Disturbance Signals Research Articles

Comparison of recent advances in set-membership techniques: Application to state estimation, fault detection and collision avoidance

There has been a vast body of research on set-membership techniques in recent years. These algorithms compute convex sets that contain the state of a dynamical system given bounds on disturbance and noise signals. Recently, a thorough comparison of zonotopes-based methods against interval arithmetic and ellipsoids has been presented in the literature. However, two main issues were left unexplored: (i) added conservatism in the presence of bounds of different kinds such as ℓ2-norm for the disturbance and an ℓ∞-norm bound on the noise: (ii) set-membership methods can be used in different settings apart from guaranteed state estimation, such as fault detection and isolation and collision avoidance of autonomous vehicles. In this paper, we extend this comparison by considering state estimation, fault detection and isolation, and collision avoidance for interval arithmetic, ellipsoids, zonotopes, constrained zonotopes, polytopes and constrained convex generators in the presence of various combination of bounds for the exogenous signals. The main objective is to compare accuracy, computation time and the scalability of the growth of the data structures required by each set representation. The results indicate that intervals, ellipsoids and zonotopes have a much worse accuracy. The recently introduced Constrained Convex Generators have a negligible increase in computation time in comparison with constrained zonotopes but have a better accuracy when bounds for disturbances, noise and initial conditions are heterogeneous or at least not polytopic.

Ground Motion Modeling and Adaptive Joint Control for Large-Scale UAVs

Aiming at the problem of lateral deviation of large-scale long-endurance solar-powered UAVs relative to the runway during takeoff or landing, a UAV ground motion control structure based on the combination of engine differential and rudder was proposed. According to the structural characteristics of large-scale long-endurance solar-powered UAVs, a ground motion model of a three-point layout UAV including landing gear was established, and the ground rolling dynamics and modal characteristics were analyzed. In order to accurately correct the trajectory error, the outer loop designs a trajectory correction control law and gives the inner loop desired control instructions. In order to solve the problem of environmental disturbance and small heading damping, the inner loop adopts the adaptive back-stepping control method. The disturbance signal is estimated through the adaptive law and compensated into the control system to achieve balanced control of speed and rolling correction. Finally, medium-speed and high-speed sliding tests were designed to verify the rationality of the proposed control scheme and control structure, as well as the efficiency of the control law design method adopted.

The Placenta as the Main Source of Serotonin in Ontogenetic Dynamics: Inflammation-Induced Modulation of Placental Serotonin Can Be Prevented by Immunoglobulin Administration.

Placental serotonin is recognized as a key component of feto-placental physiology and can be influenced by environmental factors such as maternal diet, drugs, stress, and immune activation. In this study, we compared the contribution of placental and fetal sources to the maintenance of serotonin levels required for normal fetal development during ontogenetic dynamics. Our results demonstrated the leading role of the placenta at almost all stages of development. We investigated the modulatory effect of inflammation on placental serotonin levels. The data obtained showed that the susceptibility to prenatal inflammation depends on its severity and varies considerably at different stages of development. According to our results, inflammation-induced modulation of placental serotonin levels can be prevented by immunoglobulin administration at both early and late stages of development. Disturbances in placental serotonin signaling during critical developmental periods may have long-lasting consequences for the health and behavior of the offspring. Therefore, the ability to prevent environmental modulation of placental serotonin, and hence negative effects on the developing fetus, is of great importance.

Assessment of Changes in the Expression of Genes Involved in Insulin Signaling and Glucose Transport in Leukocytes of Women with Gestational Diabetes During Pregnancy and in the Postpartum Period.

