Influence Of Measurement Noise Research Articles

Probing finite-temperature observables in quantum simulators of spin systems with short-time dynamics

Preparing finite-temperature states in quantum simulators of spin systems, such as trapped ions or Rydberg atoms in optical tweezers, is challenging due to their almost perfect isolation from the environment. Here, we show how finite-temperature observables can be obtained with an algorithm motivated from the Jarzynski equality and equivalent to the one in Lu et al., PRX Quantum 2, 020321 (2021). It consists of classical importance sampling of initial states and a measurement of the Loschmidt echo with a quantum simulator. We use the method as a quantum-inspired classical algorithm and simulate the protocol with matrix product states to analyze the requirements on a quantum simulator. This way, we show that a finite-temperature phase transition in the long-range transverse-field Ising model can be characterized in trapped ion quantum simulators. We propose a concrete measurement protocol for the Loschmidt echo and discuss the influence of measurement noise, dephasing, as well as state preparation and measurement errors. We argue that the algorithm is robust against those imperfections under realistic conditions.

State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method

State of health (SOH) and remaining useful life (RUL) prediction are crucial for battery management systems (BMS). However, accurate SOH and RUL prediction still need to be improved due to the complicated battery aging mechanism. This work combines incremental capacity analysis (ICA) and differential voltage analysis (DVA) based on the second-order RC model with an improved Bidirectional Gated Recurrent Unit (BiGRU) to develop SOH and RUL prediction framework. Firstly, the voltage is reconstructed through the second-order RC model to obtain the incremental capacity (IC) and differential voltage (DV) curves to avoid the influence of measurement noise and the complex parameter adjustment process in the filtering method on the IC and DV curves. Then, a new set of battery aging features are extracted from the reshaped IC and DV curves to improve SOH and RUL prediction accuracy and robustness. Next, the BiGRU method with attention mechanism (BiGRU-AM) is used to build the prediction models for battery aging features, SOH, and RUL. To reduce the impact of the capacity regeneration phenomenon, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is used to decompose the SOH prediction results, and the decomposed residual is used as the input to improve the prediction accuracy of RUL. The uncertainty of RUL prediction results is analyzed by Monte Carlo (MC) simulation. Finally, the proposed method is verified by experimental battery data from Center for Advanced Life Cycle Engineering (CALCE) and Sandia National Laboratory. Experimental results show that the voltage reconstruction results based on the second-order RC model are applied to ICA and DVA analysis, effectively avoiding the influence of noise. The RMSE of voltage reconstruction is within 0.0006, and the Pearson correlation coefficient between the four aging features extracted from the reconstructed IC/DV curve and SOH is above 0.9. Moreover, this method has good robustness to the cell inconsistency, temperature uncertainty, and a satisfied generalization ability to different battery chemistries, which the maximum RUL predicted AE of CALCE and Sandia battery is within 10 and 5, respectively.

Algorithm for Retrieval of Temperature in Mesosphere and Lower Thermosphere from O2 A-Band Night Glow

The O2 A-band night glow can be used to retrieve the temperature in the range of 80–120 km in the mesosphere and lower thermosphere (MLT). From the full spectrum of A-band night glow, the band used to retrieve temperature is selected based on the sensitivity of the emission line on temperature, taking into account the compromise relationship between the spectral band and resolution, inversion efficiency, and inversion accuracy. The non-linear iterative inversion method based on optimization theory is adopted for the retrieval of temperature. Meanwhile, considering the stability of inversion, Tikhonov regularization matrix is added as a constraint, and the optimal estimation inversion algorithm is optimized to suppress the influence of measurement noise on results. Through the recovering simulated noisy spectra from an interferometer, the temperature profile with vertical resolution better than 2 km and an average accuracy better than 2 K is obtained by the optimized inversion method in this paper. In addition, the influence of a priori constraints on the inversion accuracy is studied, and a priori accuracy limits the inversion accuracy. When a priori accuracy is controlled within ±5 K, the average temperature inversion accuracy can be optimized to 1.35 K, which is better than the accuracy of OSIRIS on Odin at 90–105 km.

