Internal Model Research Articles

Schema-driven prediction effects on episodic memory across the lifespan.

The predictive processing framework posits that one of the main functions of the brain is to anticipate the incoming information. Internal models facilitate interactions with the world by predicting future states against which actual evidence is compared. The difference between predicted and actual states, the prediction error (PE), signals novel information. However, how PE affects cognitive processing downstream is not fully understood: one such aspect pertains to how PE influences episodic memories, and whether those effect on memory differ across the lifespan. We examine the relationship between PE and episodic memory in children, young and older adults. We use a novel paradigm whereby rich visual narratives are used to build action schemas that enable probing different mnemonic aspects. To create different levels of PE, we manipulate the story endings to be either expected, neutral or unexpected with respect to the unfolded action. We show that (i) expected endings are better encoded than neutral endings and (ii) unexpected endings improve the encoding of mismatching events and other aspects of the narrative. These effects are differentially modulated across the lifespan with PE-driven encoding being more prominent in children and young adults and with schema integration playing a larger role on memory encoding in older adults. These results highlight the role of predictions by enriching past experiences and informing future anticipations.This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Cortical activity during online motor control in children with and without developmental coordination disorder: a cross-sectional functional near-infrared spectroscopy study

BackgroundChildren with developmental coordination disorder (DCD) have impaired online motor control. Researchers posit that this impairment could be due to a deficit in utilizing the internal model control process. However, there is little neurological evidence to support this view because few neuroimaging studies have focused specifically on tasks involving online motor control. Therefore, the aim of this study was to investigate the differences in cortical hemodynamic activity during an online movement adjustment task between children with and without DCD.MethodsTwenty children with DCD (mean age: 9.88 ± 1.67 years; gender: 14M/6F) and twenty age-and-gender matched children with typical development (TD) (mean age: 9.87 ± 1.59 years; gender: 14M/6F) were recruited via convenience sampling. Participants performed a double-step reaching task under two conditions (with and without online adjustment of reaching). Cortical hemodynamic activity during task in ten regions of interest, including bilateral primary somatosensory cortex, primary motor cortex, premotor cortex, superior parietal cortex, and inferior parietal cortex was recorded using functional near-infrared spectroscopy. In the analyses, change in oxyhemoglobin (ΔHbO) concentration was used to characterize hemodynamic response. Two-way analyses of variance were conducted for each region of interest to compare hemodynamic responses between groups and conditions. Additionally, Pearson’s r correlations between hemodynamic response and task performance were performed.ResultsOutcome showed that children with DCD required significantly more time to correct their reaching movements compared to the control group (t = 3.948, P < 0.001). Furthermore, children with DCD have a significantly lower ΔHbO change in the left superior parietal cortex during movement correction, compared to children with TD (F = 4.482, P = 0.041). Additionally, a significant negative correlation (r = − 0.598, P < 0.001) was observed between the difference in movement time of reaching and the difference in ΔHbO between conditions in the left superior parietal cortex.ConclusionsThe findings of this study suggest that deficiencies in processing real-time sensory feedback, considering the function of the superior parietal cortex, might be related to the impaired online motor control observed in children with DCD. Interventions could target this issue to enhance their performance in online motor control.

Modelling Risk Dependencies in Insurance Using Survival Clayton Copula

Our aim in this paper is to show the use of survival Clayton copula as a suitable tool for modelling risk dependencies in insurance. A purpose-built simulation of an adequate upper tail dependence can be an important part of the aggregation of risks in an insurer’s internal models. The occurrence of extreme values of the aggregate random variable might have a very negative impact on the insurer when securing coverage of unexpected losses. The upper conditional quantile exceedance probability of the copula is a suitable indicator. In addition an analysis of its effect on the level of modelling of the risk scenario is available. This effect is measured using the Tail Value at Risk of the aggregate random variable. To simplify our description of the given principle for aggregating risks we will in this paper only consider the two-dimensional case. The programming language R was used to simulate the values of the joint distribution of the marginal random variables.

