State Disturbance Research Articles

Research on adhesion properties of tidal flat soil on metal surface and discrete element simulation

The adhesion property of soil on the metal surface was tested by the orthogonal experiment with 4 factors and 3 levels based on piston pull out method. A regression model of adhesion stress and the factors including moisture content(X1), pressing time(X2), settling time(X3) and separation velocity (X4) was established. The experiment results show that the influence of each factor on adhesion stress is ranked as X1≈X2≈X3>X4>X3X4, and the adhesion stress shows an ‘S’ curve with the increase of separation velocity. The DEM parameters of the soil were calibrated based on the test results, and the contact and disturbance state of soil particles during the test were studied by discrete element method (DEM) using JKR model. The simulation tests show that the maximum adhesion stress occurs when the particles are about to separate from the probe surface, and the soil disturbance state is hierarchical.

Real‐Time Implementation of Unknown Input Observer‐Based State and Unknown Disturbance Estimation for Series Active Filters

ABSTRACTSeries active filter (SAF) is generally utilized in compensating harmonic voltage source‐type loads. In this paper, a combined action of controller and observer is proposed for SAF. Full state feedback (SFB) and linear quadratic regulator (LQR) controllers are posited in order to estimate states, and fault. SFB utilizes the pole placement method, whereas LQR utilizes the tuning of matrices by Bryson's rule. Luenberger observer (LO), proportional–integral observer (PIO), and unknown input observer (UIO) were also designed. The efficacy of this scheme is illustrated through the simulations, in the presence of faults. The state dynamics of SFB‐ and LQR‐based control of SAF with LO, PIO, and UIO in different faulty cases are compared. It can be clearly seen through simulations that the state dynamic response of LQR‐based control of SAF is faster when compared to SFB‐based control of SAF in both cases. Further, LO and PIO established a steady‐state error between the actual state and its estimate, with reference to desired input, in the presence of a fault. UIO perfectly tracked the desired input, even in the presence of various unknown fault, when compared to LO and PIO. Furthermore, UIO also estimates the unknown faults better when compared to PIO. Finally, in a dynamic operating scenario, the effectiveness of the suggested control strategy has been assessed and verified using MATLAB/SIMULINK and the output of a real‐time simulator (OPAL‐RT OP4510).

Snapshotting quantum dynamics at multiple time points

Measurement-induced state disturbance is a major challenge in obtaining quantum statistics at multiple time points. We propose a method to extract dynamic information from a quantum system at intermediate time points, namely snapshotting quantum dynamics. To this end, we apply classical post-processing after performing the ancilla-assisted measurements to cancel out the impact of the measurements at each time point. Based on this, we reconstruct a multi-time quasi-probability distribution (QPD) that correctly recovers the probability distributions at the respective time points. Our approach can also be applied to simultaneously extract exponentially many correlation functions with various time-orderings. We provide a proof-of-principle experimental demonstration of the proposed protocol using a dual-species trapped-ion system by employing 171Yb+ and 138Ba+ ions as the system and the ancilla, respectively. Multi-time measurements are performed by repeated initialization and detection of the ancilla state without directly measuring the system state. The two- and three-time QPDs and correlation functions are reconstructed reliably from the experiment, negativity and complex values in the QPDs clearly indicate a contribution of the quantum coherence throughout dynamics.

A Composite Control Method Based on Model Predictive Control and a Disturbance Observer for the Acquisition, Tracking, and Pointing System

Nonlinear disturbances, such as friction, backlash, and base vibration, are the main factors restricting the further improvement of the dynamic performance of the Acquisition, Tracking and Pointing (ATP) system. However, the modeling and compensation of the time-varying nonlinearities are still challenging. In this paper, a compound control algorithm based on model predictive control (MPC) and a Proportional Multiple-Integral State-Augmented Kalman Filter (PMISAKF) disturbance observer is proposed to improve the disturbance rejection performance. Specifically, a PMISAKF is used to estimate the system’s state and external disturbances in real-time. These observed disturbances are then treated as known external inputs at the current or future time points and incorporated into the predictive model of MPC. This allows the optimization process to take these external factors into consideration, enabling the calculation of an optimal control strategy and achieving high pointing and tracking accuracy for nonlinear time-varying disturbances. Experimental results show that the proposed method can effectively improve the system’s anti-interference ability and tracking accuracy.

Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.

