Error Model Research Articles

State estimation of magnetorheological suspension of all-terrain vehicle based on a novel adaptive Sage–Husa Kalman filtering

Abstract For the magnetorheological (MR) suspension control system of all-terrain vehicles (ATVs), state estimation is an effective method to obtain system feedback signals that cannot be directly measured by sensors. However, when confronted with modeling errors and sudden changes in sensor noise during complex road driving, conventional estimation methods with fixed parameters encounter challenges in accurately estimating the states of ATV suspension system. To address this issue, this paper introduces a novel adaptive Sage-Husa Kalman filter (ASHKF) algorithm to estimate the sprung and unsprung velocity of ATV suspension system. The algorithm uses exponential weighting function and gradient detection function to adaptively adjust the attenuation coefficient according to the driving conditions of the ATV, thereby realizing real-time correction of the covariance matrix of the prediction error. Ultimately, through simulation and real-vehicle testing, it is demonstrated that the designed ASHKF is able to effectively improve the state estimation accuracy of the speed signal of the suspension system under off-road driving conditions with low-frequency noise and outlying disturbances, and the accuracy is improved by 62.70% compared with that of the conventional Sage-Husa Kalman filter (SHKF).

Real-time 3D MR guided radiation therapy through orthogonal MR imaging and manifold learning.

In magnetic resonance image (MRI)-guided radiotherapy (MRgRT), 2D rapid imaging is commonly used to track moving targets with high temporal frequency to minimize gating latency. However, anatomical motion is not constrained to 2D, and a portion of the target may be missed during treatment if 3D motion is not evaluated. While some MRgRT systems attempt to capture 3D motion by sequentially tracking motion in 2D orthogonal imaging planes, this approach assesses 3D motion via independent 2D measurements at alternating instances, lacking a simultaneous 3D motion assessment in both imaging planes. We hypothesized that a motion model could be derived from prior 2D orthogonal imaging to estimate 3D motion in both planes simultaneously. We present a manifold learning technique to estimate 3D motion from 2D orthogonal imaging. Five healthy volunteers were scanned under an IRB-approved protocol using a 3.0 T Siemens Skyra simulator. Images of the liver dome were acquired during free breathing (FB) with a 2.6mm × 2.6mm in-plane resolution for approximately 10min in alternating sagittal and coronal planes at ∼5 frames per second. The motion model was derived using a combined manifold learning and alignment approach based on locally linear embedding (LLE). The model utilized the spatially overlapping MRI signal shared by both imaging planes to group together images that had similar signals, enabling motion estimation in both planes simultaneously. The model's motion estimates were compared to the ground truth motion derived in each newly acquired image using deformable registration. A simulated target was defined on the dome of the liver and used to evaluate model performance. The Dice similarity coefficient and distance between the model-tracked and image-tracked contour centroids were evaluated. Motion modeling error was estimated in the orthogonal plane by back-propagating the motion to the currently imaged plane and by interpolating the motion between image acquisitions where ground truth motion was available. The motion observed in the healthy volunteer studies ranged from 12.6 to 38.7mm. On average, the model demonstrated sub-millimeter precision and>0.95 Dice coefficient compared to the ground truth motion observed in the currently imaged plane. The average Dice coefficient and centroid distance between the model-tracked and ground truth target contours were 0.96±0.03 and 0.26mm±0.27mm respectively across all volunteer studies. The out-of-plane centroid motion error was estimated to be 0.85mm±1.07mm and 1.26mm±1.38mm using the back-propagation (BP) and interpolation error estimation methods. The healthy volunteer studies indicate promising results using the proposed motion modeling technique. Out-of-plane modeling error was estimated to be higher but still demonstrated sub-voxel motion accuracy.

Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent

In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.

