Abrupt Disturbances Research Articles

Successful Electroconvulsive Therapy in Aicardi-Goutières Syndrome Presenting Psychiatric Symptoms: An Unprecedented Clinical Case.

Aicardi-Goutières syndrome (AGS) is a rare genetic disorder that primarily affects the central nervous system and is characterized by severe intellectual and physical disabilities. Although AGS traditionally exhibits nonpsychiatric symptoms, our case challenges this norm by presenting an 18-year-old male with AGS who developed distinctive psychiatric manifestations that required hospital admission.The patient, diagnosed with spastic cerebral palsy and thrombotic vasculopathy, displayed abrupt behavioral disturbances, insomnia, and food aversion. Standard assessments revealed basal ganglia calcifications and chilblain-like lesions, and AGS was confirmed via genetic studies that showed a mutation in the SAMHD1 gene. Despite initial treatment with aripiprazole and diazepam, psychiatric symptoms persisted. This led to the initiation of electroconvulsive therapy (ECT) with substantial success, marking its first documented use in AGS.In conclusion, this unique case broadens the comprehension of AGS, introducing psychiatric symptoms and pioneering the successful application of ECT. The intricate interplay of neurovascular involvement, genetic nuances, and innovative treatments underscores the complexity of AGS, encouraging further exploration of its diverse clinical spectrum and evolving therapeutic strategies.

Design of a Switching Strategy for Output Voltage Tracking Control in a DC-DC Buck Power Converter

This work proposes the design of a commutation function to solve the output voltage trajectory tracking problem in the DC-DC Buck power electronic converter. Through a Lyapunov-type analysis, sufficient conditions are established, taking into account the discontinuous model, to ensure asymptotic convergence to the desired trajectories. Based on this analysis, a state-dependent switching function was designed to guarantee the closed-loop stability of the tracking error. To validate the control performance, circuit numerical simulations were carried out under abrupt disturbances in the source and load of the converter. The results demonstrate that the voltage tracking at the output of the converter is satisfactorily achieved.

Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.

An optimal feedforward circuit with null audio susceptibility in feedback‐controlled buck converter

AbstractThe ability to manage abrupt disturbances in input supply voltage and sudden fluctuations in load represents a fundamental requirement for DC‐DC buck converters. While feedback control systems typically address such transients, certain applications necessitate even faster transient responses. This paper introduces an input voltage feedforward scheme (IVFS) integrated with voltage mode feedback controllers to enhance closed‐loop performance. Relevant transfer functions resulting from the inclusion of the feedforward circuit are derived, and an optimal condition for audio susceptibility nullification is identified. This scheme ensures an additional attenuation in magnitude of audio susceptibility, in contrast to the buck converter without feedforward. In the time domain, the incorporation of IVFS results in a 20% reduction in percentage overshoot for a 12‐8‐12 step in supply voltage. Additionally, for a 12‐15‐12 step, the percentage overshoot and settling time are completely eliminated. Various active and passive parasitics are included in modeling of the buck converter to aid in designing the feedforward and feedback controller. A Type‐III controller is employed as a feedback controller to assess the closed‐loop performance. A hardware prototype is built to demonstrate the efficacy of the proposed feedforward scheme. The implementation of the entire circuit is straightforward due to its requirement of only a few discrete components, namely, analog multiplier, and error amplifier.

Attack Detection and Security Control for UAVs Against Attacks on Desired Trajectory

The paper presents a security control scheme for unmanned aerial vehicles (UAVs) against desired trajectory attacks. The key components of the proposed scheme are the attack detector, attack estimator, and integral sliding mode security controller (ISMSC). We focus on malicious tampering of the desired trajectory sent by the ground control station (GCS) to the UAV by attackers. Firstly, we model attacks by analyzing the characteristics of desired trajectory attacks. Secondly, an integrated attack detection scheme based on an unknown input observer (UIO) and an interval observer is presented. Subsequently, a robust adaptive observer (RAO) is employed to compensate for the impact of attacks on the control system. Thirdly, an ISMSC with an attack compensation mechanism is established. Finally, simulation results are provided to verify the effectiveness of the proposed scheme. The proposed detection scheme can not only detect desired trajectory attacks but also distinguish them from abrupt unknown disturbances (AUDs). By utilizing ISMSC method, UAVs under desired trajectory attacks can fly safely. The proposed comprehensive framework of detection, estimation and compensation provides a theoretical basis for ensuring cyber security in UAVs.

Composite ADRC Speed Control Method Based on LTDRO Feedforward Compensation.