Not much is currently known about disturbances in insulin signaling and glucose transport in leukocytes of women with gestational diabetes mellitus (GDM) during and after pregnancy. In this study, the expression of insulin signaling (INSR, IRS1, IRS2 and PIK3R1)- and glucose transporter (SLC2A1, SLC2A3 and SLC2A4)-related genes in the leukocytes of 92 pregnant women was assayed using quantitative RT-PCR. The cohort consisted of 44 women without GDM (NGT group) and 48 with GDM (GDM group) at 24-28 weeks of gestation. GDM women were then tested again one year after childbirth (pGDM group: 14 women (29.2%) with abnormal glucose tolerance (AGT) and 34 women (70.8%) with normoglycemia). The GDM and NGT groups were closely matched for gestational age and parameters of obesity, such as pre-pregnancy body mass index (BMI), pregnancy weight, and gestational weight gain (GWG) (p > 0.05). Compared to the NGT group, the GDM and pGDM groups were hyperglycemic, but the GDM group featured a more highly insulin-resistant condition than the pGDM group, as reflected by higher fasting insulin (FI) levels and the values of the homeostasis model assessment for insulin resistance (HOMA-IR) (p < 0.05). In leukocytes from the GDM and pGDM groups, PIK3R1, SLC2A1, and SLC2A3 were upregulated and IRS1 was downregulated, with a larger magnitude in fold change (FC) values for PIK3R1 and IRS1 in the GDM group and for SLC2A1 and SLC2A3 in the pGDM group. The expression of SLC2A4 was unchanged in the GDM group but upregulated in the pGDM group, where it was inversely correlated with HOMA-IR (rho = -0.48; p = 0.007). Although the INSR and IRS2 levels did not significantly differ between the groups, the IRS2 transcript positively correlated with pregnancy weight, fasting plasma glucose, FI, and HOMA-IR in the GDM group. Our findings indicate that pronounced quantitative changes exist between the GDM and pGDM groups with respect to the expression of certain genes engaged in insulin signaling and glucose transport in leukocytes, with insulin resistance of a variable degree. These data also highlight the relationship of leukocyte SLC2A4 expression with insulin resistance in the postpartum period.

Power quality disturbances categorization using Identity Feature Vector and Extreme Learning Machine

Power quality disturbances are variations or anomalies in the voltage, current, or frequency of electrical power that can affect the proper operation of electrical equipment. These disturbances are usually classified into different categories based on their attributes and effects. This article presents an intelligent technique based on an Identity Feature Vector and an Extreme Learning Machine (ELM). This study first derives a constant length vector for each disturbance signal. A wavelet transform is applied to derive attributes from the input disturbance signal, and the identity vector is formed using the approximation coefficients. After the required normalization procedures, the normalized identity vector is classified using an ELM. To assess the productivity of the suggested approach, 12 types of disturbances, single and combined, are generated, and the system's efficiency is studied. The results indicate that ten out of 12 combinations, including Harmonic, Sag, and Flicker, were detected with 100% accuracy. Additionally, the combination "Harmonic + Swell" exhibited the lowest accuracy, identified with 98% accuracy. The total average accuracy of this method is 99.75%. The outcomes demonstrate the highly favorable performance of this approach. This study evaluated the analyzed algorithm under noisy conditions with three different noise levels: 30 dB, 40 dB, and 50 dB, respectively. The average prediction accuracy for these three noise levels is 99.16%, 99.25%, and 98.91%. The outcomes demonstrate that the evaluated algorithm accurately detects power quality disturbances across various noisy conditions.

Application of RBF neural network PID control on buck DC-DC converter

Abstract A structure to solve low control accuracy and poor tracking performance in traditional PID controlled DC-DC converters when input voltages and load currents fluctuate has been proposed: combining PID control with Radial Basis Function (RBF) neural networks. To optimize the PID controller’s parameters, this approach makes use of the RBF neural network’s approximation capability for nonlinear functions. Under different test conditions, the neural network takes the output voltage and its deviation from the reference value as inputs. The hidden layer employs Gaussian activation and adjusts by gradient descent. The output layer computes the PID controller’s Proportional (Kp), Integral (Ki), and Derivative (Kd) by the linear combination of hidden layer node outputs. Simulation results using MATLAB/Simulink demonstrate that, under different test conditions, the system employing RBF neural network-PID control exhibits a smaller overshoot compared to traditional PID control systems. Furthermore, in steady-state circumstances, the settling time is shortened by around 1/2 and is decreased by roughly 1/4 in the presence of disturbance signals. These results unequivocally demonstrate that, in the presence of fluctuating test conditions, the new structure of this combination greatly improves the control precision and tracking performance of DC-DC converters.

Hippocampus carbonic anhydrase 1 via ERK pathway may be involved in depressive like behaviors.