Assessing Roles of Aggregate Structure on Hydraulic Properties of Saline/Sodic Soils in Coastal Reclaimed Areas

During coastal reclamation processes, land use conversion from natural coastal saline/sodic soils to agricultural land changes the soil’s physicochemical properties. However, the impact of soil structure evolution on soil hydraulic properties (SHPs, e.g., hydraulic conductivity and soil water retention curves) during long-term reclamation has rarely been reported. In this study, we aimed to evaluate the effect of reclamation duration and land use types on the soil aggregate stability and SHPs of coastal saline/sodic soils and incorporate the aggregate structures into the SHPs. In this study, a total of 90 soil samples from various reclaimed years (2007, 1960, and 1940) and land use patterns (cropland, grassland, forestland, and wasteland) were taken to analyze the quantitative effects of soil saline/sodic characteristics and the aggregate structure on SHPs through pedotransfer functions (PTFs). We found that soil macroaggregate contents in the old reclaimed areas (reclaimed in 1940 and 1960) were significantly larger than those in the new reclamation area (reclaimed in 2007). The soil saturated hydraulic conductivity (Ks) of forestland was larger than that of grassland in each reclamation year. Soil structure contributed to 22.13%, 24.52%, and 23.93% of the total variation in Ks and soil water retention parameters (α and n). The PTFs established in our study were as follows: log(Ks) = 0.524 − 0.177 × Yk3 − 0.093 × Yk1 + 0.135 × Yk4 − 0.054 × Yk2, 1/α = 477.244 − 91.732 × Yα2 − 81.283 × Yα4 + 38.106 × Yα3, and n = 1.679 − 0.086 × Yn2 + 0.045 × Yn1 − 0.042 × Yn3 (Yareprincipalcomponents). The mean relative errors of the prediction models for log(Ks), 1/α, and n were 79.30%, 36.1%, and 9.89%, respectively. Our findings quantify the vital roles of the aggregate structure on the SHPs of coastal saline/sodic soils, which will help us understand related hydrological processes.

Strong decay widths and mass spectra of the 1D , 2P and 2S singly bottom baryons

We calculate the 1D, 2P, and 2S mass spectra of the singly bottom baryons and their strong decay widths. The calculations are performed within a harmonic oscillator quark model that incorporates the spin, spin-orbit, isospin, and flavor interactions. To obtain the model parameters, we conducted a fit using only 13 of the 22 experimentally observed states. Our predictions align well with the observed states, showing a root-mean-square deviation of 9.6 MeV. We calculate the three-quark strong decay widths within the P03 model, which has only one free parameter, the pair creation strength γ0; this is the first time that the Λbη, Σbρ, Σb*ρ, Λbη′, Λbω, ΞbK, Ξb′K, Ξb*K, ΞbK*, Ξb′K*, and Ξb*K* channels have been considered in the calculation of the strong decay widths of the excited Λb states; the Σbη, ΞbK, Σbρ, Σb*ρ, Λbρ, Σb*η, Σbη′, Σb*η′, Ξb′K,Ξb*K, ΞbK*, Ξb′K*, Ξb*K*, Σbω, Σb*ω, Σ8Bs, ΔB, N(1520)B, N(1535)B, N(1680)B, and N(1720)B channels in the calculation of the strong decay widths of the excited Σb states; the ΛbK*, Ξbρ, Ξb′ρ, Ξb*ρ, ΣbK*, Σb*K*, Ξbη′, Ξb′η′, Ξb*η′, Ξbω, Ξb′ω, Ξb*ω, Ξbϕ, Ξb′ϕ, Ξb*ϕ, Ξ8Bs, Σ8B*, and Σ10B channels in the calculation of the strong decay widths of the excited Ξb and Ξb′ states; the ΞbK*, Ξb′K*, Ξb*K*, Ωbη, Ωb*η, Ωbϕ, Ωb*ϕ, Ωbη′, Ωb*η′, Ξ8B, and Ξ10B channels in the calculation of the strong decay widths of the Ωb states. Moreover, in Appendix D, we give the flavor couplings that can be useful for other articles. In Appendix E, our partial decay widths are reported for each open flavor channel; these may be useful to the LHCb, ATLAS, and CMS experimentalists in order to plan in which particular channels to look for missing bottom baryons. The experimental masses and widths of the discovered Λb(6146)0 and Λb(6152)0 states are consistent with our mass and width predictions for the Dλ excitations with quantum numbers JP=32+ and JP=52+, respectively. Moreover, the masses and widths of the new Ξb(6327)0 and Ξb(6333)0 states agree with our calculations for the Dλ excitations with quantum numbers JP=32+ and JP=52+, respectively. Finally, we calculate the electromagnetic decay widths from P-wave states to ground states. We give the exact analytical expressions of the spin-flip and orbit-flip transition amplitudes, both of which are functions of the photon-transferred momentum. The electromagnetic decays are dominant when the strong decays are suppressed. A relevant case is the Ωb− missing spin excitation, with JP=32+, which cannot decay strongly, but has a nonvanishing predicted electromagnetic decay width in the Ωb−γ channel. Therefore, we suggest the Ωb−γ electromagnetic decay channel as a golden channel in which to search for this state. In all of our calculations, we report the uncertainties related to the experimental and model errors by means of the Monte Carlo bootstrap method. Published by the American Physical Society 2024

