External Error Research Articles

Predictor‐based bipartite time‐varying formation control of nonlinear multi‐agents systems via disturbance observer

AbstractFormation control is one of critical control issues in multi‐agent systems (MASs). In this article, we extend the traditional formation to a bipartite one in a structurally balanced signed graph and propose a bipartite formation control strategy for an MAS with unknown external disturbances and nonsymmetric input saturation constraints. Neural networks (NNs) are developed to approximate followers' dynamics, and their transient performance are improved by prediction errors. On this basis, an NN‐based nonlinear disturbance observer (NDO) is constructed to estimate generalized disturbances including external disturbances and NNs approximation errors. In order to reduce the number of learning parameters, a normalization method is introduced for NNs weights and applied to the proposed NDO. An auxiliary system is constructed to compensate for asymmetric input saturation, and it is not required to access unknown control gains. The stability analysis is presented, and it is proven that all signals in the MAS are bounded. A numerical simulation and a team of quadrotors examples corroborate the effectiveness and feasibility of the strategy.

Isochron 26Al/10Be burial dating of the Xiashagou Fauna in the Nihewan Basin, northern China: Implications for biogeography and early hominin dispersals

The Nihewan Basin in northern China is one of the most dense occurrences of Early Pleistocene vertebrate paleontological and Paleolithic sites outside of Africa and thus plays a key role in the study of biochronostratigraphy and early human evolution in East Asia. The Xiashagou Fauna, as the first systematically studied fauna in the basin, is usually referred to as the Nihewan Fauna (sensu stricto) and is considered the Early Pleistocene type fauna in northern China, serving as a key reference for biostratigraphical correlations. The Xiashagou Fauna was initially correlated with the Olivola Fauna of early Late Villafranchian age in Europe, dated to ∼1.8 Ma. It has been more recently constrained to the Matuyama chron between the Réunion and Olduvai subchrons, with an estimated age range of 2.2–1.7 Ma. However, there is still an absence of absolute dates associated with this important Fauna. Here, the isochron 26Al/10Be burial dating method was used to radio-isotopically verify the biostratigraphic and magnetostratigraphic age of the Xiashagou Fauna. Two sets of quartzose clasts were collected from the cross-sections near Xiashagou. The isochron burial age of six samples from one of the fossil-bearing layers is 2.05 ± 0.38 (0.39) Ma (bracketed uncertainty includes external systematic errors, ±1σ), in agreement with previous paleomagnetic estimates of 2.2–1.7 Ma. The isochron age of another five samples from a gravel layer about 80 m below the sampled fossil layer and 55 m beneath the deepest buried fossil layer is 2.43 ± 0.16 (0.18) Ma (±1σ), in agreement with previous paleomagnetic estimates of 2.58 Ma. Our results show that the age of the Xiashagou Fauna is consistent with the updated ages (2.21–1.95 Ma) of the Senèze and Olivola Faunas in Europe. The potential existence of an ecological corridor for faunal dispersals across northern Eurasia during this time period should be important to help contextualize the nature of the earliest hominin dispersals into and across Eurasia. As the first radio-isotopic ages for the Nihewan Basin sites, our results provide independent age control to anchor the magnetostratigraphy of the Early-Middle Pleistocene strata in the Nihewan Basin and to calibrate the Early Pleistocene stage of the Chinese Quaternary.

Research on roundness error consistency model for crank journal cylindrical grinding

Cylindrical grinding is an important way to form the external shape error of the crank journal, and the accuracy consistency directly affects the interchangeability of products. To study the accuracy consistency of crank journal, a dynamic model of the grinding wheel-crankshaft grinding system based on Timoshenko beam is established, and the grinding transition process simulation algorithm with iterative convergence of grinding force-transient grinding amount cycle adapted to the model is proposed, which realizes the simulation of the roundness of the crank journal coupled with the process parameters of the grinding system. Aiming at the grinding position of each crank journal, the grinding roundness of five crank journals is simulated, respectively. On this basis, the crank journal roundness consistency prediction model is established, and the effectiveness of the prediction model is verified by field experiments. Finally, the influence of grinding parameters on the consistency of the roundness of crank journal is studied. The research conclusion can provide a reference for the grinding accuracy consistency design of this type of crank journal.