Major depressive disorder (MDD) is a destructive mental disease, yet the mechanism is still not clear. Carbonic anhydrase, an efficient catalyst for CO2 conversion to carbonate and protons, could affect many functions, such as memory formation recognition. Lately, we illustrated that carbonic anhydrase 1 (CAR1) knockout (CAR1-/-) mice could lead to depressive-like behaviors, but the underlying molecular mechanism is unknown. Herein, we attempted to explore whether CAR1 knockout could result in transcriptional changes thus involve in depressive like behaviors. The present study revealed that compared with WT mice, the CAR1 Knockout (CAR1-/-) mice led to depressive-like behavior. According to the microarray profiling, a couple of disturbed signaling pathways are found in CAR1-/- mice. Proteins like GluR1 and GABA Aα1 were validated compared to control groups by western blotting, while NMDAR 2A was increased in RNA level compared to the control group. More interestingly, the proteins might be related to the ERK signal pathway, MAPK, and RSK decreased in protein level compared to the control group. Moreover, this decline could be restored when CAR1 was overexpressed in the ventral hippocampus of CAR1-/- mice and depressive-like phenotypes were also rescued. Our dataset suggests that CAR1 might influence depressive-like behavior through the ERK signal pathway, which may provide novel insights and evidences to MDD study.

Hybrid adaptive control for vibration reduction in the floating raft system based on FxLMS

To enhance the real-time performance of multi-frequency line spectrum vibration control within the floating raft system and minimize the vibration of the floating raft, a hybrid adaptive active vibration control algorithm based on the FxLMS algorithm is introduced. Focusing on the bottom degree of freedom in the floating raft system, a horizontal two-degree of freedom system is established. Initially, the dynamic model of this system is analyzed; subsequently, the hybrid adaptive control algorithm is analyzed theoretically, and its stability is verified by simulation under normal condition, shock disturbance condition, and random signal disturbance condition. The results reveal that the average control effects of feedforward control across each line spectrum under these conditions are −9.55 dB, −10.88 dB, and −9.91 dB, respectively. In contrast, hybrid adaptive control demonstrates stronger performance with average control effects of −23.19 dB, −21.65 dB, and −21.48 dB. Lastly, an experimental platform was established to assess the performance of the proposed control algorithm. The experimental results demonstrate that the hybrid adaptive control successfully mitigates vibrations arising from multiple frequency spectra. The hybrid adaptive control achieves average control effects of −14.77 dB, −15.13 dB, and −14.63 dB across the three conditions, demonstrating superior control efficacy.

Artificial Intelligence Signal Control in Electronic Optocoupler Circuits Addressed on Industry 5.0 Digital Twin

The paper is focused on the modeling of a digital twin (DT) through a circuit simulation and artificial intelligence (AI) analysis to determine the effects of disturbances and noise in optocoupler devices integrated into programmable logic controller (PLC) systems. Specifically, the DT analyzes the parametric and the predicted simulations about the sensitivity of the optocouplers versus noise and interference to provide possible corrective actions, compensating for the distortion of the output signal. The model is structured into two main data processing steps: the first is based on the circuit simulation of the optocoupler noise coupling by highlighting the time-domain sensitivity aspects and the frequency behavior of the coupled signals; the second one estimates the predicted disturbed signal by means of supervised random forest (RF) and unsupervised K-Means algorithms to provide further elements to prevent corrective solutions by means of risk maps. This work is suitable for Industry 5.0 scenarios involving machine control supported by AI-based DT platforms. The innovative elements of the proposed model are the DT features of scalability and modularity; the spatial multidimensionality, able to couple the effects of different undesired signals; and the possibility to simulate the whole PLC system, including its control circuits.