Dielectric response, non-Ohmic behaviors and humidity-sensing characteristics: Tin doping in sodium yttrium copper titanate ceramics

The impact of tin (Sn4+) doping on sodium yttrium copper titanate (Na0.5Y0.5Cu3Ti4O12) ceramics, prepared using a conventional mixed-oxide route, was thoroughly investigated in terms of microstructure, dielectric properties, electrical response, non-linear behaviors, and humidity-sensing characteristics. All sintered ceramics showed a dense micro-structure and a pure Na0.5Y0.5Cu3Ti4O12 phase without any secondary phases. The doped Na0.5Y0.5Cu3Ti4−xSnxO12 ceramics, with x = 0.05, demonstrated excellent dielectric properties, including a low dielectric loss tangent (∼0.032) and a giant dielectric permittivity (∼1.8 × 104) across a wide temperature range. Sn4+ doping improved the non-linear behaviors at 25 °C in Na0.5Y0.5Cu3Ti4O12, with a reduced dielectric loss tangent corresponding to an enhanced grain boundary (GB) response. The colossal dielectric response is attributed to the internal barrier layer capacitor model, which relates to the Schottky barrier height (ΦB) at the GBs. Notably, the ΦB values for the Na0.5Y0.5Cu3Ti4O12 ceramic increased with Sn4+ ion doping. Additionally, an in-depth study of the humidity-sensing properties of the Sn4+-doped Na0.5Y0.5Cu3Ti4O12 material revealed that the capacitance at 1 kHz increased with increasing relative humidity levels, from 30 % to 90 %, suggesting potential applications in humidity-sensing technologies.

Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high case fatality rate. Significant gaps remain in studies analyzing the clinical characteristics of fatal cases.MethodsFrom January 2017 to June 2023, 427 SFTS cases were included in this study. A total of 67 variables about their demographic, clinical, and laboratory data were collected. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) method was used to screen predictors from the cohort. Multivariate logistic regression was used to identify independent predictors and nomograms were developed. Calibration, decision curves and area under the curve (AUC) were used to assess model performance.ResultsThe multivariate logistic regression analysis screened out the four most significant factors, including age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452–4.360), elevated serum PT (p < 0.001, OR = 1.383, 95% CI 1.143–1.673), high viral load (p < 0. 001, OR = 1.496, 95% CI 1.290–1.735) and high level of serum urea (> 8.0 μmol/L) (p < 0.001, OR = 4.433, 95% CI 1.888–10.409). The AUC of the nomogram based on these four factors was 0.813 (95% CI, 0.758–0.868). The bootstrap resampling internal validation model performed well, and decision curve analysis indicated a high net benefit.ConclusionsThe nomogram based on age, elevated PT, high serum urea level, and high viral load can be used to help early identification of SFTS patients at risk of fatality.

Language experience shapes predictive coding of rhythmic sound sequences.

Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language's word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker's linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term 'duration' prior. These findings show that experience with a given language can shape a fundamental aspect of human perception - the neural processing of rhythmic sounds - and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.

Pragmatic Nature of Identity as a Linguacultural Category

The article focuses on the phenomenon of identity from the pragmatic point of view. The process of communication is proved to reveal and form the subject’s affiliation with a particular community. Identity is viewed as the result of the process of communication, which consists in searching for an answer to the question of who I am. It is objectified in a dialogue that generates a situation of significance for the subjects of culture involved in it, thus actualizing their integral and differential attributes, which are taken into account in the course of identification. The study shows that two types of communication are used in the process of identity formation: external, according to the I – ONE model, and internal, according to the I – I model. The realization of the first model consists in the fact that a linguacultural community delivers information about itself to other cultural subjects by means of emblems and brands. This method of information transfer is connected with the formation of an opinion about oneself in the minds of other subjects of culture. The internal model of communication relates to the transmission of information to oneself. This type of communication may be considered in diachronic and synchronous aspects. Major means used in auto-communication are etalons, symbols, measures, amulets. Linguaculture, while implementing both models, can generate or borrow new principles, values, symbolary for their expression, which results in transformation of identity. The article determines the significant aspects in describing the pragmatic essence of identity, including the subjects of culture perceptions of oneself and the other, the content of goal-setting, the cultural code, and the message. The cultural code is embodied in the symbolary of identity and the texts in which it is realized. It reflects the image and way of thinking, self-perception of the subjects of culture. Natural language is proved to be the main means of identity objectification.

