identification techniques could be employed in order to estimate their values. Systems for which their inputs and outputs vary both temporally and spatially are the so-called distributed parameter systems (DPSs) modeled through partial differential equations (PDEs). The way in which their parameters evolve with respect to time may not always be known and may also induce undesired behavior of the dynamics of the system. To reverse the above, the well-known adaptive boundary control technique can be used to estimate the unknown parameters assuring a stable behavior of the dynamics of the system. In this work, we focus our attention on the design of an adaptive boundary control for a parabolic type reaction–advection–diffusion PDE under the assumption of unknown parameters for both advection and reaction terms and Robin and Neumann boundary conditions. An identifier PDE system is established and parameter update laws are designed following the certainty equivalence approach with a passive identifier. The performance of the adaptive Neumann stabilizing controller is validated via numerical simulation.