The performance of the extended state observer (ESO) in an Active Disturbance Rejection Control (ADRC) is limited by the operational load in stepper motor control, which has high real-time requirements and may cause delays. Additionally, the complexity of parameter tuning, especially in high-order systems, further limits the ESO's performance. This paper proposes a composite ADRC (LTDRO-ADRC) based on a load torque dimensionality reduction observer (LTDRO). Firstly, the LTDRO is designed to estimate abrupt load disturbances that are difficult to compensate for using the ESO. Secondly, the transfer function under the double-closed loop is deduced. Additionally, the LTDRO uses a magnetic encoder to gather the system state and calculate the load torque. It then outputs a compensating current feedforward to the current loop input. This method reduces the delay and complexity of the ESO, improving the response speed of the ADRC speed ring and the overall response of the system to load changes. Simulation and experimental results demonstrate that it significantly enhances dynamic control performance and steady-state errors. LTDRO-ADRC can stabilize the speed again within 49 ms and 17 ms, respectively, in the face of sudden load increase and sudden load removal. At the same time, in terms of steady-state error, compared with ADRC and CADRC, they have increased by 94% and 88%, respectively. In terms of zero-speed starting motors, the response speed is increased by 58% compared to a traditional ADRC.

Enhanced control in time-delay processes: Diagnostic, monitoring, and self-tuning strategies for the filtered smith predictor in response to model-plant mismatch and abrupt load disturbances

The Filtered Smith Predictor (FSP) is a practical structure in industrial control systems, especially those with dominant time delays. Despite its importance, a notable gap exists in developing tailored algorithms for the FSP that can effectively diagnose model degradation and the presence of abrupt disturbances. This research addresses the challenge of distinguishing between model-plant mismatch (MPM) and unmeasured disturbances (UD), which is crucial to maintaining control system performance and stability. We propose a novel algorithm that diagnoses, monitors, and self-tunes the FSP structure. By leveraging the sensitivity transfer function, our method ascertains nominal closed-loop performance without MPM or UD and meticulously analyzes the real effects of these factors. A dynamic time window and an operating range, used as tuning parameters, enable a precise assessment of the model’s predictive capacity and the detection of UD. Additionally, the algorithm incorporates a self-tuning mechanism for the FSP’s robustness filter, responding adaptively to identified discrepancies and ensuring enhanced system stability. Demonstrated through detailed simulated case studies and a real-life temperature control experiment, our methodology significantly improves the diagnosis and self-tuning capabilities of FSP structures. The results highlight a marked enhancement in system stability and performance, providing our approach’s practical value and effectiveness in real-world industrial applications.

Incorporating small‐scale disturbances in models of forest structure and aboveground biomass of tropical mountains

AbstractSeveral efforts have been made to model forest structure and estimate global aboveground biomass (AGB) magnitude and distribution. However, AGB models still show large uncertainties, especially over tropical mountains that are difficult to survey and understudied. Although forest disturbance and land use greatly influence forest structure and AGB accumulation, they are rarely considered in forest structure and AGB models. Including the effect of forest disturbance and land use could improve model performance. In this study, we used Landsat time series analysis and forest inventory data to assess and model the effect of small‐scale disturbance and land use on tropical montane forest structure and AGB. We explored two approaches: abrupt forest change detection, using the algorithm Breaks For Additive Seasonal Trend (BFAST), and gradual vegetation change by calculating the variation of remote sensing vegetation indices over time. We found that tropical montane landscapes are very dynamic and heterogenous. Out of the 284 forest plots analyzed, 27% had abrupt disturbances at least once between 1993 and 2014, and some of them showed abrupt disturbances several times. Forest plots with abrupt disturbances exhibit statistically significant lower basal area, tree height, and AGB than plots without abrupt disturbances. Using linear mixed‐effects models, we found that the number of breakpoints, annual Normalized Difference Vegetation Index SD, and Normalized Difference Water Index minimum values over time were the most informative variables for predicting forest structure, particularly basal area (adjusted R2 = 0.61). These variables are easy to calculate and could add significant power to forest structure and AGB models. In conclusion, incorporating small‐scale disturbance to model forest structure and AGB at regional scales is feasible through the identification of recent abrupt and gradual forest change using remote sensing time series. Including recent forest disturbance variables as predictors in forest structure models can greatly improve biomass predictability and mapping in dynamic landscapes.