Selected Aspects of Positioning with the GNSS Galileo

The quality of Galileo system services is affected by the accuracy of distance determination from the user’s application to the individual satellite. The goal of our research was to find out what influence the accuracy of distance determination between the user’s application of the Galileo system and the cooperating satellites of the Galileo system has on the ability to determine the location of the user’s application. A solution based on Groebner’s algebraic approaches was used to determine the receiver user’s position. When creating distance measurement error models between the Galileo system user’s receiver and cooperating satellites, we assumed that the values of those errors considered all factors that affected the accuracy of those distance measurements. To evaluate the algorithms, we used a statistical set of 500 simulation results to determine the positioning of the user’s application of the Galileo system. If the distances between the user’s application and the individual satellite were measured accurately, then the user’s application coordinate errors had values between 1.86 × 10−9 m and −1.8 × 10−8 m. These errors should be equal to zero. The positioning error was caused by a numerical error in the calculation due to the software used. If the errors of distance determination from the user’s application to the individual satellite varied from −0.05 m to 0.09 m, then the error in determining the positioning of the user’s application of the Galileo system was from 0.0 m to 1.2 m. If the distances of the user’s receiver to the satellites were measured with errors greater than 0.09 m, the errors in determining its position were much larger. The simulation results confirmed the known fact that the satellites’ geometry influences the accuracy of determining the location of the user’s application. In the following research, we will solve the problem of how to reduce the sensitivity of the mentioned algorithms when determining the position of the satellite navigation system receiver due to errors in determining the distance from the user’s application to the individual satellite.

DeepMulticut: Deep Learning of Multicut Problem for Neuron Segmentation From Electron Microscopy Volume.

Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NP-hard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver-the greedy additive edge contraction (GAEC)-to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut.

Enhancing model robustness to imbalanced species abundance distributions: Eliminating misclassified records via a model-agnostic approach, exemplified by tuna fisheries datasets

Anomalies in species abundance data can potentially cause classification errors in ecological forecasting models. Accurate estimation of anomalies locations can enhance the predictive capacity of models. This study aims to propose an approach for precisely identifying and correcting anomalies within imbalanced species abundance data, thereby addressing the challenges posed by both anomalous and imbalanced species abundance distributions (SADs). A model-agnostic statistical tool, Confident Learning (CL) theory, is introduced to estimate the probability of each sample being misclassified during the prediction phase. Specifically, the approach targets classification errors from models trained on imbalanced SADs for data-cleansing, identifying these records as anomalies. The approach is applied to tuna fisheries datasets, focusing specifically on bigeye tuna (Thunnus obesus), a targeted species in longline fishing, and albacore tuna (Thunnus alalunga), a non-target species in the tropical Atlantic Ocean. These datasets, spanning from 2016 to 2019, featured a spatial resolution of 0.5° × 0.5° and daily temporal resolution, providing a comprehensive view of imbalanced data scenarios. The results demonstrate that all the predictors: Support Vector Machine (SVM), Logistic Regression (LR), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) were considerably enhanced after training on the cleaned datasets. Notably, SVM and LR achieved overall accuracy rates of over 90% on predicting both low- and high-abundant fishing grounds. The proposed approach reveals that the elimination of anomalies can enhance the robustness of ecological forecasting models to imbalanced SADs, offering new insights and technical support for the delicate prediction and assessment of ecological resources.