Speed Control for PMSM with Fast-Terminal Super-Twisting Sliding Mode Controller via Extended State Disturbance Observer

Since robustness is only maintained on the sliding-mode surface, sliding mode control is inherently non-globally stable. Therefore, reducing the time to reach the sliding mode is crucial for enhancing sliding mode robustness. To improve the performance of conventional super-twisting reaching law (CSTRL) further, we propose a fast-terminal super-twisting reaching law (FTSTRL) designed to improve the quality of sliding mode control. This approach incorporates a terminal term and an exponential term with the CSTRL to ensure rapid convergence. The effectiveness of the designed FTSTRL is validated by comparing it to the CSTRL and a new sliding mode reaching law, demonstrating its superior performance. Finally, integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE) are employed for detailed comparative and quantitative analyses of the simulation results.

Toxicity profiles of immune checkpoint inhibitors in nervous system cancer: a comprehensive disproportionality analysis using FDA adverse event reporting system

Background: Immune-related adverse events (irAEs) always occur during treatment with immune checkpoint inhibitors (ICIs). Patients with nervous system cancer (NSC) may gain clinical benefit from ICIs, but irAEs in NSC patients are rarely examined. Therefore, our study systematically summarized reports of irAEs in NSC. Methods: We obtained information from the FDA adverse event reporting system from the first quarter (Q1) of 2013 to the fourth quarter (Q4) of 2022. We examined use of a combination of ICIs and chemotherapy (ICI_Chemo) or chemotherapy only (ICI_Chemo) for patients with NSC. Multiple disproportionality analyses were applied to assess irAEs. Multiomics data from the gene expression omnibus (GEO) database were analyzed to explore potential molecular mechanisms associated with irAEs in NSC patients. Results: Fourteen irAEs were identified in 8,357 NSC patients after removing duplicates; the top five events were seizure, confused state, encephalopathy, muscular weakness and gait disturbance. Older patients were more likely to develop irAEs than were younger patients. From the start of ICIs_Chemo to irAE occurrence, there was a significant difference in the time to onset of irAEs between age groups. irAEs may occur via mechanisms involving the inflammatory response, secretion of inflammatory mediators, and aberrant activation of pathologic pathways. Conclusions: This study helps to characterize irAEs in NSC patients treated with ICIs. We combined GEO database analysis to explore the potential molecular mechanisms of irAEs. The results of this study provide a basis for improving the toxic effects of ICIs in NSC patients.

Safety-Preserving Lyapunov-Based Model Predictive Rendezvous Control for Heterogeneous Marine Vehicles Subject to External Disturbances.

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.

THE INFLUENCE OF THE PSYCHO-EMOTIONAL STATE ON THE REPRODUCTIVE HEALTH OF WOMEN AFTER THE EXPERIENCED DISEASE OF COVID-19

Introduction. The coronavirus disease (COVID-19) recognized as a pandemic caused by the rapid spread of the SARS-CoV-2 coronavirus has become a serious challenge for the health care system around the world. Consequences of psychological health during recovery from COVID-19 can contribute to social alienation, social isolation, and economic losses. In particular, it remains to be investigated how the reproductive system can be directly or indirectly affected by SARS-CoV-2, in the long-term, precisely against the background of psycho-emotional changes. Aim. To study the psycho-emotional state of women and the impact on the reproductive system due to the impact of SARS-CoV-2. Materials and methods. In order to determine the impact of changes in the psycho-emotional state after suffering from the COVID-19 disease on the reproductive function of women with infertility, we included 80 patients who applied to the Carpathian Center for Human Reproduction in Ivano-Frankivsk in the period 2020–2022 for infertility treatment in a comprehensive examination, aged 18–40. Results. Vegetative dysfunction syndrome (VD), which was previously called vegetovascular dystonia (VDD), is attracting the attention of researchers again, in particular in association with "long-Covid-19", an increase in stressogenic loads. Currently, in world medicine, the term somatoform autonomic dysfunction is used, which most often refers to symptoms that are a consequence of another disease. Personal psycho-emotional characteristics of patients play a certain role in the occurrence and course of diseases, especially psychosomatic ones, which include the long-COVID-19 syndrome. A strong correlation of the severity of SVD with three main scales of the FPI personality test was established: with neuroticism (the strongest direct relationship, ρ = 0.72), depression (strong direct relationship, ρ = 0.64) and equanimity (strong inverse relationship, ρ = -0.61). Thus, the vast majority of women with long-Covid-19 were in an anxious state (91.2% vs. 30.0% of women in the control group, p<0.05), 22.5% had an anxiety level of 11 or higher and corresponded to clinically expressed, only 5.5% patients with clinically expressed anxiety were found in the group without COVID-19 (p<0.05). Clinically expressed depression in the main group was diagnosed 5 times more often than in the control group (13.8% vs. 2.5%, respectively, p<0.05). Conclusions. Disturbance of the psycho-emotional state in women with impaired fertility of reproductive age after suffering from Covid-19 has an impact on the effectiveness of infertility treatment. Understanding the mechanisms of the impact of the SARS-CoV-2 virus on the psycho-emotional state of women with infertility will contribute to the search for ways to prevent reproductive losses and pregnancy complications in women who have experienced COVID-19.