Enhancing gearbox fault diagnosis under phase estimation errors: A dynamic time warping and blind deconvolution approach

The blind deconvolution technique is widely used to extract subtle fault-related features from the noisy vibration signals of rotating machinery. However, utilizing conventional Minimum Entropy Deconvolution (MED) methods is challenging for analyzing gearboxes, where significant internal and external non-Gaussian disturbance signals are inherent. Although periodicity analysis with signal alignment can be employed to solve this challenge, most available methods require exact phase information for the signals; this must be measured by a specially installed encoder system. To address this issue, this paper introduces a novel method for the fault diagnosis of gearboxes that effectively addresses the significant challenge of extracting periodic fault characteristics from noisy vibration signals, particularly in scenarios characterized by significant phase estimation errors and without the need for an encoder system. By combining the strengths of blind deconvolution with Dynamic Time Warping (DTW)-based sequential signal alignment, our method facilitates the identification and alignment of samples with identical rotational phases. This approach enables the distinction between impulsive features caused by faults and those resulting from noise, thus overcoming the limitations of conventional techniques, which fail to extract periodic fault characteristics under phase estimation errors. The effectiveness of the proposed approach is verified through analytic simulation and experimental validation using data from 2-kW planetary gearbox testbeds at Seoul National University (SNU) and the Korea Research Institute of Standards and Science (KRISS). Results demonstrate the superiority of the proposed method for identifying fault conditions under significant phase estimation errors compared to conventional methods.

Finite-time trajectory tracking control of quadrotor UAVs based on neural network disturbance observer and command filter

The paper proposes a novel finite-time control strategy for quadrotor UAV trajectory tracking using a neural network disturbance observer and a command filter. This method is used to address input saturation and disturbances, ensuring that the UAV can accurately follow the desired trajectory in finite time. The neural network disturbance observer is crucial for approximating external disturbance signals within a finite time, while the finite-time backstepping scheme accelerates the convergence of tracking errors. The command filtering technique is employed to avoid the complex derivation of virtual control laws, simplifying the controller design. The importance of this method lies in its ability to achieve fast, disturbance-resistant trajectory tracking for UAVs, making the control system more robust in practical applications. Simulations were conducted, showing that the proposed control strategy enables the quadrotor UAV to track its desired trajectory effectively, with improved anti-jamming capability. Both filtering and observation errors converged to the equilibrium point, validating the effectiveness of the approach. However, internal factors like actuator failure were not considered, pointing to future work in refining the method and applying it in real-world UAV experiments.

Research on impedance measurement based on Gallium Nitride Transistor

Abstract Impedance measurement has broad applications in fields such as electrochemistry, biology, medicine, and food safety. Typically, frequency response analyzers are used for small signal disturbance measurements, but they come with drawbacks such as high cost, large size, and limited excitation capabilities. To address the need for wide-band, large-signal, and portable impedance measurement in electrochemical applications, a novel scheme based on a Gallium Nitride device excitation circuit and amplitude-phase discrimination module is proposed. A multiphase cascade frequency-doubling buck circuit is used to inject disturbance signals into the impedance under test, and impedance parameters are obtained using discrete fourier transformafter signal sampling. By varying the frequency of the disturbance signal, impedance information across different frequencies can be captured. In this approach, the amplitude-phase discrimination module replaces the frequency response analyzer, reducing the overall size of the measurement system, while the power device-based excitation circuit enables large signal disturbances.

Molecular landscape of the overlap between Alzheimer’s disease and somatic insulin-related diseases

Alzheimer’s disease (AD) is a multifactorial disease with both genetic and environmental factors contributing to its etiology. Previous evidence has implicated disturbed insulin signaling as a key mechanism that plays a role in both neurodegenerative diseases such as AD and comorbid somatic diseases such as diabetes mellitus type 2 (DM2). In this study, we analysed available genome-wide association studies (GWASs) of AD and somatic insulin-related diseases and conditions (SID), i.e., DM2, metabolic syndrome and obesity, to identify genes associated with both AD and SID that could increase our insights into their molecular underpinnings. We then performed functional enrichment analyses of these genes. Subsequently, using (additional) GWAS data, we conducted shared genetic etiology analyses between AD and SID, on the one hand, and blood and cerebrospinal fluid (CSF) metabolite levels on the other hand. Further, integrating all these analysis results with elaborate literature searches, we built a molecular landscape of the overlap between AD and SID. From the landscape, multiple functional themes emerged, including insulin signaling, estrogen signaling, synaptic transmission, lipid metabolism and tau signaling. We also found shared genetic etiologies between AD/SID and the blood/CSF levels of multiple metabolites, pointing towards “energy metabolism” as a key metabolic pathway that is affected in both AD and SID. Lastly, the landscape provided leads for putative novel drug targets for AD (including MARK4, TMEM219, FKBP5, NDUFS3 and IL34) that could be further developed into new AD treatments.