Prediction of Summer Precipitation via Machine Learning with Key Climate Variables：A Case Study in Xinjiang, China

Study region: Xinjiang is located in the mid-latitude region of Eurasia in northwestern China. Precipitation is predominantly concentrated in northern Xinjiang, while southern Xinjiang remains comparatively arid. Summer precipitation accounts for 54.4% of the annual total. Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. The SHapley Additive exPlanations method was integrated with an extreme tree model to quantify the contributions of variables towards precipitation. Artificial neural networks, support vector machines, and extreme gradient boosting were considered to predict summer precipitation. To train the ML model, we used precipitation data from 1961 to 2012, whilst the forecast results from 2013 to 2017 were used for validation.New hydrological insights for the regions: The results demonstrated that the ANN model achieved robust performance during both the training and validation periods. For Northern and Southern XJ, the Mean Absolute Error and Root Mean Square Error of the ANN model were 15.34 (20.40) and 23.21 (30.01), respectively. The SHAP analysis showed that in the context of Northern Xinjiang, the Niño B Sea Surface Temperature Anomaly, Western Pacific Subtropical High Intensity, Pacific Subtropical High Intensity, and Multivariate ENSO Index play crucial roles in the prediction of summer precipitation. In Southern Xinjiang, the South China Sea Subtropical High Intensity, South China Sea Subtropical High Area, Western Pacific Warm Pool Strength, and Atlantic multidecadal oscillation have emerged as key variables affecting summer precipitation forecasting.

Digital-driven in-situ monitoring for thermally-induced volumetric errors of CNC machine tools

In order to realize the in-situ monitoring of thermally-induced volumetric errors of CNC machine tools, a digital twin system is designed and developed through combined programming of C#, ANSYS, and MATLAB. The thermal error mapping models are constructed through superposing the axis motion-induced thermal errors and the spindle rotation-induced thermal deformations. The models of axis motion-induced thermal errors are established based on the orthogonal polynomial tabular modeling method, and real-timely corrected through correction models which are constructed based on the predicted and measured errors. The spindle rotation-induced thermal deformations are obtained via digital twin for thermal characteristics of the spindle system. The error distributions are generated by MATLAB through pre-creating the DLL file. Experimental results show that the prediction accuracy of digital twin-driven in-situ monitoring for thermally-induced volumetric errors is greater than 95 %, which effectively improvs the prediction accuracy of thermally-induced volumetric errors of CNC machine tools and provides a basis for thermal error compensation.

Recent Patents of Thermal Error Modeling and Thermal Error Prediction of CNC Machine Tool Spindle

: With the development of machine tools to high precision and high automation, thermal error has become the key reason to hinder the improvement of thermal stability of machine tools. The spindle electrothermal error is an important factor affecting the machining accuracy of highspeed CNC machine tools. The thermal error model of a high-speed motorized spindle is established to compensate the thermal error, which can effectively improve the influence of thermal error on the machining accuracy of machine tools. The thermal displacement during the operation of motorized spindle is the main reason that affects the machining stability and precision of motorized spindle. The thermal error compensation of CNC machine tool spindle can improve the machining efficiency, the machining quality of parts and the service life of CNC machine tools. This paper summarizes the representative patents and papers on thermal error modeling and thermal error prediction in China. By summarizing a large number of patents and papers on modeling, prediction and compensation of thermal error of spindle of CNC machine tools, the modeling method is analyzed and the development trend is discussed. The improvement of thermal error modeling method of CNC machine tool spindle has an important influence on improving machining accuracy, efficiency, and stability. Therefore, in the future, there will be more and more patents for thermal error modeling of CNC machine tools and spindles.

Model-based dimensional NDE from few X-ray radiographs: Application to the evaluation of wall thickness in metallic turbine blades

The extraction of 3D dimensional measurements based on a limited number of 2D X-ray radiographs of a part would offer a significant speed-up of quality control procedures in industry. However, there are challenges with respect to both measurements and uncertainties. This work addresses these challenges by creating an estimated numerical model of the imaged part on which dimensional measurements can be made. The numerical model is chosen as a parametric deformable model that encodes the expected shape variability of the parts resulting from the manufacturing process. The parameters and uncertainties of the numerical model of the imaged part are estimated by the registration of the computed projections of the model and the observed radiographs without the need of any segmentation. The registration requires the model, the initial parameters, and the observed radiographs. The proposed approach is applied to the inspection of turbine blades manufactured by investment casting, and in particular to the measurement of their wall thickness, which is a critical control. The deformable model consists in partitioning the inner ceramic core into multiple subparts, which may undergo a rigid body motion with respect to the master die. Wall thickness measurements are determined from the estimation of these rigid body motions. To assess the reliability of the proposed procedure, a repeatability study is performed. In addition, wall thickness measurements were compared to corresponding measurements from the surface of the metal boundary obtained by X-ray computed tomography. This surface was determined from a reconstructed tomogram using commercial software. Both analyses show that such measurements are reliable and efficient. Furthermore, residual differences between captured and computed projections reveal localized shape deviations from the CAD model, meaning that despite localized model errors, the approach is operable.