Active Safety Control of Automated Electric Vehicles at Driving Limits: A Tube-Based MPC Approach

To enhance the active safety performance for automated electric vehicles (AEVs) at driving limits, the collaborative control of four-wheel steering (4WS) and direct yaw-moment control (DYC) is adopted. To deal with external disturbance and modeling error, tube-based model predictive control (MPC) is applied to the control algorithm design, which takes the improvement of handling stability and path-tracking performance into considerations. Taking the constraints into account, including control vector constraints, lateral stability constraints, rollover prevention constraints, and path-tracking error constraints, the integrated controller is designed and worked out by addressing the optimization issue. To verify the effectiveness and feasibility of the integrated controller, two extreme driving conditions are conducted based on hardware-in-the-loop (HIL) tests. The test results indicate that the integrated controller can improve vehicle’s handling stability and path-tracking performance in unison at driving limits. Besides, the integrated controller shows strong robustness in extreme conditions.

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such "representational drift" raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.

Nonsingular fast terminal sliding mode control for manipulator based on combinatorial reaching law

Aiming at the problems of external disturbance, modeling errors, tracking speed and chattering in manipulator trajectory tracking control, a nonsingular fast terminal sliding mode control strategy for manipulator based on combinatorial reaching law was proposed. First, the nonlinear disturbance observer was used to observe the system’s external disturbance and modeling errors. Then, on the basis of the nonsingular fast terminal sliding mode surface, an improved nonsingular fast terminal sliding mode surface was proposed, which improves the convergence speed in the sliding motion process of sliding mode control. At the same time, a combinatorial reaching law combining the power reaching law and the variable speed reaching law was proposed, which improved the approach speed of the system under the premise of weakening the chattering. The simulation results verified the effectiveness of this control strategy.

Autonomous Quadcopter Landing on a Moving Target.

Autonomous landing on a moving target is challenging because of external disturbances and localization errors. In this paper, we present a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared to that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad. No prior information about the landing target is needed. The guidance is based on pure pursuit guidance law. The convergence of the closing velocity controller is shown, and we test the efficacy of the proposed approach through simulations and field experiments. The landing target during the field experiments was manually dragged with a maximum speed of m/s. In the simulations, the maximum target speed of the ground vehicle was 3 m/s. We conducted a total of 27 field experiment runs for landing on a moving target and achieved a successful landing in 22 cases. The maximum error magnitude for successful landing was recorded to be 35 cm from the landing target center. For the failure cases, the maximum distance of vehicle landing position from target boundary was 60 cm.

Asymptotic Tracking Control for Nonaffine Systems With Disturbances

This brief intends to address an adaptive asymptotic tracking control with prescribed performance function for a class of nonaffine systems with unknown disturbances. First, the nonaffine system is transformed into an affine system by using a set of alternative state variables. Subsequently, a prescribed performance function with predefined convergence rate, maximum overshoot and steady-state error is introduced. To achieve the asymptotic tracking control performance, the robust integral of the sign of the error (RISE) feedback term is utilized in the control design to reject the unknown external disturbances and NN approximation errors. Finally, an adaptive controller is presented so that the asymptotic tracking performance with guaranteed prescribed performance is achieved. Comparative experiments are provided to show the effectiveness of the proposed control scheme.

Vibration Suppression of a Coupled Aircraft Wing with Finite‐Time Convergence

A nonlinear coupled wing model subject to unknown external disturbances is proposed in this paper. Since the model is modeled by partial differential equations, the traditional control design scheme based on the ordinary differential equation model is not applicable, and the control law design becomes very complex. In this paper, a new antidisturbance boundary control scheme based on a finite time convergent disturbance observer is proposed. The control laws are designed based on the new disturbance observers to make the external disturbance errors converge to zero in a finite time and ensure the uniformly bounded stability of the controlled system. Finally, the effectiveness of the controllers and the finite‐time convergence of disturbance errors are verified by the simulation and comparison.

