System Disturbances Research Articles

The sliding mode controller with composite reaching law for upper limb rehabilitation robot

Purpose Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs. Design/methodology/approach First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system. Findings The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%. Originality/value The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

Integral Sliding Mode‐Composite Nonlinear Feedback Control Strategy for Microgrid Inverter Systems

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

ROLE OF NF-κB ACTIVATION IN CHANGES OF NITRIC OXIDE PRODUCTION IN SKELETAL MUSCLES DURING METABOLIC SYNDROME

Nitric oxide, as a signaling molecule, plays an ambiguous role in many pathological processes. On the one hand, nitric oxide is necessary for maintaining the tone of the vascular wall of arteries and prevents the development of ischemia in various organs and skeletal muscles in particular. On the other hand, excessive production of nitric oxide in the tissue can contribute to the formation of toxic metabolites, such as peroxynitrite that leads to the development of nitrosative damage to various organs and tissues. Metabolic syndrome is also one of the diseases accompanied by disturbances in the nitric oxide production system. Currently, the sources of nitric oxide production, the predominant ways of its metabolism, and the influence of the transcription factor NF-κB on these processes in skeletal muscles under the conditions of the metabolic syndrome are insufficiently studied. The purpose of this study is to study the effect of an inhibitor of the activation of the transcription factor NF-κB on the production of nitric oxide and the concentration of its metabolites in the biceps femoris muscle of rats under the conditions of modeling the metabolic syndrome. The experimental study was carried out on male Wistar rats weighing 200-260 g. The animals were randomly divided into 4 groups of 6 animals each. Animals of group 1 (control group) received a standard vivarium diet. In group 2 (metabolic syndrome), in addition to the standard diet, the animals received a 20% fructose solution as their sole source of drinking water for 60 days. In group 3, the animals were given a standard vivarium diet and were injected intraperitoneally with ammonium pyrrolidine dithiocarbamate at a dose of 76 mg/kg, three times a week for 60 days. Group 4 received the same diet as group 2 along with the injections administered in group 3. In 10% homogenate of the biceps femoris muscle, the following were determined: total NO-synthase activity, activity of constitutive and inducible isoforms of NO-synthase, arginase activity, aitrate- and nitrite reductase activity, concentrations of nitrite, peroxynitrite, nitrosothiols, and sulfides. The administration of ammonium pyrrolidinedithiocarbamate during metabolic syndrome simulation resulted in a reduction in total NO-synthase activity and inducible NO-synthase activity by 33.85% and 34.66%, respectively, compared to the metabolic syndrome group; arginase activity decreased by 37.56%, while nitrate and nitrite reductase activities were reduced by 19.29% and 47.71%, respectively; nitrite concentration increased by 21.77%, peroxynitrite concentration decreased by 32.05%, nitrosothiol concentration increased by 29.27%, and sulfide concentration decreased by 17.39% compared to the group with metabolic syndrome. Activation of the transcription factor NF-κB under metabolic syndrome conditions leads to increased nitric oxide production through both L-arginine-dependent and L-arginine-independent pathways in the biceps femoris muscle, enhances arginase activity, and results in elevated peroxynitrite formation.

Variable impedance control for synchronizer position tracking based on gain scheduling

Achieving fast and stable synchronizer position tracking control can greatly improve the power and comfort of transmission shifting. In order to further improve the shift speed and stability of the synchronizer, this article takes the synchronizer control of a three-speed seamless transmission as the research object and proposes a variable impedance control method based on gain scheduling. Firstly, dynamic models of the shift actuator and synchronizer was established. Then, an impedance outer loop controller and a position inner loop controller were designed. In the impedance outer loop, adjust the synchronizer reference position based on the difference between the reference shift force and the actual shift force. At the same time, based on the actual control requirements of different motion stages of the synchronizer, the optimal impedance control parameters for each stage are selected for gain scheduling control. In the position inner loop, a prescribed performance-active disturbance rejection control is adopted, which solves the problem of system disturbance while ensuring the instantaneous performance of position tracking. Finally, simulations and bench tests were conducted on the position tracking control of synchronizer. The simulation and bench test results show that the control strategy proposed achieves fast and stable synchronizer position tracking, with superior control performance, which preliminarily verifies the effectiveness of the control strategy.

