Position Signal Research Articles

A novel wafer defocus measurement method for spot-scanning imaging system using laser triangulation

Abstract For spot scanning dark-field scattering technology, the defocus caused by the change in wafer surface height decreases the defect detection rate and size measurement repeatability. The demand for accurately and rapidly measuring the wafer surface height online is becoming increasingly urgent. The defect detection system integrates scattering, reflection, film thickness, and topography defect detection channels. The integration of laser-triangulation-based defocus measurement into the bright-field optical path is proposed. The laser beam is directed onto the surface of the wafer, and the reflected light is transmitted through a lens onto the photosensitive surface of a four-quadrant detector. This detector captures both the strength and position of the reflected signal simultaneously. In this paper, a triangulation-modified model is established to obtain the height of each inspection spot on the wafer surface through the position signal of the reflected image. When the signal-to-noise ratio of the simulated reflection image position is 20 dB, the signal-to-noise ratio of the height measurement resolved by the triangulation-modified model is 41 dB, and the measurement error is less than 0.2 μm, indicating that the model has a noise suppression effect. The spot-scanning imaging system is established, and the height measurement results of the triangulation measurement model are verified using a laser interferometer. Within the travel range of 69 μm, the system has a measurement accuracy better than ±1 μm and a measurement repeatability better than ±0.2 μm, which meet the measurement requirements of the spot-scanning imaging system. The system is utilized for defect detection and defocus measurement of the wafer surface, along with the motion mechanism, to accurately and rapidly obtain the height distribution of the wafer surface.

Adaptive control of magnetic levitation system based on fuzzy inversion

A novel adaptive tracking controller for magnetic levitation systems (MLS) is developed. The controller is based on a special adaptive control scheme incorporating fuzzy model of electromagnetic acceleration enabling fast and accurate fuzzy inversion. The controller ensures accurate tracking of any smooth desired position signal, despite unknown MLS parameters. The closed-loop system stability, in the sense of uniform ultimate boundedness (UUB) of error trajectories, is proved using Lyapunov approach. The closed-loop system performance is investigated during numerical experiments. Finally the proposed controller is verified by successful implementation on a DSP board controlling a typical magnetic levitation ball system. Performed tests and experiments demonstrate that the proposed control technique is robust against discretization and unknown MLS model parameters, provides high tracking accuracy, is easily implementable, and simple to tune.

Mechanism model for effect of eccentricity on capacitive control rod position indicators

The continuous identification technology for position signals based on the capacitive method has a good application potential in the field of the control rods position measurement in nuclear reactors. The main source for error of the Capacitive Control Rod Position Indicators (CCRPIs) comes from the effect of eccentricity. The conformal mapping technique was used in this paper, and the geometry of the CCRPIs could be transformed. A model for revealing the mechanism of the effect of eccentricity was established, and it was tested and verified by the method of numerical calculation. The characteristics of the distribution of the sensitive field was clarified by the model. The results indicate that the maximum error of the model is smaller than 13 %, and the eccentric effect of the indicator can be described by the model.

First evolutionary insights into the human otolithic system

The human otolithic system (utricle and saccule), housed within the bony vestibule of the inner ear, establishes our sense of balance in conjunction with the semicircular canals. Yet, while the morphological evolution of the semicircular canals is actively explored, comparative morphological analyses of the otolithic system are lacking. This is regrettable because functional links with head orientation suggest the otolithic system could be used to track postural change throughout human evolution and across primates more broadly. In this context, we present the first analysis of the evolution of the human otolithic system within an anthropoid primate setting. Using the vestibule as a morphological proxy for the utricle and saccule, we compare humans to 13 other extant anthropoid species, and use phylogenetically-informed methods to find correlations with body size, endocranial flexion, and head-neck posture. Our results, obtained through micro-CT of 136 inner ears, reveal two major evolutionary transitions in hominoids, leading to distinctive vestibular morphology in humans, characterized by otolithic morphology resembling squirrel monkeys (possibly due to reversal), with a pronounced supraovalic fossa. Finally, we find a positional signal embedded in the anthropoid bony vestibule, providing the foundation to further explore the evolution of human head-neck posture using inner ear morphology.