Delamination and Skin-Spar Debond Detection in Composite Structures Using the Inverse Finite Element Method

This work presents a novel strategy for detecting and localizing intra- or inter-laminar damages in composite structures using surface-instrumented strain sensors. It is based on the real-time reconstruction of structural displacements using the inverse Finite Element Method (iFEM). The iFEM reconstructed displacements or strains are post-processed or 'smoothed' to establish a real-time healthy structural baseline. As damage diagnosis is based on comparing damaged and healthy data obtained using the iFEM, no prior data or information regarding the healthy state of the structure is required. The approach is applied numerically on two carbon fiber-reinforced epoxy composite structures: for delamination detection in a thin plate, and skin-spar debond detection in a wing box. The influence of measurement noise and sensor locations on damage detection is also investigated. The results demonstrate that the proposed approach is reliable and robust but requires strain sensors proximal to the damage site to ensure accurate predictions.

Indirect measurement of solar array rotation angle for satellite emergency rescue

In the emergency rescue mission, the primary task is to achieve the satellite’s orientation to the sun and ensure the energy supply. For this purpose, it is necessary to determine the rotation angle of solar array relative to satellite body. However, the rotation angle cannot be measured directly by the angle sensor in case of satellite emergency. To handle this issue, an indirect measurement method of solar array rotation angle is proposed in this paper. First, the indirect point measurement value of solar array rotation angle is given via the relationship between satellite body coordinate frame and solar array coordinate frame and the Rodrigues’ rotation formula. Then, the interval analysis technique is used to determine the upper and lower bounds of rotation angle and evaluate the influence of measurement noises. Based on the determined point measurement value and the interval analysis results, a zonotopic Kalman filter-based strategy is further proposed to improve the measurement accuracy. Finally, the effectiveness and validity of the proposed approach are demonstrated by numerical simulations, which illustrate its engineering application value.

Using Quadratic Interpolated Beetle Antennae Search to Enhance Robot Arm Calibration Accuracy

During the past decades, industrial robot arms have been widely adopted in various fields, like aerospace and medical care. However, an uncalibrated industrial robot arm suffers from its high absolute positioning error, which tremendously restricts its application in high-precision intelligent manufacture. Related robot arm calibration methods present a potential solution to this issue, while recent algorithms frequently encounter some issues, such as long-tail convergence and low calibration accuracy. To address this thorny issue, this study proposes a novel robot arm calibration method incorporating the extended Kalman filter with Quadratic Interpolated Beetle Antennae Search algorithm. It adopts three-fold ideas: a) developing a new Quadratic Interpolated Beetle Antennae Search algorithm that greatly addresses the issue of local optimum and low convergence rate in a Beetle Antennae Search algorithm; b) adopting an extended Kalman filter algorithm to decrease the influence of measurement noises and c) proposing a robot arm calibration method incorporating an extended Kalman filter with Quadratic Interpolated Beetle Antennae Search algorithm to searching the optimal kinematic parameters of a robot arm. Extensive experiments on an ABB IRB120 industrial robot arm demonstrate that the calibration accuracy of the proposed method is 5.17% higher than that of the state-of-the-art method. Therefore, it is appropriate for practical application occasions with high-precision requirements.

Design of fractional order PID controller based on minimum variance control and application of dynamic data reconciliation for improving control performance

Fractional order PID (FOPID) has received much attention in recent years and has been applied in different fields. However, the parameter tuning of FOPID is a tough problem. In order to design an optimal FOPID controller, a parameter tuning method based on minimum variance control (MVC) rule is proposed in both SISO and MIMO control systems Considering the influence of measurement noise with Gaussian and non-Gaussian distribution, which is generally ignored in the controller design based on MVC rule, this paper applies dynamic data reconciliation (DDR) technology to suppress its influence and improve the control performance of the designed FOPID. In the case of considering Gaussian and non-Gaussian distributed measurement noise, SISO and MIMO control systems are employed to verify the effectiveness of the proposed FOPID parameter tuning and DDR method. The results show that the designed FOPID improves the performance of the control system, and the DDR technology suppresses the negative effect of measurement noise and improves the control performance of the designed FOPID.

Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results.