Improved adaptive robust control of direct-drive pump-valve cooperative brake-by-wire unit with disturbance compensation

Abstract Aiming at the needs of high-level autonomous driving, a direct-drive pump-valve coordinated brake-by-wire unit was designed. To improve the response speed, control accuracy, and robustness of the brake-by-wire unit, an improved adaptive integral robust control method (AIRC-AD) based on a novel adaptive law and disturbance compensation was proposed. By integrating pressure tracking error information and overall parameter estimation error information, a novel parameter adaptive law with fast convergence speed was designed. A finite-time disturbance observer was designed to observe the overall system disturbance, improving the system’s anti-disturbance performance. Finally, the stability of the control method was proven using the Lyapunov stability proof method. The results show that under step target signals, sinusoidal target signals and triangular wave target signals, AIRC-AD has better response speed and control accuracy. In conditions such as increased permanent magnet temperature, increased coil resistance, or reduced flow area due to hydraulic pipeline blockage, AIRC-AD has better adaptability. The unit and the method can meet the demand for precise braking force control in high-level autonomous driving.

Reinforcement learning-adaptive fault-tolerant IGC method for a class of aircraft with non-affine characteristics and multiple uncertainties

Abstract In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Investigating disturbances of the core material system in the lung-gut axis of COPD based on the transcriptomics-metabolomics-microbiomics integration strategy

Investigating disturbances of the core material system in the lung-gut axis of COPD based on the transcriptomics-metabolomics-microbiomics integration strategy

Analysis and control of the equilibrium position in bare electrodynamic tether systems

This study focuses on the calculation of the equilibrium position of the bare electrodynamic tether (BEDT) and the implementation of stable control during the de-orbiting process. A novel method for calculating the equilibrium position of the BEDT system is proposed, utilizing integral variable substitution. This approach provides an analytical expression for the equilibrium position, thereby addressing the limitations of numerical curve-fitting methods and facilitating further dynamic analysis and controller design. To address the influence of variations in geomagnetic field strength and electron density on the tether attitude during deorbiting, a sliding mode controller based on prescribed performance is designed to stabilize the BEDT around its equilibrium position by adjusting the tether length. This adjustment simultaneously regulates angular momentum and current on the tether. In contrast to conventional current switching methods, the proposed strategy effectively mitigates undesired transient responses and additional system disturbances, and accurately stabilizing the BEDT system around the equilibrium position. Numerical simulations show that the analytical equilibrium calculations closely match the nonlinear model, with an error margin of less than 5%. Additionally, the tether length adjustment strategy successfully stabilizes the system around the equilibrium position, achieving an angular deviation of less than 0.2 degrees and enhanced deorbit efficiency compared to the current switching control method.

Sensing of Power System Disturbances Using CNN-Aided Tailored BiLSTM Model

Sensing of Power System Disturbances Using CNN-Aided Tailored BiLSTM Model

Optimization of Harmony Improvement in Led Lights Using The MQPSO Method

Currently, there are many types of lighting used in the lighting field, one of which is LED lighting. Harmonics is a phenomenon in the electrical system that causes quality disturbances because the current waveform or voltage of the power source is distorted and can cause disturbances in the electrical system. In this research, mathlab was used with the MQPSO method to optimize LED lamp harmonics and reduce the THD value. This method uses a series of LED lighting components to optimize harmonic values. The components that make up the LED light circuit in question are resistors, capacitors and inductors. The optimization results show the best R, L, and C values ​​in the 53rd swarm (particle), and convergence values ​​are achieved in the 17th iteration with a fitness value of 0.5 and an R1 value of 550 Ω, and R2 530 Ω, R3 220 Ω, C1 1.0409 μF, C2 1.0117 μF, C3 1.0221 μF, L1 4.9984 H. The resulting THD and C-RMS values ​​are 1.4901% and 61.674%.

GRANULOSA CELL DYSFUNCTION AND IMPAIRED OOCYTE MATURATION PLAY SIGNIFICANT ROLES IN POLYCYSTIC OVARY SYNDROME PATHOGENESIS