MEMS-Scanner Testbench for High Field of View LiDAR Applications

LiDAR sensors are a key technology for enabling safe autonomous cars. For highway applications, such systems must have a long range, and the covered field of view (FoV) of >45° must be scanned with resolutions higher than 0.1°. These specifications can be met by modern MEMS scanners, which are chosen for their robustness and scalability. For the automotive market, these sensors, and especially the scanners within, must be tested to the highest standards. We propose a novel measurement setup for characterizing and validating these kinds of scanners based on a position-sensitive detector (PSD) by imaging a deflected laser beam from a diffuser screen onto the PSD. A so-called ray trace shifting technique (RTST) was used to minimize manual calibration effort, to reduce external mounting errors, and to enable dynamical one-shot measurements of the scanner’s steering angle over large FoVs. This paper describes the overall setup and the calibration method according to a standard camera calibration. We further show the setup’s capabilities by validating it with a statically set rotating stage and a dynamically oscillating MEMS scanner. The setup was found to be capable of measuring LiDAR MEMS scanners with a maximum FoV of 47° dynamically, with an uncertainty of less than 1%.

A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics

Tea is a widely consumed drink in the world which is susceptible to undergoing adulterations to reduce manufacturing costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, as well as to detect and quantify frauds, is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify, and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety and, except for some white tea extracts, perfectly discriminated from the chicory ones. One hundred percent classification rates for the PLS-DA calibration models were achieved, except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, also showing, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available.

Mathematical Modeling and Designing a Heavy Hybrid-Electric Quadcopter, Controlled by Flaps

Systematic hybrid-electric unmanned aerial vehicles (UAVs) and, especially, quadcopters are so promising due to their long flight endurance and their usage in patrol and rescue missions which gain a high interest to be under examination and test scope by researchers; however, a complete mathematical design is required to fulfill theoretical complexities such as aerodynamic analysis and flight dynamics models related. This paper investigates salient sections from hypothesis to implementation. Researchers at Drone Hopper company have conducted various calculations to perform a precise novel platform called Duty-Hopper (DH). The benefit of this design is to control the attitude by flap vanes and electrical ducted fans (EDFs) when using gasoline engines; while, the principle propellers only lift the drone. This paper examines the attitude control system of DH, once using only flaps, then by only EDFs, and eventually, by compounding both. During this research, the scientific software used is ANSYS-Fluent and MATLAB-SimScape to analyze the entire body of the DH. Furthermore, a robust fault-tolerant controller is designed to immune the DH against internal and external errors. Our research reveals that using flaps is a feasible way to control attitude when it is augmented by EDFs.

Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes

Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations that are driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses on the task of minority representations in degree-based rankings. By analysing synthetic and real networks with varying homophily levels and group sizes, we find that the effect of introducing systematic edge errors depends on both the type of edge error and the level of homophily in the system: in heterophilic networks, minority representations in rankings are very sensitive to the type of systematic edge error. In contrast, in homophilic networks we find that minorities are at a disadvantage regardless of the type of error present. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks.

A Nonlinear Model Predictive Control Design for Autonomous Multivehicle Merging into Platoons

Integrated control for automated vehicles in platoons with nonlinear coupled dynamics is developed in this article. A nonlinear MPC approach is used to address the multi-input multi-output (MIMO) nature of the problem, the nonlinear vehicle dynamics, and the platoon constraints. The control actions are determined by using model-based prediction in conjunction with constrained optimization. Two distinct scenarios are then simulated. The first scenario consists of the multivehicle merging into an existing platoon in a controlled environment in the absence of noise, whereas the effects of external disturbances, modeling errors, and measurement noise are simulated in the second scenario. An extended Kalman filter (EKF) is utilized to estimate the system states under the sensor and process noise effectively. The simulation results show that the proposed approach is a suitable tool to handle the nonlinearities in the vehicle dynamics, the complication of the multivehicle merging scenario, and the presence of modeling uncertainties and measurement noise.