The influence of iron nutrition on the development of intestine and immune cell divergency in neonatal pigs

BackgroundAppropriate iron supplementation is essential for neonatal growth and development. However, there are few reports on the effects of iron overload on neonatal growth and immune homeostasis. Thus, the aim of this study was to investigate the effects of iron nutrition on neonatal growth and intestinal immunity by administering different levels of iron to neonatal pigs.ResultsWe found that iron deficiency and iron overload resulted in slow growth in neonatal pigs. Iron deficiency and iron overload led to down-regulation of jejunum intestinal barrier and antioxidant marker genes, and promoted CD8+ T cell differentiation in jejunum and mesenteric lymph nodes (MLN) of pigs, disrupting intestinal health. Moreover, iron levels altered serum iron and tissue iron status leading to disturbances in redox state, affecting host innate and adaptive immunity.ConclusionsThese findings emphasized the effect of iron nutrition on host health and elucidated the importance of iron in regulating redox state and immunity development. This study provided valuable insights into the regulation of redox state and immune function by iron metabolism in early life, thus contributing to the development of targeted interventions and nutritional strategies to optimize iron nutrition in neonates.

Unbiased minimum-variance disturbance and state estimation for linear systems with both state and output disturbances

This paper addresses the problem of simultaneously estimating the states, the state disturbances and the output disturbances of a linear time variant system. The objective is to develop a simple but optimal filter in the unbiased minimum-variance sense without depending extra complex system decomposition transformation. The derived filter bases on the assumption that no prior knowledge about the dynamical evolution of these disturbances is available.

Fixed-time disturbance observer-based funnel control for controllable excitation linear synchronous motor with state constraints

This paper proposes a fixed-time disturbance observer-based funnel control method for a controllable excitation linear synchronous motor (CELSM) with state constraints and disturbances. This approach simultaneously resolves the issues of prescribed tracking performance and state constraints, taking into account disturbances. We employ a fixed-time disturbance observer (DO) to estimate both matched and mismatched disturbances. Additionally, we design a modified funnel variable to confine the tracking error within a prescribed region and the singularity problem is avoided. An integral barrier Lyapunov function (iBLF) is utilized to directly tackle state constraints. Furthermore, we employ a fixed-time differentiator (FTD) to address the “explosion of complexity” issue in backstepping control. Theoretical analysis demonstrates that the tracking error asymptotically converges to the origin within a prescribed region while satisfying state constraints. Finally, simulation results demonstrate the efficacy of the proposed method.

Method of Equivalent Error as a Criterion of the Assessment of the Algorithms Used for Estimation of Synchrophasor Parameters Taken from the Power System.

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error's value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement).

Разработка технологии проведения и крепления горной выработки в зоне тектонически-ослабленных пород

Presence of tectonically weakened and disturbed zones leads to decreased underground excavation rates and reduced safety of the mining operations. As a rule, such zones are bounded by vertical and subvertical disturbances that contribute to weakening and loss of stability of the rock and ore mass. Disturbance of the natural state, in particular driving of a mine workings, provokes irreversible deformations due to redistribution and concentration of stresses within the boundary rock mass, which is more rigid. At the same time no significant stresses are formed within the rock mass of the tectonic zone due to its caving. Therefore, when developing solutions for driving and supporting mining excavations within the tectonic zone, the rock mass should be considered as weakened and highly fragmented with unbonded structural blocks. The particle-size distribution within the rock mass of the tectonic zone varies in a wide range of sized from several to tens of centimetres. In this context, the mining and rock reinforcement technology presented in this paper accounts for the possible free caving of the non-bonded host rock mass made up of small-sized blocks. The paper discusses physical and mechanical characteristics and the stress-and-strain state of the rock mass within the tectonically weakened zone and the boundary host rocks, and a technology of driving and supporting underground mine workings is proposed on this basis.

Robust State-Coupled Fault Estimation Based on Observer Design for Lipschitz Nonlinear Systems With State Disturbance Using Polytopic Linear Model

Robust State-Coupled Fault Estimation Based on Observer Design for Lipschitz Nonlinear Systems With State Disturbance Using Polytopic Linear Model

Fast Finite-Time Observer-Based Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Full-State Constraints.