The polycystic ovary syndrome (PCOS) is an endocrine disorder that affects women of reproductive age worldwide. It is characterized by hyperandrogenism, ovarian dysfunction, and polycystic ovarian morphology.With a particular focus on granulosa cell dysfunction and abnormal oocyte maturation, this comprehensive review explores the intricate molecular mechanisms driving PCOS pathogenesis. PCOS is characterized by hormonal and metabolic disturbances caused by long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and the insulin-like growth factor 1 (IGF1) signaling pathway.In this review, we discuss the dysregulation of certain lncRNAs, such as NEAT1, and miRNAs, including miR-29a-3p, in PCOS granulosa cells, as well as how these dysregulations affect cell proliferation, insulin sensitivity, and steroidogenesis. This study examines the interaction between non-coding RNAs and the IGF1 system, which reveals an intricate regulatory network involved in follicular development and ovarian function. These pathways are altered in expression and activity, which has implications for how oocytes mature and how fertility is affected.In addition, this review emphasizes the interconnected nature of granulosa cell dysfunction, oocyte maturation defects, and systemic metabolic disturbances in PCOS pathogenesis. Granulosa cells have the potential to be used as a research and therapeutic intervention focus, due to their accessibility and central role in ovarian function.Additionally, we propose future research directions using advanced technologies such as single cell sequencing and multi-omics technologies. According to the review, studies that focus on granulosa cells can help develop novel diagnostic markers, personalized treatment strategies, and potential PCOS-based therapies.In addition to providing insight into the molecular underpinnings of PCOS, this study sets the stage for future investigations aimed at improving diagnosis, management, and treatment.

From Plaques to Pathways in Alzheimer's Disease: The Mitochondrial-Neurovascular-Metabolic Hypothesis.

Alzheimer's disease (AD) presents a public health challenge due to its progressive neurodegeneration, cognitive decline, and memory loss. The amyloid cascade hypothesis, which postulates that the accumulation of amyloid-beta (Aβ) peptides initiates a cascade leading to AD, has dominated research and therapeutic strategies. The failure of recent Aβ-targeted therapies to yield conclusive benefits necessitates further exploration of AD pathology. This review proposes the Mitochondrial-Neurovascular-Metabolic (MNM) hypothesis, which integrates mitochondrial dysfunction, impaired neurovascular regulation, and systemic metabolic disturbances as interrelated contributors to AD pathogenesis. Mitochondrial dysfunction, a hallmark of AD, leads to oxidative stress and bioenergetic failure. Concurrently, the breakdown of the blood-brain barrier (BBB) and impaired cerebral blood flow, which characterize neurovascular dysregulation, accelerate neurodegeneration. Metabolic disturbances such as glucose hypometabolism and insulin resistance further impair neuronal function and survival. This hypothesis highlights the interconnectedness of these pathways and suggests that therapeutic strategies targeting mitochondrial health, neurovascular integrity, and metabolic regulation may offer more effective interventions. The MNM hypothesis addresses these multifaceted aspects of AD, providing a comprehensive framework for understanding disease progression and developing novel therapeutic approaches. This approach paves the way for developing innovative therapeutic strategies that could significantly improve outcomes for millions affected worldwide.

New Predictor‐Based Anti‐Disturbance Control for Discrete‐Time Systems With Input Delay Subject to Time‐Varying Uncertainties

ABSTRACTIn this article, a new predictor‐based anti‐disturbance control scheme is proposed for discrete‐time systems with input delay subject to time‐varying uncertainties, by only using the system output measurement. A high‐order extended state observer is firstly constructed to simultaneously estimate the system state, unknown disturbance and its high‐order difference with specification. Two alternative designs of the system state and disturbance predictors are then presented to construct an advanced anti‐disturbance control scheme. The disturbance attenuation performance of the resulting closed‐loop system is quantitatively analyzed. Moreover, a matrix‐inequality‐based sufficient condition is established to evaluate robust stability of the closed‐loop system under time‐varying uncertainties. Finally, a benchmark example is adopted to demonstrate the effectiveness and advantages of the proposed control schemes over the existing methods.

Under Voltage Relay System for Battery and DC Load Safety Using Arduino Nano

The quality of the electric power supply in an electrical system is very necessary. One of the important things that need to be considered is the quality of the voltage supply. The disturbance in the electrical system is under voltage. Undervoltage is an electrical system disturbance that will affect the performance of connected electrical equipment and reduce the life of the equipment, including battery components in DC voltage source electrical systems such as solar power plants. To anticipate this occurrence, Under Voltage Relay equipment is needed which can be used to protect batteries and DC equipment from under voltage conditions by using a potentiometer as a voltage divider and IC 7809 to reduce the voltage from the battery input, Relays to disconnect the load, LCD for monitoring and using Arduino Nano microcontroller control. The method used is a development method or Research and Development. This method is used as a basis for designing and manufacturing Low Voltage Relay devices to protect batteries and DC equipment. From the results of the tests carried out, it can disconnect the DC load when detecting low voltage conditions with an accuracy of ± 99.795%. for the normalized execution test for under voltage relay at voltages of 12 V and 24 V, there is a difference value of ± 0.096V and an error of ± 0.554%.