In-Flight Actuator Failure Recovery of a Hexrotor Via Multiple Models and Extended High-Gain Observers

We study an in-flight actuator failure recovery problem for a hexrotor UAV. The hexrotor may experience external disturbances and modeling error, which are accounted for in the control design and distinguished from an actuator failure. A failure of any one actuator occurs during flight and must be identified quickly and accurately. This is achieved through the use of a multiple-model, multiple extended high-gain observer (EHGO) based output feedback control strategy. The family of EHGOs are responsible for estimating states, disturbances, and are used to select the appropriate model based on the system dynamics after a failure has occurred. The proposed method is theoretically analyzed and validated through simulations and experiments.

Multivariate calibration on heterogeneous samples

Data heterogeneity has become a challenging problem in modern data analysis. Classic statistical modeling methods, which assume the data are independent and identically distributed, often show unsatisfactory performance on heterogeneous data. This work is motivated by a multivariate calibration problem from a soil characterization study, where the samples were collected from five different locations. Newly proposed and existing signal regression models are applied to the multivariate calibration problem, where the models are adapted to handle such spatially clustered structure. When compared to a variety of other methods, e.g. kernel ridge regression, random forests, and partial least squares, we find that our newly proposed varying-coefficient signal regression model is highly competitive, often out-performing the other methods, in terms of external prediction error.

Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy.

Simple SummaryIn the Puna region of Argentina, llama fibre production has enormous social, economic and environmental potential, but is still in its early stages of development. For this reason, classification and quality analysis systems used today are still deficient. Near infrared reflectance spectroscopy is a technological resource used in the agroindustry for quality analysis of organic compounds. In this work we studied the feasibility of this technology to evaluate the mean fibre diameter, which is one of the most important quality parameters in the textile industry. Despite some limitations, which are mainly related to fibre heterogeneity, the results obtained were encouraging as spectroscopy could be used in screening programmes as a sustainable, fast and low-cost method to improve fibre quality validation.Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R2: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R2 = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.

Was Banfield right? New insights from a nationwide laboratory experiment.

Since the pioneering study by Banfield, the North‐South gap in Italian social capital has been considered by international scholars as an example of how cultural diversity within a country can generate different developmental outcomes. Most studies, however, suffer from limited external validity and measurement error. This paper exploits a new and representative online lab‐experiment to assess social‐capital patterns in Italy. Unlike previous experiments, we do not inform participants about the geographic origins of their counterparts. This feature allows us to assess the North‐South gap in universal, as opposed to parochial, behavior. Results suggest that Southerners and Northerners do not systematically differ in generalized prosocial preferences. Only trustworthiness is higher among. Northerners, while they are statistically similar to Southerners in many other economic preferences such as cooperation, trust, expected trustworthiness, altruism, and risk tolerance. We also show that the gap in trustworthiness stems from the lower reciprocity of Southerners in response to large transfers, and it is characterized by the intergenerational transmission of norms. Possible policy implications are discussed.

Comprehensive Evaluation of the GF-4 Satellite Image Quality from 2015 to 2020

GaoFen-4(GF-4) is the highest spatial resolution Earth observation satellite operating in geosynchronous orbit. Its fixed Earth observation location, rapid responsiveness, and wide observation range make it popular in disaster and emergency monitoring. To evaluate the GF-4 image quality in detail on a long-term basis, this study analyzes the image quality after the commissioning phase by focusing on ground sample distance (GSD) and geometric and radiometric quality. The theoretical calculation, geometric and radiometric measurements, and on-site experiments results show that (1) the GSD of the GF-4 image is ~50 m at the nadir point and increases gradually with the distance away from the nadir point, (2) most external geometric errors are within the design requirements of 4 km despite some exceeding the limit, and the internal geometric errors are tested within 1 pixel, and (3) image sharpness is generally stable but varies with the atmosphere condition and imaging time, and the radiometric response gradually degrades at the rate of less than 5.5% per year.