Sliding Mode Control of Grid-Connected Neutral-Point-Clamped Converters Via High-Gain Observer

In this article, a nonlinear high-gain observer (NHGO)-based second-order sliding mode (SOSM) control strategy is proposed for the three-phase three-level neutral-point-clamped (NPC) converter. This controller applies the advanced SOSM algorithm both in the voltage regulation loop and in the power tracking loop, which provides a fast dynamic for the dc-link voltage, and also assures a good steady-state behavior for the NPC converter. Additionally, an NHGO technique is implemented in the voltage regulator combining with the SOSM algorithm. The conventional observer-based controllers suffer from the destructive effects of measurement noise, and it can only be addressed by diminishing the observer gain, which sacrifices the observer property. The NHGO technique adopts a time varying gain, that is, high gain in transient while low gain in steady state, which minimizes the adverse influence of measurement noise. The tuning method of the proposed NHGO-based SOSM controller is given to simplify the implementation process. Finally, the simulation and experimental results of the proposed control scheme for the NPC converter are given and compared with the conventional PI controller as well as the well-known linear extended state observer-based control method, which validates the feasibility and superiority of the proposed controller.

Performance assessment for non-Gaussian systems by minimum entropy control and dynamic data reconciliation

Control performance of the industrial process is inevitably influenced by the measurement noises and non-Gaussian external disturbances. This influence has not been fully considered in the traditional variance-based controller design. To reduce the influence, a novel scheme that can enhance the control performance is developed by integrating dynamic data reconciliation (DDR) into minimum rational entropy control (MREC) in this paper. Firstly, the influence of measurement noise is fully considered, and a novel DDR method is proposed to deal with the minimum entropy control (MEC) process such that the influence of measurement noise can be reduced, and the control performance will be improved. Then, based on the DDR-MREC performance index, a benchmark for evaluating the control performance of non-Gaussian systems is established. Finally, the proposed control performance assessment (CPA) method is applied to the wind energy conversion system and compared with the CPA method based on DDR-minimum variance control. The experimental results have demonstrated that the proposed new method is more effective than existing works.

Time‐varying formation control for multi‐agent systems under directed topology base on gain re‐adaptation fault‐tolerant compensation approach

AbstractThe distributed fault‐tolerant time‐varying formation control problem for second‐order multi‐agent systems (MAS) with loss‐of‐effectiveness and bias actuator faults over the directed topology is investigated in this article. A novel robust fault‐tolerant state feedback controller is constructed with the re‐adaptation gain and adaptive compensation, where the re‐adaptation control gain is introduced to accomplish formation control with no need for any global knowledge of interaction network among the agents in the entire system. The adaptive compensation is addressed to cope with the unknown bound of faults and time‐varying formation. Moreover, the adaptive laws will not always rise because of the influence of measurement noise. On the analysis of Lyapunov stability, one can obtain that all signals of the closed‐loop fault‐tolerant control system are uniformly bounded, and the formation errors can asymptotically converge to zero. The numerical simulation of second‐order MAS is performed to prove the effectiveness of the mentioned results.

A Comparative Study of Observer-Based State-of-Charge Estimation using Single-Particle Model with Electrolyte Dynamics and Equivalent Circuit Model of Lithium-ion Cells

Various techniques have been implemented for battery state-of-charge estimation over the years, with the most common being the equivalent-circuit model (ECM). Among the physics-based models, the single-particle model (SPM) has found regular usage for state-of-charge estimation. The observers designed using the ECM and a recently developed physics-based models incorporating electrolyte dynamics for high C-rate applications are compared in this work. The results demonstrate improvement in the estimation performance of the observer by 7% for the proposed SPM as compared to ECM. The SoC estimation under the influence of measurement noise are also investigated for different ambient temperatures.