Automated Identification of a Hybrid VSI Error Model for PMSM Drives

Automated Identification of a Hybrid VSI Error Model for PMSM Drives

Drought risk assessment for citrus and its mitigation resistance under climate change and crop specialization: A case study of southern Jiangxi, China

Crop specialization has become an important way to promote agricultural efficiency and increase incomes for farmers in China. Under extreme weather conditions, this is likely to increase resistance to drought risk mitigation and lead to greater drought damage. In order to make more sophisticated mitigation policy decisions, it requires efforts to assess crop drought risk variability and its risk mitigation resistance in detail. This study focuses on citrus as a cash crop, using a case of southern Jiangxi. We first constructed a drought risk assessment system for citrus, and then geographic information technology was used to characterize the risk dynamics. The factor contribution model was applied to calculate the risk mitigation resistance. And the least square error (LSE) model was finally employed to classify risk mitigation resistance types. The results showed that citrus drought risk had fluctuated moderately over macro-regions in the past 30 years, but had varied dramatically at local scales. Citrus drought risk significantly increases during the bud bust to flowering, fruit expansion, and fruit maturation stages. Exposure contributed 33.23 % to drought risk fluctuations, specifically, mainly attributed to the amplification of drought exposure in the farmer and agricultural production sectors, while 14.95 % was derived from climate change. In large-scale citrus expansion areas, the improvement in drought mitigation capacity generally lags behind the increase rate of the exposure system. The LSE model showed that risk mitigation resistance type varied across regions at system level and indicator level. In addition, water scarcity has emerged as a widespread issue that limits drought risk mitigation. Generally, this study provides new insights into the measurement of drought risk mitigation resistance and its type identification, thus contributing to the safe supply of agricultural products and human well-being.

A Fixed‐Time Composite Control With Variable Exponent Coefficients for Stewart Parallel Mechanism Tracking in Task Space

ABSTRACTTo improve tracking control performance in the task space of the Stewart parallel mechanism (SPM) with uncertainties of modeling errors and external disturbances in our developed rust removal sandblasting robot for the surface roughness processing of a bridge's steel box girder, a fixed‐time composite control with variable exponent coefficients (VEC‐FxTCC) approach for multi‐input multi‐output (MIMO) nonlinear robotic systems is developed. Firstly, by devising only one regulation term with a variable exponent coefficient (VEC) function in the system's Lyapunov differential inclusion, a novel VEC fixed‐time stability theorem for the MIMO nonlinear dynamic system is proposed and proven. Secondly, based on the proposed theorem, VEC‐FxTCC is formed by designing and combining a VEC fixed‐time disturbance observer (VEC‐FxTDOB) and a VEC fixed‐time super‐twisting control (VEC‐FxTSTC), in order to achieve fast, steady, and uniformly bounded‐time convergence for SPM's end‐effector tracking, while avoiding singularity. Finally, the effectiveness of the proposed VEC‐FxTCC approach is validated through the simulations and the rust removal sandblasting robot prototype experiments.

Socio-economic and environmental factors are related to acute exacerbation of chronic obstructive pulmonary disease incidence in Thailand.