Signature of climate dynamics on hydrological drought dynamics: A qualitative analysis

Understanding the complexity of climate dynamics is of paramount importance, because climate variability and change significantly affect water cycles and ecosystems. However, recurrent hydrological droughts that have been observed every 10 years on the two primary tributaries of the Congo River (the Ubangui and Kasaï Rivers) since the advent of the 1969 drought lack a plausible explanation for variability or climate change. This study proposes a plausible explanation for recurring hydrological droughts. Given the low rate of human activity and vegetation cover evolution in the Congo Watershed, we propose that climate dynamics play a crucial role in hydrological drought dynamics. By applying the Bayesian approach to a gridded precipitation database, we obtained posterior probability maps for each annual time step during our observational period (1940–2020). This provided a spatiotemporal representation of the areas affected by climatic disturbances, unlike previous studies that were limited to a spatial representation of the temporal location of the disturbances. Our qualitative analysis of the maximum intensity of the climate disturbance signal (CPS) revealed an average cycle of 10 years and eight months of signal migration. However, we observed that every 10 years since the advent of the drought during 1969, the hydrological drought occurrence dates coincided with CPS migration dates. This highlights the influence of this cycle on the hydrological drought dynamics. Nevertheless, because of the monthly scale involved in the propagation time from meteorological to hydrological drought, the error in this cycle was considered to be the propagation time of disturbances. Therefore, we recommend that future research should focus on estimating this time to test this hypothesis. This study underscores the significance of the cyclic dynamics of lengthy transient processes in understanding hydrological drought dynamics.

Generalized Nested Logit-Based Stochastic User Equilibrium Considering Static Wayfinding Instructions

Despite the availability of electronic navigators and automated vehicles, static wayfinding instructions remain widely used due to their resistance to signal disturbances, as well as their economic and environmental advantages over electronic signs. To investigate the impact of static wayfinding on the network cost and flow distribution, this paper presents an efficient method for updating the incident matrix and extends the stochastic user equilibrium (SUE) framework to incorporate static wayfinding instructions by using the generalized nested logit (GNL) choice model to represent user behavior. The SUE principle relaxes the assumption that users possess perfect knowledge of traffic conditions and always choose the optimal link to minimize their costs. The GNL model improves generalization performance of the cross-nested logit (CNL) model while solving the overlap problem of the multinomial logit (MNL) model. The disaggregate simplicial decomposition (DSD) algorithm is applied to solve proposed user equilibrium by iteratively finding decent directions through an auxiliary solution and determining step size using different methods. The results indicate that the self-regulated averaging (SRA) method can solve the proposed model efficiently. Additionally, increasing travel time cost on guided links and even outer links can be potential influences caused by static wayfinding instructions. The study results can assist decision-makers in quantitatively assessing the value of placing static wayfinding instructions at certain locations and choosing effective layout information.

Rational Search for Betaine/GABA Transporter 1 Inhibitors─In Vitro Evaluation of Selected Hit Compound.

Inhibitory neurotransmission mediated by γ-aminobutyric acid (GABA) plays an important role in maintaining body homeostasis. Disturbances in GABA signaling are implicated in a multitude of neurologic and psychiatric conditions, including epilepsy, ischemia, anxiety, depression, insomnia, and mood disorders. Clinically relevant increases in GABA neurotransmitter level can be achieved by inhibition of its uptake into presynaptic neurons and surrounding glial cells, driven by GABA transporters (GAT1, BGT1, GAT2, and GAT3). Herein, we focused on the search for inhibitors of the BGT1 transporter which is understudied and for which the therapeutic potential of its inhibition is partly unknown. We applied multilevel virtual screening to identify compounds with inhibitory properties. Among selected hits, compound 9 was shown to be a preferential inhibitor of BGT1 (IC50 13.9 μM). The compound also revealed some inhibitory activity against GAT3 (4x lower) while showing no or low activity (IC50 > 100 μM) toward GAT1 and GAT2, respectively. The predicted binding mode of compound 9 was confirmed by mutagenesis studies on E52A, E52Y, Q299L, and E52A+Q299L human BGT1 mutants. Subsequent evaluation showed that the selected hit displayed no affinity toward major GABAA receptor subtypes. Moreover, it was nontoxic when tested on normal human astrocytes and even showed some neuroprotective activity in SH-SY5Y cells. Compound 9 is considered a promising candidate for further evaluation of the therapeutic potential of BGT1 transporter inhibition and the development of novel inhibitors.