A low-complexity solution for LMIs of hardly constrained nonlinear MIMO tracking control systems

This research is dedicated to the discrete-time tracking control of nonlinear multi-input multi-output (MIMO) Newtonian mechanics systems with an equal number of inputs and outputs, in the presence of input saturation and high-order dynamics of the actuation system. Firstly, the continuous-time model of the plant and actuation system has been transformed into the discrete-time domain using Adams–Bashforth and Adams–Moulton discretization techniques. In the next step, considering saturation boundaries, the modified position and velocity references are derived using the discrete-time reaching law-based control approach. These modified references are obtained through an optimization algorithm that calculates the closest achievable position and velocity trajectories with respect to the desired reference trajectory. Modifying desired trajectories in MIMO systems results in the formation of a system of linear matrix inequalities (LMIs). Solving such systems of inequalities requires the use of complex methods, and this difficulty increases with the increase in the system’s degrees of freedom (DOFs). The proposed strategy offers a more straightforward approach to solving these inequalities. Once the modified references are calculated, the corresponding position and velocity control signals can be obtained using the mentioned reaching law strategies. The final step of the proposed algorithm is to combine the resulting position and velocity control signals using a Gaussian-weighted averaging method to form the main control signal. A desirable performance for the control system can be achieved by appropriately adjusting the standard deviations of the Gaussian weighing functions. To evaluate the performance characteristics of the proposed method, it has been applied in the tracking control procedure of a nonlinear 3-DOF mechanical system. The numerical simulation results demonstrate the effectiveness of the method.

Development of a low energy loss micro-channel plate based position-sensitive detector for the Rare Radio-Isotope Ring

A position-sensitive micro-channel plate type detector has been developed for use in the Rare Radio-Isotope Ring at RIKEN, Japan. It uses secondary electrons emitted from a conversion foil and deflected at 90° to create a position signal. Design constraints included operation in a high-vacuum, a large area to cover the ring acceptance and low beam interaction to reduce energy loss. It must achieve a precision sufficient to measure the in-ring dispersion and perform position diagnostics for tuning the injection. Reducing the time-of-flight of secondary electrons was principal in improving resolution as it reduces their final gaussian spread. This was implemented by compacting the geometry of the detector and raising the acceleration potential. Electric field homogeneity was also improved by decreasing the electrostatic grid wire pitch from 2 mm to 1 mm. Offline tests with alpha sources and online tests with a 200 MeV/u 84Kr beam were conducted, reaching a final average position resolution of σ=1.3 mm. This is sufficient for conducting beam diagnostics in the storage ring.

Challenges and advances for huntingtin detection in cerebrospinal fluid: in support of relative quantification.

Huntington disease (HD) is a progressive and devastating neurodegenerative disease caused by expansion of a glutamine-coding CAG tract in the huntingtin (HTT) gene above a critical threshold of ~35 repeats resulting in expression of mutant HTT (mHTT). A promising treatment approach being tested in clinical trials is HTT lowering, which aims to reduce levels of the mHTT protein. Target engagement of these therapies in the brain are inferred using antibody-based assays to measure mHTT levels in the cerebrospinal fluid (CSF), which is frequently reported as absolute mHTT concentration based on a monomeric protein standard used to generate a standard curve. However, patient biofluids are a complex milieu of different mHTT protein species, suggesting that absolute quantitation is challenging, and a single, recombinant protein standard may not be sufficient to interpret assay signal as molar mHTT concentration. In this study, we used immunoprecipitation and flow cytometry (IP-FCM) to investigate different factors that influence mHTT detection assay signal. Our results show that HTT protein fragmentation, protein-protein interactions, affinity tag positioning, oligomerization and polyglutamine tract length affect assay signal intensity, indicating that absolute HTT quantitation in heterogeneous biological samples is not possible with current technologies using a single standard protein. We also explore the binding specificity of the MW1 anti-polyglutamine antibody, commonly used in these assays as a mHTT-selective reagent and demonstrate that mHTT binding is preferred but not specific. Furthermore, we find that MW1 depletion is not only incomplete, leaving residual mHTT, but also non-specific, resulting in pull down of some wildtype HTT protein. Based on these observations, we recommend that mHTT detection assays report only relative mHTT quantitation using normalized arbitrary units of assay signal intensity, rather than molar concentrations, in the assessment of central nervous system HTT lowering in ongoing clinical and preclinical studies, and that MW1-depletion not be used a method for quantifying wildtype HTT protein.

Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road

Under the intelligent transportation system, the use of autonomous vehicles can ease traffic jam, and reduce fuel consumption and emissions. The effects of different data transmission delays on traffic jam, fuel consumption and emissions on curved road are studied by constructing an extended multi-vehicle curve following model considering different data transmission delays to describe the curve following behavior of multiple autonomous vehicles. Subsequently, the stability of the novel model is derived by using linear stability theory. And then, numerical simulation is executed. Finally, some influence factors including curvature radius, backward looking effect, position signal time delay, and velocity signal time delay are discussed. Numerical simulation results show that reducing curvature radius and increasing backward looking effect can reduce fuel consumption and emissions, decrease density fluctuations, thereby gradually eliminating traffic jam. In addition, as difference between position signal time delay and velocity signal time delay decreases, the improvement degree in fuel consumption and emissions is limited. Meanwhile, it also makes density fluctuation decreases gradually, enhances traffic stream stability, and alleviates traffic jam, which is keeping with theoretical analysis. The finding has theoretical implications for designing autonomous vehicle control algorithms to reduce the negative effects of data transmission delays in traffic flow.

Study on the detection of power line operation anomalies in the Hexi Corridor based on multi-scale filtering of BeiDou satellite signals

In the artificial intelligence detection of power line anomalies, the identification effect is determined by the positioning process of two-dimensional map, which is also a difficult point in this field Therefore, this paper proposes a method to detect the abnormal operation of power lines in Hexi Corridor based on multi-scale filtering of Beidou satellite signals. Multi-scale processing is carried out by wavelet transform to obtain multiple wavelet basis functions of different scales. On this basis, a two-dimensional time–frequency diagram is drawn according to the results of continuous wavelet transform. The adaptive multi-time frequency entropy method is selected to divide the two-dimensional time–frequency map into multiple layers and scales, and the adaptive multi-scale time–frequency entropy of the time–frequency plane is calculated. After smoothing the obtained power line position signal by S-G filtering method, the power line position signal of Hexi Corridor is obtained by GPS/ Beidou dual-mode satellite positioning system, and the positioning signal is optimized by using the positioning mechanism of multiple receiving stations. Realize the abnormal operation detection of power lines in Hexi Corridor. The experimental results show that this method can obtain more accurate positioning signals, and the geometric accuracy factor GDOP fluctuates in the range of 0 ∼ 2. The unsmooth signal can be effectively removed; The maximum error result of abnormal position detection is 1.22kn；; At the same time, the abnormal displacement section of the line can be determined.

ORASIS-MAE Harnesses the Potential of Self-Learning from Partially Annotated Clinical Eye Movement Records

Self-supervised learning (SSL) has gained significant attention in the past decade for its capacity to utilize non-annotated datasets to learn meaningful data representations. In the medical domain, the challenge of constructing large annotated datasets presents a significant limitation, rendering SSL an ideal approach to address this constraint. In this study, we introduce a novel pretext task tailored to stimulus-driven eye movement data, along with a denoising task to improve the robustness against simulated eye tracking failures. Our proposed task aims to capture both the characteristics of the pilot (brain) and the motor (eye) by learning to reconstruct the eye movement position signal using up to 12.5% of the unmasked eye movement signal patches, along with the entire REMOBI target signal. Thus, the encoder learns a high-dimensional representation using a multivariate time series of length 8192 points, corresponding to approximately 40 s. We evaluate the learned representation on screening eight distinct groups of pathologies, including dyslexia, reading disorder, and attention deficit disorder, across four datasets of varying complexity and size. Furthermore, we explore various head architecture designs along with different transfer learning methods, demonstrating promising results with improvements of up to approximately 15%, leading to an overall macro F1 score of 61% and 61.5% on the Saccade and the Vergence datasets, respectively. Notably, our method achieves macro F1 scores of 64.7%, 66.1%, and 61.1% for screening dyslexia, reading disorder, and attention deficit disorder, respectively, on clinical data. These findings underscore the potential of self-learning algorithms in pathology screening, particularly in domains involving complex data such as stimulus-driven eye movement analysis.