Sensing and Suppressing Abrupt Disturbances for Robot Manipulators via Supervisory Disturbance Observer

Sensing and Suppressing Abrupt Disturbances for Robot Manipulators via Supervisory Disturbance Observer

Nonlinear Model Predictive Control using Neural ODE Replicas of Dynamic Simulators

We propose simulation-based nonlinear model predictive control as a first step towards autonomous decision-making for stable operation of large complex dynamical systems such as chemical plants. The effect of abrupt external disturbances should be quickly eliminated, taking into account such complex dynamic responses, to maintain stable production. In this paper, we propose a control system to eliminate these effects. The system uses engineering models, including dynamic simulators, based on chemical engineering knowledge. Dynamic simulators are generally not differentiable with respect to actions; however, differentiable models are advantageous for fast nonlinear optimization. To take advantage of both reliable dynamic simulators and differentiable models, we introduce neural ordinary differentiable equation models and clone the behaviour of simulators on them. The cloned differentiable neural replica model is then incorporated into a gradient-based nonlinear model predictive control. Evaluation of this method in a real methanol distillation plant confirms that it can significantly remove abrupt heavy rain disturbances compared to existing methods.

A Synthesized Sliding-Mode Control for Attitude Trajectory Tracking of Quadrotor UAV Systems

This article presents a practical synthesized sliding-mode control (SSMC) scheme and, then, applies it to quadrotor unmanned aerial vehicle (UAV) systems. A new adaptive law is developed to be suitable for real applications with small moment of inertia such as quadrotor UAV systems that become more sensitive to abrupt and external disturbances. Since it is designed to respond to both sliding variables and time-delayed estimation errors, it makes the switching gains provide remarkably fast convergence rate and high robustness while compensating for abrupt and external disturbances. Besides, the proposed SSMC scheme works together with the pole-placement control to enhance the system stability while yielding the desirable closed-loop dominant poles. Also, the proposed control scheme employs one-sample delayed information to cancel out the system uncertainties and disturbances so that it makes a simple and compact structure. It is shown that attitude trajectory tracking errors of the proposed SSMC scheme are guaranteed to be bounded by the bounded-input bounded-output stability theory. The practical effectiveness and feasibility of the proposed control scheme are illustrated in real implementation with a quadrotor UAV system, which are compared with those of the existing control schemes.

Composite-disturbance-observer-based backstepping control for three-phase inverters with multiple disturbances

This paper deals with the output voltage tracking control problem of three-phase inverters with multiple disturbances (including parametric perturbations of filter, abrupt disturbances caused by load switching, and harmonic disturbances brought by unbalanced loads and nonlinear loads). Some novel composite disturbance observers are designed to estimate the above multiple disturbances. Based on the disturbance estimates and backstepping control technique, an innovative feedforward-feedback composite tracking control scheme is proposed. The new control scheme has a good disturbance rejection ability and ensures good tracking performances. In the meantime, rigorous stability proof is presented. Simulation and experimental results are illustrated to verify the feasibility and effectiveness of the proposed control scheme.

UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series

Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting tree mortality and has been proven effective for the assessment of abrupt and large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing tree mortality events (e.g., due to drought) are more difficult to detect with satellite data – especially across regions and forest types. A common limitation for this is the availability of spatially explicit reference data. To address this issue, we propose an automated generation of reference data using uncrewed aerial vehicles (UAV) and deep learning-based pattern recognition. In this study, we used convolutional neural networks (CNN) to semantically segment crowns of standing dead trees from 176 UAV-based very high-resolution (<4 cm) RGB-orthomosaics that we acquired over six regions in Germany and Finland between 2017 and 2021. The local-level CNN-predictions were then extrapolated to landscape-level using Sentinel-1 (i.e., backscatter and interferometric coherence), Sentinel-2 time series, and long short term memory networks (LSTM) to predict the cover fraction of standing deadwood per Sentinel-pixel. The CNN-based segmentation of standing deadwood from UAV imagery was accurate (F1-score = 0.85) and consistent across the different study sites and years. Best results for the LSTM-based extrapolation of fractional cover of standing deadwood using Sentinel-1 and -2 time series were achieved using all available Sentinel-1 and --2 bands, kernel normalized difference vegetation index (kNDVI), and normalized difference water index (NDWI) (Pearson’s r = 0.66, total least squares regression slope = 1.58). The landscape-level predictions showed high spatial detail and were transferable across regions and years. Our results highlight the effectiveness of deep learning-based algorithms for an automated and rapid generation of reference data for large areas using UAV imagery. Potential for improving the presented upscaling approach was found particularly in ensuring the spatial and temporal consistency of the two data sources (e.g., co-registration of very high-resolution UAV data and medium resolution satellite data). The increasing availability of publicly available UAV imagery on sharing platforms combined with automated and transferable deep learning-based mapping algorithms will further increase the potential of such multi-scale approaches.