Reliable Algebraic Fault Detection and Identification of Robots

This paper presents a reliable algebraic method for fault detection and identification for rigid robots considering additive faults and external forces. The presented method is based on the reconstruction of a residual or the fault by nonlinear differential algebraic expressions using the polynomial approximation operator. Furthermore, it is shown how the filter parameters and the noise suppression properties of the resulting filter can be parameterized for reliable fault diagnosis according to the characteristics of the fault signals in the frequency domain. The presented method provides an evaluable expression that can be used to explicitly estimate the influence of measurement noise, quantization, numerical errors, and parameter uncertainties in terms of computable error bounds, thus ensuring reliable fault diagnosis. Based on a SCARA robot, a comparison of the method with the generalized momentum observer is performed, highlighting the advantages of the presented approach. Furthermore, it is applied to a real-world application of a delta robot with parallel kinematics for the identification of additive faults while considering interactions due to the application. For this purpose, the appropriate parameterization of the filter is determined and then the feasibility of the method is verified by the implementation on a real-time controller.

Displacement detection based on Bayesian inference from GNSS kinematic positioning for deformation monitoring

Displacement is an important parameter in engineering analysis in structural mechanics and geomechanics. For decades, displacement detection based on the Global navigation satellite system (GNSS) has increasingly been important for a wide range of applications, from landslide monitoring, subsidence survey, to industrial measurement. However, due to the influence of measurement noise, it is still a challenge to identify and extract displacement from GNSS kinematic positioning results. To resolve this, we propose a novel displacement detection approach with the purpose of identifying and extracting displacement from GNSS kinematic positioning. Specifically, we use the Bayesian inference to obtain the displacement change time from the coordinate time series of GNSS kinematic positioning. By investigating the posterior distribution of the designed change point parameter, we can identify the change points. Furthermore, we derive the mean value from the posterior distribution of the mean parameter, and further obtain the displacement. Results from simulation and field experiments have demonstrated the effectiveness and flexibility of the proposed method. For significant displacement, it can be clearly identified; for small displacement, it can be identified by adding an interval constraint prior. The accuracy of vertical displacement extraction from GNSS real-time kinematic positioning can reach within 2 mm in 15 min.

Indirectly measuring the displacements of tension structures under dominant design loads by exerting simple testing loads

Structural stiffness of a real tension structure may degrade due to many factors, so the stiffness monitoring is very important. If the displacements under the dominant design loads are obtained, the stiffness state of the real structure can be intuitively assessed by a deformation checking process. However, exerting design loads on a real structure is neither efficient nor economical, because they usually have too many loading points. A method is hence proposed to indirectly measure the displacements under the design loads by merely exerting several simple testing loads. By utilizing the linear combination of several contributing eigenvectors to express the displacements, the problem of measuring displacements is transformed to that of measuring combinational coefficients of these contributing eigenvectors. The relationship between the combinational coefficients of the mutual contributing eigenvector of the design load and the testing load is set up firstly, with two basic conditions established. To satisfy the basic conditions, a strategy to decompose the design load and an algorithm to find proper testing loads are suggested. The numerical example of a cable net with two typical design loads is finally analyzed. Results illustrate that the proposed indirect method is effective, with three simple testing loads (no more than 3 loading points) found to replace the two design loads (181 loading points) in the on-site static testing. Besides, the proposed indirect method is more robust to resist the influence of measurement noise than the traditional direct method. The reason why the proposed indirect method has better robustness is also discussed.

REAL TIME SEARCH OF AGRICULTURAL MACHINERY BASED ON MATRIX SEQUENCE SENSOR

Omni-directional vision sensor can provide information within the sensor range, and the directional angle of an object can be accurately obtained through omni-directional images. Based on this characteristic, an automatic navigation and positioning system for agricultural machinery is developed, and a three-dimensional positioning algorithm for agricultural wireless sensor networks based on cross particle swarm optimization is proposed. The method mainly includes three stages: convergence node selection, measurement distance correction and node location. Using the idea of crossover operation of genetic algorithm for reference, the diversity of particles is increased, and the influence of ranging error and the number of anchor nodes on positioning results is effectively improved. The location algorithm has the ability of global search. On the positioning node, the symmetric bidirectional ranging algorithm based on LFM (Linear frequency modulation) spread spectrum technology is used to calculate the distance between the positioning node and each beacon node, and the trilateral centroid positioning algorithm is used to calculate the coordinate position information of unknown nodes. Finally, the Kalman filter algorithm is used to superimpose the observed values of the target state to solve the influence of measurement noise on the positioning accuracy.