A Novel Simplified PID Controller Tuning Method for Exact Maximum Sensitivity Specification

Many PID (Proportional plus Integral plus Derivative) controller tuning methods are proposed in the control literature based on different closed-loop specifications. Also, tuning techniques, based on the maximum sensitivity specification, have been proposed in the literature. In this paper, a novel PID controller design method, based on maximum sensitivity, has been proposed. In this work, the PID controller is viewed as a gain and dynamic part and tuned separately. The PID controller dynamic part is tuned using one of the existing popular tuning methods. The tuned dynamic part is cascaded with the process. After that, the PID controller gain is tuned for the maximum sensitivity specification. The maximum sensitivity versus controller gain graph can be generated. The user can choose the controller gain for the desired maximum sensitivity specification. The popular Ziegler & Nichols and IMC (Internal Model Control) methods tune the dynamic part of the system in this work. These proposed techniques can achieve the desired maximum sensitivity by tuning the controller gain in the subsequent stage. Many illustrative instances are considered to display the applicability and the proposed technique’s simplicity.

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations.

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals' accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6.

Adaptive Control of Ships’ Oil-Fired Boilers Using Flame Image-Based IMC-PID and Deep Reinforcement Learning

The control system of oil-fired boiler units on ships plays a crucial role in reducing the emissions of atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxides (SO2), and carbon dioxide (CO2). Traditional control methods using conventional measurement sensors face limitations in real-time control due to response delays, which has led to the growing interest in combustion control methods using flame images. To ensure the precision of such combustion control systems, the system model must be thoroughly considered during controller design. However, finding the optimal tuning point is challenging due to the changes in the system model and nonlinearity caused by environmental variations. This study proposes a controller that integrates an internal model control (IMC)-based PID controller with the deep deterministic policy gradient (DDPG) algorithm of deep reinforcement learning to enhance the adaptability of image-based combustion control systems to environmental changes. The proposed controller adjusts the PID parameter values in real-time through the learning of the determination constant lambda (λ) of the IMC internal model. This approach reduces computational resources by shrinking the learning dimensions of the DDPG agent and limits transient responses through constrained learning of control parameters. Experimental results show that the proposed controller exhibited rapid adaptive performance in the learning process for the target oxygen concentration, achieving a reward value of −0.05 within just 105 episodes. Furthermore, when compared to traditional PID tuning methods, the proposed controller demonstrated superior performance, achieving a target value error of 0.0032 and a low overshoot range of 0.0498 to 0.0631, providing the fastest response speed and minimal oscillation. Additionally, experiments conducted on an actual operating ship verified the practical feasibility of this system, highlighting its potential for real-time control and pollutant reduction in marine applications.

Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design.

Prediction of Wind Turbine Gearbox Oil Temperature Based on Stochastic Differential Equation Modeling

Aiming at the problem of high failure rate and inconvenient maintenance of wind turbine gearboxes, this paper establishes a stochastic differential equation model that can be used to fit the change of gearbox oil temperature and adopts an iterative computational method and Markov-based modified optimization to fit the prediction sequence in order to realize the accurate prediction of gearbox oil temperature. The model divides the oil temperature change of the gearbox into two parts, internal aging and external random perturbation, adopts the approximation theorem to establish the internal aging model, and uses Brownian motion to simulate the external random perturbation. The model parameters were calculated by the Newton–Raphson iterative method based on the gearbox oil temperature monitoring data. Iterative calculations and Markov-based corrections were performed on the model prediction data. The gearbox oil temperature variations were simulated in MATLAB, and the fitting and testing errors were calculated before and after the iterations. By comparing the fitting and testing errors with the ordinary differential equations and the stochastic differential equations before iteration, the iterated model can better reflect the gear oil temperature trend and predict the oil temperature at a specific time. The accuracy of the iterated model in terms of fitting and prediction is important for the development of preventive maintenance.