Precipitable water vapor retrieval for rainfall forecasting using BDS-3 PPP-B2b signal in the coastal region of China

Abstract Currently, rainfall cannot be accurately forecast because of poor network communication at the ocean. The advantage of the BeiDou Global Navigation Satellite System (BDS-3) precise point positioning (PPP-B2b) signal, which does not rely on network communication to receive data, is that it can provide precipitable water vapor (PWV) retrieval application services for the open seas in eastern China, where the communication system presents difficulties. In this study, data from stations in the coastal region of China were used to establish a rainfall forecasting method for monitoring extreme weather on the sea. First, the service performance of PPP-B2b was explored. Then, based on 17 Chinese coastal stations, the PWV accuracy was evaluated. Finally, based on an analysis of the relationship between PWV and actual rainfall, a threshold rainfall forecasting method based on a sliding window was constructed. The experimental results show that the PWV accuracy varies slightly depending on the geographic location, in which the mean absolute error in the North Sea region is the smallest (2.1 mm), that of the South China Sea region is the largest (2.60 mm), and that of the East China Sea region is in the middle (2.48 mm). The optimal predictors of the constructed 12 h sliding-window threshold rainfall prediction method are a PWV maximum of 49 mm, PWV increase of 5 mm, and PWV increase rate of 1.2 mm h−1. The prediction results can reach a critical success index value of more than 45%, indicating high prediction accuracy and applicability to the coastal region of China during the same period.

Adaptive event-triggered security control for networked teleoperation systems.

An adaptive event-triggered security control method is proposed for networked robotic teleoperation systems subject to time-varying delays and false data injection (FDI) attacks. An event-triggered scheme is designed via the position and velocity signals of the master and slave robots, where the triggering thresholds can change adaptively with the system states. The position and velocity triggered signals and the feedback error between the operator and the environment forces are utilized to detect whether the transmitted signals suffered from FDI attacks. Then, a switching controller is designed, which is able to adopt the corresponding control strategy according to the corresponding detection result. The stability of the system and the convergence of the tracking errors are proved through Lyapunov functions, and the proposed method is validated through practical experiments. The proposed method is able to adaptively adjust the triggering frequency and effectively reduce the transmitted data, thus saving the network resources. Meanwhile, it can ensure the stability of networked teleoperation systems under time-varying delays and FDI attacks, as well as the position and force tracking performance.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Chlorophyll content estimation and ripeness detection in tomato fruit based on NDVI from dual wavelength LiDAR point cloud data

Tomato (Solanum lycopersicum L.) is a climacteric fruit exhibiting the ripening pattern with change in colour from green to red. Normalized difference vegetation index (NDVI) derived from spectral-optical analysis has been previously employed in ripeness analysis of various fruits based on its strong correlation to ripening-related chlorophyll content. In this study, light detection and ranging (LiDAR) laser scanner was applied to gain 3D tomato point clouds aimed at estimating the spatially resolved chlorophyll content and ripeness class of tomato fruit. Freshly harvested tomatoes, capturing six ripeness classes (mature green, breaker, turning, pink, light red, and red) according to USDA colour standard, were analysed using a linear conveyor mounted sensor system consisting of two LiDAR units measuring position and return signal strength intensity at 660 nm and 905 nm. Fruit point clouds were pre-processed including geometric correction considering the curvature of each tomato sample, removing highest intensity areas at the specular highlighted spots, calibration of intensity values using standard black and white colour coated boards, and calibration on attenuation of the opaque samples. Particularly, for gaining the attenuation coefficient (μ), 9 opaque synthetic models with known attenuation were used. Obtained μ660 and μ905 were merged to gain NDVILiDAR. Chemically analysed chlorophyll content of tomato samples was correlated to μ660 and NDVILiDAR, whereas low correlation appeared for μ905 with a coefficient of determination (R2) of 0.58, 0.60, and 0.06, respectively. Regression models were used to estimate the total tomato chlorophyll content and the lowest root mean square error (RMSE) was found for μ660 (RMSE = 4.97 mg (100 g dry mass)−1) followed by NDVILiDAR (RMSE = 5.22 mg (100 g dry mass)−1), and μ905 (RMSE = 53.50 mg (100 g dry mass)−1). Histograms of NDVILiDAR and μ660 were extracted from each tomato point cloud and utilized to construct PLS-DA model for tomato ripening class prediction considering the spatial chlorophyll distribution. The overall accuracies for NDVILiDAR and μ660 were 70 % and 68 %, respectively, in leave-one-out cross-validation for visually defined colour classes. The LiDAR-based approach could support selective detection of ripe fruit in robots for harvesting and postharvest handling.