A high-performance, flexible, and dual-modal humidity-piezoelectric sensor without mutual interference

High-performance, flexible, and multifunctional humidity-piezoelectric sensors are applicable to respiratory pattern detection, man-machine motion distinction, and dual-modal speech recognition. However, signal disturbances between the humidity and pressure signals remain, and interference from the external environment such as temperature further results in signal vibrations. In this study, a high-performance, flexible, and dual-modal humidity-piezoelectric sensor is introduced to achieve accurate and stable sensing signals by fully prohibiting mutual interference towards simultaneous humidity and pressure stimuli. It is first designed using a high-performance humidity-sensing unit constructed by polyacrylate sodium (PAAS)/carbon nanotube (CNT) film and a piezoelectric sensing unit formed by polyvinylidene fluoride (PVDF)/CNT nanofiber film. The patterned PAAS/CNT unit exhibits a resistance change of 1100 % in relative humidity of 23 %–98 %, minor hysteresis, favorable durability, and negligible pressure interference. The PVDF/CNT piezoelectric unit demonstrates a sensitivity of 0.11 V/kPa in a wide pressure range of 3–115 kPa, good linearity, high durability, negligible humidity and temperature interference, and fully prohibited signal noise. Moreover, the high-performance, flexible, dual-modal humidity-piezoelectric sensors display significant applications for man-machine motion detection, human gestures and object distinction, and dual-modal speech recognition.

Transient voltage stability assessment and margin calculation based on disturbance signal energy feature learning

With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages. Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

Optical signal characteristics analysis of atmospheric disturbance density fields generated by high-speed aircraft

Aircraft disturbs the adjacent atmospheric environment in flight, forming spatial distribution features of atmospheric density that differ from the natural background, which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft. In this paper, the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed, and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed. By modeling the spatial distribution of the disturbance density field, the scattered echo signal images of active light towards the disturbance field at long distance are simulated. On this basis, the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed. The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images. The disturbance signal can be further highlighted by differential processing of the adjacent scattering images. As the distance behind the aircraft increases, the diffusion range of the disturbance signal increases, and the signal intensity and contrast with the background decrease. Under the ground-based observation conditions of the aircraft at a height of 10000 m, a Mach number of 1.6, and a detection distance of 100 km, the contrast between the disturbance signal and the background was 30 dB at a distance of one time from the rear of the fuselage, and the diffusion diameter of the disturbance signal was 50 m. At a distance eight times the length of the aircraft, the contrast decreased to 10 dB, and the diameter increased to 290 m. The contrast was reduced to 3 dB at a distance nine times the length of the aircraft, and the diameter was diffused to 310 m. These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.

Analysis of disturbance factors of magnetorheological damper in continuous impact buffer system

Abstract Magnetorheological (MR) impact buffering systems are widely used in vehicle suspensions, bridge damping, and aircraft landing gear due to their excellent buffering performance and rapid response time. However, under the condition of high-speed continuous impact, magnetorheological damper (MRD) operate in complex environments where various internal and external uncertainties can negatively affect control performance. This paper analyzes the impact of disturbance signals on MR buffering systems and explores control strategies to mitigate these effects. First, we established a hysteresis model based on experimental data and identified parameters using a genetic algorithm to determine the influence of hysteresis disturbances. Next, we developed a temperature model based on the thermal characteristics of SG-MRF2035 magnetorheological fluid, fitting the relationship between temperature and dynamic viscosity to identify temperature disturbances. The results showed that when disturbances were considered, the system exhibited higher peak damping forces and a deviation from the desired ‘platform effect’ in the damping force-displacement relationship. Finally, we applied an Active Disturbance Rejection Control(ADRC) strategy, which effectively compensated for the hysteresis and temperature disturbances, enhancing the system’s robustness. Compared to PID control, the ADRC-controlled system demonstrated lower peak damping forces and a damping force-displacement relationship closer to the desired platform effect.