Привязанность как необходимость в защите и безопасности

&lt;p&gt;The work aims to clarify the concept of &amp;ldquo;attachment&amp;rdquo;. The article, based on materials from foreign sources, examines the development of the main provisions of attachment theory. Attachment is based on an evolutionary need for survival. Depending on the connection with security, the following types of attachment are distinguished: secure attachment, avoidant attachment, ambivalent attachment, and disorganized attachment. For a child, attachment is important for safety and survival. However, attachment style can undergo changes and adaptation to varying degrees in light of different life experiences. Attachment theory suggests that the way the bond develops between a child and their mother early in life will influence their sense of security and social, emotional, and mental development later in life. Research on this topic shows that the initial relationships formed at the very beginning of life give rise to the attachment style characteristic of a person in adulthood, which, as a rule, underlies the degree to which a person feels safe and emotionally secure in different relationships throughout their life. The quality of attachment helps shape a child's internal model of behavior regarding the social world, predicting how well children function in their relationships with teachers and friends, as well as their curiosity and motivation to solve problems. Conclusion: For humans, attachment is important for security and survival. At the same time, attachment style may undergo changes and adaptation to varying degrees in light of different life experiences.&lt;/p&gt;

New results on the gamma-ray burst variability--luminosity relation

At the dawn of the gamma--ray burst (GRB) afterglow era, a Cepheid-like correlation was discovered between the time variability $V$ and the isotropic-equivalent peak luminosity $L_ iso $ of the prompt emission of about a dozen long GRBs with measured redshift available at that time. Soon afterwards, the correlation was confirmed in a sample of about 30 GRBs, even though it was affected by significant scatter. Unlike the minimum variability timescale (MVT), $V$ measures the relative power of short-to-intermediate timescales. We aim to test the correlation using about 200 long GRBs with spectroscopically measured redshift, detected by Swift Fermi and Konus/ WIND for which both observables can be accurately estimated. The variability for all selected GRBs was calculated according to the original definition using the 64 ms background-subtracted light curves of Swift /BAT ( Fermi /GBM) in the 15--150 (8--900) keV energy passband. Peak luminosities were either taken from the literature or derived from modelling broad-band spectra acquired with either Konus/ WIND or Fermi /GBM. The statistical significance of the correlation has weakened to $ 2$&lt;!PCT!&gt;, mostly due to the appearance of a number of smooth and luminous GRBs that are characterised by a relatively small $V$. At odds with most long GRBs, three out of four long-duration merger candidates have high $V$ and low iso The luminosity is more tightly connected with shortest timescales measured by MVT than the short to intermediate timescales measured by $V$. We discuss the implications for internal dissipation models and the role of the $e^ pm $ photosphere. We identified a few smooth GRBs with a single broad pulse and low $V$ that might have an external shock origin, in contrast with most GRBs. The combination of high variability $(V 0.1)$, low luminosity iso $ erg s$^ $, and short MVT ($ 0.1$ s) could be a good indicator for a compact binary merger origin.