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems.

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation

Under the influence of uncertain parameters and external disturbance, the accuracy is low and the response is slow of attitude and trajectory tracking in the flight control of the quadrotor unmanned air vehicles (UAV). In order to solve the problems, Because of the excellent adaptability and noise suppression ability of the extended Kalman filter for nonlinear system problems , it is used to suppress the high-frequency signal disturbance to reduce the estimation burden of the extended state observer, and jointly with the expanded state observer to estimate the total disturbance formed by the system uncertainty parameters and external disturbances to reduce system reliance on accurate models, and the differential values of perturbation estimated values is used by feedforward compensation to improve the tracking accuracy under abrupt disturbances and to overcome the phase lag caused by abrupt disturbances. Extended Kalman filtering, extended state observer with the feedforward compensation and PD controller with error compensation are comprehensively integrated into an improved active disturbance rejection controller which can suppress high frequency noise and abrupt disturbances to the better extent. Simulation and experiment results show that the joint observer can effectively reduce the observation error amplitude, correct the observation phase lag in advance, and obtain more accurate state information, and that the improved active disturbance rejection controller can better meet requirements of quadrotor UAV for fast response, efficient and stable control. Therefore, it can accurately and efficiently complete complex trajectory tracking operations.

A discussion on energy harvesting through suspension system

Currently, most of the automotive vehicles use suspension systems where shock absorbers and dampers are used to smoothen the ride and dampen the vibrations caused by the abrupt changes and disturbances like road irregularities, braking and accelerating forces and so on. The vibration forces acting on the suspension system is converted into waste heat energy by the shock absorbers, this energy can be put into use by using a regenerative suspension system where the vibration energy is converted into electrical energy, this increases an automotives energy efficiency. This paper is a review on how we can use the various types of regenerative suspension systems and how it can be used to generate power and increase efficiency of our everyday lives.

Disentangling Environmental, Economic, and Technological Factors Driving Scallop (Argopecten purpuratus) Aquaculture in Chile

The boom-and-bust trajectory of the Argopecten purpuratus industry in Chile shows the progression from resource extraction (fishing) to production (aquaculture). This paper analyses the effects of environmental, economic, and scientific–technological factors. The influence of each factor on scallop production in Chile was reviewed for the period between the 1980s and 2020. The evaluation of the effects allows the visualisation of the industry’s productive evolution and reveals the current challenges. The occurrence of abrupt environmental disturbances, commercialisation under imperfect market configurations, and public and private efforts in scientific and technological advances have acted favourably on scallop production. However, an industry mainly focused on prices and high production volumes did not devote much effort to develop low-cost climate-resilient technologies. Today, economic challenges must be addressed by helping to reduce production costs and add economic value to products and by-products. Our results show that the industry must focus on low-cost technologies, the use of renewable energy, and the circularity of its processes. The environment ensures the capture of natural seeds and their adaptation to climate change. These challenges must not lose sight of the emerging effects of the COVID-19 pandemic.

Hydroclimate variability in the Madagascar and Southeast African summer monsoons at the Mid- to Late-Holocene transition

The 4.2 ka event at the Mid- to Late-Holocene transition is often regarded as one of the largest and best documented abrupt climate disturbances of the Holocene. The event is most clearly manifested in the Mediterranean and Middle East as a regional dry anomaly beginning abruptly at 4.26 kyr BP and extending until 3.97 kyr BP. Yet the impacts of this regional drought are often extended to other regions and sometimes globally. In particular, the nature and spatial extent of the 4.2 ka event in the tropics have not been established. Here, we present a new stalagmite stable isotope record from Anjohikely, northwest Madagascar. Growing between 5.22 and 2.00 kyr BP, stalagmite AK1 shows a hiatus between 4.31 and 3.93 kyr BP (±40 and ± 35 yrs), replicating a hiatus in another stalagmite from nearby Anjohibe, and therefore indicating a significant drying at the Mid- to Late-Holocene transition. This result is the opposite to wet conditions at the 8.2 ka event, suggesting fundamentally different forcing mechanisms. Dry conditions are also recorded in sediment cores in Lake Malawi, Lake Masoko and the Tatos Basin on Mauritius, also in the southeast African monsoon domain. However, no notable event is recorded at the northern (equatorial East Africa) and eastern (Rodrigues) peripheries of the monsoon domain, while a wet event is recorded in sediment cores at Lake Muzi and Mkhuze Delta to the south. The spatial pattern is largely consistent with the modern rainfall anomaly pattern associated a with weak Mozambique Channel Trough and a northerly austral summer Intertropical Convergence Zone position. Within age error, the observed peak climate anomalies overlap with the 4.2 ka event. However regional hydrological change consistently begins earlier than a 4.26 kyr BP event onset. Gradual hydrological change frequently begins around 4.5 kyr BP, raising doubt as to whether any coherent regional hydrological change is merely coincident with the 4.2 ka event or part of a global climatic anomaly.