Plasmodesmata dynamics in bryophyte model organisms: secondary formation and developmental modifications of structure and function

Main conclusionDeveloping bryophytes differentially modify their plasmodesmata structure and function. Secondary plasmodesmata formation via twinning appears to be an ancestral trait. Plasmodesmata networks in hornwort sporophyte meristems resemble those of angiosperms.All land-plant taxa use plasmodesmata (PD) cell connections for symplasmic communication. In angiosperm development, PD networks undergo an extensive remodeling by structural and functional PD modifications, and by postcytokinetic formation of additional secondary PD (secPD). Since comparable information on PD dynamics is scarce for the embryophyte sister groups, we investigated maturating tissues of Anthoceros agrestis (hornwort), Physcomitrium patens (moss), and Marchantia polymorpha (liverwort). As in angiosperms, quantitative electron microscopy revealed secPD formation via twinning in gametophytes of all model bryophytes, which gives rise to laterally adjacent PD pairs or to complex branched PD. This finding suggests that PD twinning is an ancient evolutionary mechanism to adjust PD numbers during wall expansion. Moreover, all bryophyte gametophytes modify their existing PD via taxon-specific strategies resembling those of angiosperms. Development of type II-like PD morphotypes with enlarged diameters or formation of pit pairs might be required to maintain PD transport rates during wall thickening. Similar to angiosperm leaves, fluorescence redistribution after photobleaching revealed a considerable reduction of the PD permeability in maturating P. patens phyllids. In contrast to previous reports on monoplex meristems of bryophyte gametophytes with single initials, we observed targeted secPD formation in the multi-initial basal meristems of A. agrestis sporophytes. Their PD networks share typical features of multi-initial angiosperm meristems, which may hint at a putative homologous origin. We also discuss that monoplex and multi-initial meristems may require distinct types of PD networks, with or without secPD formation, to control maintenance of initial identity and positional signaling.

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

Design of a Wind Tunnel Nozzle Section Trolley Control System Based on Backstepping Method

Abstract To accommodate different types of supersonic wind tunnel tests, frequent replacements of nozzle sections and test sections are required, involving multiple complex technical processes. To optimize operational procedures and improve efficiency, a control system for the wind tunnel nozzle section trolley based on the backstepping method was designed. The system uses radio frequency identification (RFID) signals for station positioning and intelligently adjusts the speed and direction of the control motor, thus driving the trolley to move back and forth automatically. Through the simulation of the motor motion control model, the system effectively reduces random disturbances in the motion system caused by load changes, achieves precise positioning of wind tunnel plugins and nozzle assemblies, enhances system operating accuracy, reduces production cycle times, and realizes a one-button positioning function for all components, playing a significant role in improving wind tunnel test efficiency and operational quality.

Control and recalibration of path integration in place cells using optic flow.

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

Research on Foot Contact State Detection Technology of Wheel-Legged Robot.

The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate foot-end contact signals can provide robust support for robot motion control and decision-making processes. The shape and uncertain rotation of the wheel-legged robot foot end represent a significant challenge to sensing the robot foot-end contact state, which current foot-end sensing schemes cannot solve. This paper presents a sensing method for the tire stress field of wheel-legged robots. A finite element analysis was conducted to study the deformation characteristics of the foot-end tire under force. Based on this analysis, a heuristic contact position estimator was designed that utilizes symmetrical deformation characteristics. Strain sensors, arranged in an array, extract the deformation information on the inner surface of the tire at a frequency of 200 Hz. The contact position estimator reduces the dimensionality of the data and fits the eigenvalues to the estimated contact position. Using support vector regression, the force estimator utilizes the estimated contact position and sensor signal to estimate the normal reaction force, designated as FZ. The sensing system is capable of detecting the contact position on the wheel circumference (with a root mean square error of 1.150°), as well as the normal force of 160 N on the Z axis (with a root mean square error of 6.04%). To validate the efficacy of the sensor detection method, a series of randomized and repeated experiments were conducted on a self-constructed test platform. This novel approach offers a promising avenue for perceiving contact states in wheel-legged robots.

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.