Integrated high-precision real scene 3D modeling of karst cave landscape based on laser scanning and photogrammetry

In recent years, the application of real scene 3D technology has become widespread in urban planning and cultural heritage protection. However, there has been relatively little attention paid to the construction of real scene 3D models for special natural landscapes such as caves. Given the global distribution of karst topography and the large number of naturally developed caves with diverse types, unique landscape styles, and significant scientific value, this paper enriches the research in this field. By combining ground-based and aerial remote sensing techniques, and based on 3D laser scanning and photogrammetry, we have successfully constructed a real scene 3D model of the internal structure of a karst cave with a precision better than 4 cm. Utilizing Unmanned Aerial Vehicle (UAV) oblique photography, we established a real scene 3D model of the external karst landform with a precision better than 2 cm. We also integrated the internal and external 3D models of the cave, developing a new, complete, and high-precision method for constructing real scene 3D models of karst cave landscapes. Furthermore, we proposed a method for texture reproduction in the dark environment inside the caves, enhancing the reproduction and visual appeal of the real interior. The establishment of high-precision real scene 3D models can not only serve as an effective tool for scientific research on caves but also, as replicas of the real world, play a crucial role in public dissemination and education, thereby enhancing public understanding of cave geological landscapes.

Model of Internalization of Religious Moderation Values ​in Salafiyah Boarding Schools

This study aims to analyze the process of internalizing religious moderation values at the Salafiyah Islamic Boarding School, Rubath Al Muhibbien, Palembang. Religious moderation is crucial for maintaining stability and avoiding excessive religious interpretations that may violate humanitarian values, social agreements, and public order. Salafiyah Islamic boarding schools were chosen as the focus due to their pivotal role in fostering a moderate understanding of Islam, particularly in instilling these values in their students. The research utilized a qualitative phenomenological approach. Key informants included school leaders, administrators, teachers, students, alumni, and the general community. Activities observed encompassed the internalization process of religious moderation values, with classical Islamic texts (kitab kuning) serving as supporting documents: data collection methods involved observations, in-depth interviews, and written and oral documentation. The Miles and Huberman data analysis method, known for its three primary activities, data reduction, data display, and conclusion drawing or verification was employed. The findings reveal a model of internalizing religious moderation values at the Salafiyah Islamic Boarding School, consisting of three stages: value transformation, transaction, and internalization. The first stage involves value transformation through conceptual, institutional, operational, and architectural dimensions. The second stage consists of implementing advisory strategies and customary practices to instil values among students. The third stage incorporates role modelling, habituation, exemplification, advisory roles, promises, threats, and disciplinary measures. By focusing on one Islamic boarding school, the research results may not fully reflect the internalizing religious moderation values ​​in other Islamic boarding schools with different characteristics, cultures, and teaching methods. The originality lies in the model of internalizing religious moderation values ​​that have been developed. This holistic approach combines unique conceptual, institutional, operational, and architectural aspects and strategies relevant to the culture and traditions of Islamic boarding school education.

Drone Swarm Robust Cooperative Formation Pursuit through Relative Positioning in a Location Denial Environment

This paper considers the pursuit problem of a moving target by a swarm of drones through a flexible-configuration formation. The drones are modeled by second-order systems subject to uncertain damping ratios, whereas the moving target follows a polynomial-type trajectory whose coefficient vectors are fully unknown. Due to location denial, drones cannot obtain their absolute positions, but they can obtain their positions relative to other neighboring drones and the target. To achieve a robust formation pursuit, a robust cooperative control protocol is synthesized, which comprises three key components, namely, the pseudo drone position estimator, the pseudo target position estimator, and the local internal model control (IMC) law. The pseudo drone position estimator and the pseudo target position estimator aim to recover for each drone the position of itself and the target, respectively, but are subject to some common unknown constant bias in a distributed manner. By subtracting the pseudo target position from the pseudo drone position, each drone can acquire its position relative to the target, which facilitates the design of a local IMC law to fulfill formation pursuit in the presence of system parametric uncertainties. Both pure numerical simulation and hardware-in-the-loop (HIL) simulation are performed to verify the effectiveness of the proposed control protocol.