Tropical Indian Ocean basin hydroclimate at the Mid- to Late-Holocene transition and the double drying hypothesis

The spatial pattern of Holocene climate anomalies is crucial to determining the mechanisms of change, distinguishing between unforced and forced climate variability, and understanding potential impacts on past and future human societies. The 4.2 ka event is often regarded as one of the largest and best documented abrupt climate disturbances of the Holocene. Yet outside the data-rich Northern Hemisphere mid-latitudes, the global pattern of climate anomalies is uncertain. In this study we investigate the spatial and temporal variability of the tropical Indian Ocean hydroclimate at the Mid- to Late-Holocene transition. We conducted Monte-Carlo principal component analysis, considering full age uncertainty, on ten high-resolution, precisely dated paleohydroclimate records from around the tropical Indian Ocean basin, all growing continuously or almost continuously between 5 and 3 kyr BP. The results indicate the dominant mode of variability in the region was a drying between 3.97 kyr BP (±0.08 kyr standard error) and 3.76 kyr BP (±0.07 kyr standard error) with dry conditions lasting for an additional 300 years in some records, and a permanent change in others. This drying in PC1, which we interpret as a proxy of summer monsoon variability, fits with a previously recognised tropic wide change in hydroclimate around 4.0 kyr BP. An abrupt event from 4.2 to 3.9 kyr BP is seen locally in individual records but lacks regional coherence.A lack of apparent 4.2 ka event in tropical Indian Ocean hydroclimate has ramifications for climate variability in the Indus valley, and for the Harappan civilization. Through a comparison of existing Indian subcontinent paleoclimate records, upstream climatic variability in the Indian Summer Monsoon and winter Westerly Disturbances source regions, and modern climatology, we present the “Double Drying hypothesis”. A winter rainfall drying between 4.2 and 3.9 kyr BP was followed by a summer rainfall drying between 3.97 kyr BP and at least 3.4 kyr BP. The Double Drying hypothesis provides more detailed climatic context for the Harappan civilization, resolves the cropping paradox, and fits the spatial-temporal pattern of urban abandonment. The consequences for the new Mid- to Late-Holocene Global Boundary Stratotype Section and Point in a stalagmite from Meghalaya are explored.

Loss of riparian forests from wildfire led to increased stream temperatures in summer, yet salmonid fish persisted

AbstractNative salmonid fishes—cutthroat trout (Oncorhynchus clarkii) and steelhead/rainbow trout (Oncorhynchus mykiss)—are ecologically, culturally, and economically important species distributed across western North America. These fish are generally considered “cold‐water” adapted species. As such, recent studies have speculated about the potential effects of climate change on these native salmonids if stream temperature thresholds exceed 16–20°C during the summer. However, the magnitude of stream thermal responses to the slow but steady increases in regional temperatures associated with climate change remains uncertain and hard to predict. Comparatively, abrupt disturbances, such as wildfire, may produce almost instantaneous and substantial increases in stream temperatures that may persist for multiple years until near‐stream vegetation becomes re‐established. In the first summer following a severe wildfire in western Oregon, we observed the initial persistence of populations of O. clarkii clarkii (coastal cutthroat trout) and O. mykiss (rainbow/steelhead trout). The fire burned the entire catchment, including the riparian area (~76% of the watershed area burned at moderate or high severity), resulting in stream temperature that regularly exceeded 20°C and represented increases of 6–7°C relative to prefire conditions. However, the mechanisms enabling the persistence of cold‐water fishes despite the dramatic increases in stream temperature remain unclear and require further investigation. Nevertheless, wildfires represent acute natural disturbances that can substantially alter stream thermal regimes and provide unique insights that allow us to better understand how native fishes in natural systems cope with projected increases in stream temperatures.