Structural damage identification based on dynamic deflection sensitivity of bridge under vehicle loading

Based on the concept of dynamic response sensitivity, a method of identifying bridge structural damage by using the dynamic deflection response of bridge under vehicle load is proposed. Firstly, the dynamic equation of vehicle-bridge coupling dynamic system is established. And then, the basic principle and iterative identification process of bridge damage identification based on dynamic deflection response sensitivity are derived. Finally, the effectiveness of the algorithm is verified with a numerical example of three-span continuous beam vehicle-bridge coupling system. The research results show that the proposed algorithm can overcome the influence of measurement noise and initial model error, and realize the damage location and quantification of bridge from dynamic deflections of a few measuring points.

Performance of unscented Kalman filter for model updating with experimental data

AbstractThe unscented Kalman filter (UKF) is one of the most widely used algorithms for identifying and updating numerical model parameters. When updating from experimental data, the UKF performs well but it is sensitive to the selection of the initial algorithm variables and vulnerable to the influence of measurement noise. Furthermore, the ability to capture new behavioral features, such as hardening at large displacements, is a challenge. To achieve a more robust algorithm that can learn new features, techniques from the constrained UKF and the adaptive UKF are combined with an additional weighting on learning based on the magnitude of the input. The weighted adaptive constrained unscented Kalman filter (WACUKF) updates the numerical model with adaptive calculation of measurement noise and assigns a different learning rate for measurement data through the weighting function. To confine parameter values in the ranges with physical sense, the WACUKF adopts the sigma points projecting strategy for constrained parameters. Data from two experiments conducted on different structural components, in different laboratories, with different setups are used to validate the effectiveness of the WACUKF and compare against the performance of the UKF. The results demonstrate that the WACUKF can capture and preserve new features during model updating and is more robust and accurate than the UKF.

A Robust Circuit and Controller Parameters’ Identification Method of Grid-Connected Voltage-Source Converters Using Vector Fitting Algorithm

This article presents a vector fitting (VF) algorithm-based robust circuit and controller parameters’ identification method for grid-connected voltage-source converters (VSCs). The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dq$ </tex-math></inline-formula> -domain impedance frequency responses (IFRs) of the VSCs are first measured using the frequency scanning method, based on which the corresponding measured phasor-domain IFRs are calculated. Then, polynomial transfer functions are generated by applying the VF algorithm on the measured phasor-domain IFRs, from which the circuit and controller parameters, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LCL$ </tex-math></inline-formula> filter parameters, digital sampling time, current controller parameters, and phase-locked loop parameters, are identified. The influence of measurement noise on parameters’ identification accuracy and corresponding countermeasure to mitigate the adverse influence are also theoretically investigated. The proposed method is able to identify the circuit and controller parameters when detailed parameters are missing due to industrial secrecy or parameters variation caused by operating condition change, temperature fluctuation, or aging. The effectiveness of the proposed circuit and controller parameters’ identification method is validated by theoretical demonstration, OPAL-RT-based real-time simulation, and experimental validation.

Dynamic data reconciliation to improve the result of controller performance assessment based on GMVC

Due to the complexity of the industrial working environment, controllers are susceptible to various disturbance signals, resulting in unsatisfactory control performance. Therefore, it is especially important to assess the controller performance. Considering the harmful effect of measurement noise on controller performance assessment (CPA) based on generalized minimum variance control (GMVC), this paper proposes dynamic data reconciliation (DDR) to improve the accuracy of CPA based on GMVC. The paper first introduces CPA based on GMVC, and then analyzes the influence of measurement noise on GMVC based CPA index. DDR combined with GMVC based CPA is then proposed and analyzed in both SISO and MIMO systems to weaken the impact of measurement noise on CPA index. For both Gaussian distributed noise and non-Gaussian distributed noise, the formulation of DDR is derived from the Bayesian formula and maximum likelihood estimate. The effectiveness of the proposed method is verified in different case studies (involving both SISO and MIMO systems), and further verified by the control process of DC–AC converter. The simulation and experiment results demonstrate that the results of CPA based on GMVC can be obviously improved by using DDR.