Distributed modelling of snow and ice melt in the Naltar Catchment, Upper Indus basin

Energy balance distributed modelling in High Mountain Asia (HMA) is important to examine glaciological and hydrological processes and assess changes in streamflow in the current and future climate. In this study, the Physically based Distributed Snow Land and Ice Model (PDSLIM) using detailed observed meteorological data at hourly scale is employed to simulate the hydrological response of the Naltar catchment, 242.62 km2 in size, (in the Karakoram region in Pakistan to simulate its glaciers’ mass balance as well as daily runoff. The results exhibited overall satisfactory performance in terms of coefficient of determination (R2 = 0.96) and Nash-Sutcliffe Efficiency (NSE=0.95) modelled against satellite-based snow cover areas, for internal model verification, in eight years. The results of runoff simulations compared for external model verification, with observed daily discharge resulted in NSE 0.90 and 0.89 for calibration and validation period respectively. Flow composition analysis revealed that the streamflow regime of Naltar catchment is composed to 40 % by glacier runoff, 42 % by sub-surface runoff and 18 % by surface runoff. The eight year mean value of net mass balance exhibited a slightly negative mass balance (−0.810 ± 0.31 m w.e. a-1) less pronounced than that observed globally in several continental glaciers distinct from the Greenland and Antarctic ice sheets in the current climate. It seems that the ‘Karakoram anomaly’, i.e. the balanced to slightly positive glacier budgets observed in the region in the recent decades, a unique dynamics worldwide, has a moderate impact in the central Karakoram. Overall, the distributed energy-balance model PDSLIM, so far tested in the Alps, results to be a suitable tool to estimate energy and mass balance in the glacierized catchments of Karakoram and Himalaya and to better understand snow and ice melt runoff dynamics and floods in highly complex and glacierized mountain basins in the current and, in our research perspective, in the future climate.

Physical model and Bayesian theory based hydraulic component fault diagnosis method

Hydraulic components are integral parts of hydraulic systems, and their failures directly impact the normal operation of hydraulic systems. Due to the dynamic variation of working pressure in hydraulic components with load, it is challenging to diagnose faults of hydraulic components under dynamic pressure conditions using existing methods. This paper focuses on the research of internal leakage faults in hydraulic directional control valves and proposes a fault diagnosis method based on physical model and Bayesian theory for hydraulic components. Firstly, this method investigates the working fault mechanism of hydraulic directional control valves and constructs an internal leakage model. Then, correlation analysis is introduced to select fault feature signals, and principal component analysis is adopted to construct internal leakage features. Finally, based on Bayesian model, Markov chain Monte Carlo sampling, and internal leakage features, the parameters of the internal leakage model are estimated to achieve fault diagnosis of internal leakage in hydraulic directional control valves. Experimental results demonstrate that compared to several traditional fault diagnosis methods, the proposed method exhibits higher adaptability and accuracy under dynamic pressure conditions. This method, based on the internal leakage model and Bayesian theory, realizes fault diagnosis of hydraulic components under dynamic pressure conditions, avoiding the shortcomings of deep learning methods that rely on a large amount of fault data to train fault models.

Data-Driven Dynamic Internal Model Control.

A data-driven dynamic internal model control (D3IMC) scheme is proposed for unknown nonlinear nonaffine systems bypassing modeling steps. Different from the traditional internal model constructed by either a first-principle or an identified model, a dynamic internal model (DIM) is developed in this work using I/O data where a compact form dynamic linearization approach is introduced for addressing the nonlinearity and nonaffine structure. Then, the D3IMC is proposed with both a nominal control algorithm and an uncertainty compensation control algorithm. The former can quickly respond to the feedback errors and the latter can compensate the model-plant mismatch and external disturbances. Meanwhile, the adaptive parameter updating law in the proposed D3IMC method inherits the robustness against uncertainties. A nominal D3IMC is also designed without including the compensator when there is no exogenous disturbance since the adaptive mechanism can handle system uncertainty. Further, the results are extended and a full-form dynamic linearization-based D3IMC is developed to address control of nonlinear systems with more complex dynamics. All the proposed D3IMC methods are data-driven without need of an explicit model, and thus they are significant extensions from the traditional model-based IMC. Simulation study verifies the results.