This article studies a class of uncertain nonlinear multiagent systems (MASs) with state restrictions. RBFNNs, or radial basis function neural networks, are utilized to estimate the uncertainty of the system. To approximate the unknown states and disturbances, the state observer and disturbance observer are proposed to resolve those issues. Moreover, a fast finite-time consensus control technique is suggested in order to accomplish fast finite-time stability without going against the full-state requirements. It is demonstrated that every signal could be stable and boundless, and an event-triggered controller is considered for the saving of resources. Ultimately, the simulated example demonstrates the validity of the developed approach.

Humidity and temperature nonlinear control based on almost disturbance decoupling and state observers for air-handling units

The almost disturbance decoupling problem for air-handling units (AHUs) containing unmeasurable states is addressed. Firstly, a state observer is introduced to estimate the supply air temperature, which is an unmeasurable state. Secondly, the almost disturbance decoupling technique is utilized to attenuate the influence of unknown disturbances including outdoor temperature, outdoor humidity ratio, heat load, and strength of the humidity source. Finally, compared with the linearization and PID control methods, simulation results verify the superiority and effectiveness of the proposed controller.

Avaliação do Estado Mental: Comparação entre grupos de policiais militares do Distrito Federal com e sem indicadores clínicos

The present study aims to identify mental state disturbances associated with clinical indicators of mental health problems in a sample of military police officers from the Federal District. 263 police officers participated in this cross-sectional descriptive convenience sampling research. Participants responded to the Mental State Assessment Battery (BAEM), which assesses 11 dimensions of mental functioning. Data were obtained online and analyzed using descriptive and inferential statistics. The sample was categorized into two groups: with and without clinical indicators of mental health problems. The mean BAEM scores of these two groups were compared using the Welch’s t-test. The effect size was examined using the Hedge’s g method. The results indicated that police officers with clinical indicators tended to present higher BAEM scores than those without these indicators. The most altered psychological domains were attention, memory, executive functions, depressive and anxious thoughts, impulsive and restless behaviors, negative affects, emotional instability and intensity, and sexual, somatic and autonomy problems. Mapping these disturbances serves to build indicators of clinical mental health care for military police officers in the Federal District.

Observer‐based controller design for blood glucose regulation for type 1 diabetic patients with disturbance estimation: A backstepping approach

AbstractIn this paper, an observer‐based nonlinear controller is proposed to regulate blood glucose concentration (BGC) in type 1 diabetes mellitus (T1DM). The considered virtual patient model is the extended Bergmann minimal model, which is augmented by a meal disturbance and adapted to represent the insulin–glucose homeostasis of T1DM. The backstepping (BS) technique is used to design a closed‐loop feedback controller. The proposed controller does not need to measure insulin, plasma concentrations, and external disturbances while improving control performance and robustness against uncertainty. Insulin concentrations and plasma levels are estimated using state observers and disturbance using a disturbance estimator. These estimations are used as feedback to the controller. The asymptotic stability of the observer‐based controller is proved using the Lyapunov theorem. Furthermore, it is proven that the system is bounded input–bounded output (BIBO) stable in the presence of uncertainties caused by uncertain parameters and external disturbances. For real situations, we consider only the BGC available for measurement, and in addition, inter‐ and intra‐patient variability of system parameters is considered. The results confirm that the proposed controller can asymptotically regulate BGC through appropriate injection of insulin under meal disturbance and of variations in system parameters.

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

This paper proposes an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for multi-degree-of-freedom (n-DOF) hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. The controller design is formulated by integrating Radial Basis Function Neural Networks (RBFNNs) with state and disturbance observers using the backstepping method. The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error, coupled with external disturbances, is estimated through the combination of state observer and disturbance observer. The unique features of the proposed controller lies in its capability to estimate both matched and unmatched lumped disturbances. The auxiliary disturbance estimation law is guided by the neural learning weights and estimated system states provided by state observers. By effectively utilizing neural networks to approximate and mitigate most nonlinear uncertainties, the workload of the disturbance observer is substantially reduced. High-gain feedback is therefore avoided and improved tracking performance can be expected. Moreover, to avoid the tedious analysis and the problem of “explosion of complexity” in the conventional backstepping method, we employ a first-order sliding-mode differentiator. Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system, ensuring guaranteed and satisfactory tracking performance under the integrated influence of unknown nonlinearities, unmeasured states, and external disturbances. Extensive simulations are conducted to verify the effectiveness of the nested control strategy.