Chronic Obstructive Pulmonary Disease (COPD) is a significant global health issue, leading to high rates of sickness and death worldwide. In Thailand, there are over 3 million patients with the COPD, with more than a million patients admitted to hospitals due to symptoms of the disease. This study investigated factors influencing the incidence of acute exacerbations among COPD patients in Thailand, including the spatial autocorrelation between socioeconomic and environmental factors. We conducted a spatial analysis using Moran's I, Local Indicators Of Spatial Association (LISA), and spatial regression models, specifically the Spatial Lag Model (SLM) and the Spatial Error Model (SEM), to explore the relationships between the variables. The univariate Moran's I scatter plots showed a significant positive spatial autocorrelation of 0.606 in the incidence rate of COPD among individuals aged 15 years and older across all 77 provinces in Thailand. High-High (HH) clusters for the COPD were observed in the northern and southern regions, while Low-Low (LL) clusters were observed in the northern and north-eastern regions. Bivariate Moran's I indicated a spatial autocorrelation between various factors and acute exacerbation of COPD in Thailand. LISA analysis revealed 4 HH clusters and 5 LL clusters related to average income, 12 HH and 8 LL clusters in areas where many people smoke, 5 HH and 8 LL clusters in areas with industrial factory activities, 11 HH and 9 LL clusters associated with forested areas, and 6 LL clusters associated with the average rice field. Based on the Akaike information criterion (AIC). The SLM outperformed the SEM but only slightly so, with an AIC value of 1014.29 compared to 1019.56 and a Lagrange multiplier value of p<0.001. However, it did explain approximately 63.9% of the incidence of acute exacerbations of COPD, with a coefficient of determination (R² = 0.6394) along with a Rho (ρ) of 0.4164. The results revealed that several factors, including income, smoking, industrial surroundings, forested areas and rice fields are associated with increased levels of acute COPD exacerbations.

Switching funnel transformation function-based discrete-time sliding-mode control for servo systems with time-varying external disturbances

This paper proposes a switching funnel transformation function-based discrete-time sliding-mode control with lower cost to prevent the issue of control failure caused by time-varying external disturbances leading to tracking errors exceeding the performance boundary. This scheme employs an offline spectral regularization-based neural network with good generalization capability to approximate the unknown nonlinear dynamics and modeling errors in the system, resulting in a more accurate discrete-time system model. Based on the discrete-time system model with time-varying external disturbances, a novel discrete-time switching funnel transformation function-based sliding surface is proposed to solve the problem when the tracking error exceeds performance boundaries. The discrete-time funnel boundaries are switched through a predefined event-triggered mechanism, ensuring that the tracking error remains within the performance boundaries at all times, thereby avoiding control failure. Furthermore, a time-varying sliding mode variable reaching rate is proposed to reduce control cost. Finally, theoretical analysis demonstrates that the tracking error remains within a smaller funnel region compared to the predefined one, and the sliding-mode variable ultimately stays within a bounded sliding-mode boundary. Experimental results on SCARA verify the effectiveness of the proposed method.

Underwater radiated noise characteristics and source spectrum model of typical ships in Yangtze River

To address the ecological challenge posed by the underwater radiated noise (URN) from ships in the Yangtze River, it is crucial to thoroughly understand its characteristics and establish a comprehensive source spectrum model. In this study, the spectral characteristics and transmission loss (TL) law of URN from ships in the Yangtze River were investigated by the on-site monitoring. A spectral source level (SSL) for a "reference" ship was constructed, and the URN source spectrum model was optimized. The results indicated that the URN frequencies predominantly ranged from 25 to 2500 Hz, with source levels (SL) for each one-third octave (1/3-octave) band distributed between 140 and 175 dB (re μPa). The hydroacoustic TL coefficient increased with frequency, indicating greater attenuation at higher frequency. The absolute error of the source spectrum models of typical ships in the Yangtze River was within ±10 dB (re μPa), showing strong agreement with the measurement values. These findings provide theoretical support for the coordinated development of shipping activities and ecological protection in the Yangtze River.

Geometric error modeling and source error identification methodology for a serial–parallel hybrid kinematic machining unit with five axis

Geometric error modeling and source error identification methodology for a serial–parallel hybrid kinematic machining unit with five axis

Compensator based improved model predictive control for Aero-engine

Abstract Model predictive control (MPC) can be applied to aero-engine for optimal command tracking and constraint handling. However, the performance of MPC is highly dependent on the accuracy of the predictive model. Therefore, a new structure MPC with an additional modeling error compensation loop is proposed, which can substantially reduce the dependence of MPC on the accuracy of the predictive model, and enhance the performance and real-time property of the system simultaneously. The new MPC replaces the traditional feedback correction loop with a compensation control loop to realize high-performance tracking over the large envelope with only one predictive model. The compensation loop controller employs an augmented discrete linear quadratic method for robust tracking capability. Simulations on the turboprop engine show that, compared with the standard MPC, the new MPC shows greater robustness over the envelope with smaller settling time, overshoot, and fluctuation of power turbine speed.