Mitochondrial dysfunction is a key link involved in the pathogenesis of sick sinus syndrome: a review.

Sick sinus syndrome (SSS) is a grave medical condition that can precipitate sudden death. The pathogenesis of SSS remains incompletely understood. Existing research postulates that the fundamental mechanism involves increased fibrosis of the sinoatrial node and its surrounding tissues, as well as disturbances in the coupled-clock system, comprising the membrane clock and the Ca2+ clock. Mitochondrial dysfunction exacerbates regional tissue fibrosis and disrupts the functioning of both the membrane and calcium clocks. This plays a crucial role in the underlying pathophysiology of SSS, including mitochondrial energy metabolism disorders, mitochondrial oxidative stress damage, calcium overload, and mitochondrial quality control disorders. Elucidating the mitochondrial mechanisms involved in the pathophysiology of SSS and further investigating the disease's mechanisms is of great significance.

Neural Network Adaptive Inverse Control of Flexible Joint Space Manipulator Considering the Influence of Gravity.

With the aim of correcting the problem of trajectory tracking control of a flexible joint space manipulator in environments with different gravity, a neural network adaptive inverse control algorithm based on singular perturbation theory is proposed to resist the disturbance caused by system uncertainty. Firstly, the dynamic model of a flexible joint space manipulator with the influence of gravity is established, and then the system is divided into a fast subsystem and a slow subsystem using singular perturbation theory. The velocity feedback control rate is designed for the fast subsystem to suppress the elastic vibration caused by the joint flexibility. For the slow subsystem, the uncertain term and known term are separated by the inverse control algorithm, where the uncertain term is approximated online by the RBF neural network, and the robust control rate is designed to compensate for the approximation error. The simulation results show that the control method can not only effectively reduce the high-frequency vibration caused by the flexible joint but also resist the system disturbance so that a good track control effect is achieved.

Robust data-driven model-free adaptive control to uncertainties and disturbances for NARMAX systems

This paper introduces an enhancement to the model-free adaptive control (MFAC) method based on a new control input cost function for full-form dynamic linearization (FFDL) for NARMAX systems. The new control cost function includes the one-step-ahead tracking error, OER, and output error integral. The main feature of the approach is that it has a high ability to overcome disturbances in the control input and measured noise in the output. Analytical and extensive simulations have demonstrated that the novel control law exhibits superior response and robustness in comparison to the prototype MFAC. Moreover, it guarantees stability and convergence of the tracking error by enforcing bounded-input bounded-output (BIBO) criteria. In addition, 20 Monte Carlo simulations were conducted to test the initial conditions and random control parameters, and the results demonstrated that the proposed control outperformed the prototype MFAC.

Abnormal EEG Effects of Acute Apomorphine Injection in 5xFAD Transgenic Mice Are Partially Normalized in Those Chronically Pretreated with Apomorphine: The Time–Frequency Clustering of EEG Spectra

Background: In experimental and clinical studies of pharmacological treatments for Alzheimer’s disease (AD), the electroencephalogram (EEG) frequency spectrum approach has demonstrated its efficacy in determining the characteristics of pathological changes in the functioning of different cerebral structures, interconnections between them, and disturbances in the brain neurotransmitter systems. The main results have been obtained in frames of traditionally used so-called “classical” EEG frequency bands: delta, theta, alpha, and beta. Objective: This unified approach simplifies comparing data from different studies but loses the dynamic peculiarities of the effects because of their time-dependent transition through the borders of the “classical” bands. Methods: In this study on non-narcotized freely moving 5xFAD transgenic mice, a model of AD, chronically pretreated with a non-selective dopamine (DA) receptor agonist, apomorphine (APO), we analyze the transitory EEG effects of acute APO injection in different brain areas by use of our “time–frequency” clustering program. The acute injection of APO was used to compare DA receptor sensitivity in 5xFAD mice pretreated with either APO or saline vs. wild-type (WT) mice pretreated with saline. Results: After acute APO injection, the clusters of enhanced EEG activity centered in the theta–alpha frequency range observed in WT mice disappeared in 5xFAD mice pretreated with saline and practically recovered in 5xFAD mice pretreated with APO. Conclusions: In 5xFAD mice pretreated with saline, the sensitivity of DA receptors was disturbed; chronic APO pretreatment mainly recovered this characteristic in 5xFAD mice. The “clustering” of pharmacological EEG effects and their time-dependent transition between classical frequency bands is a new effective approach for analyzing cerebral neurotransmission in neurodegenerative pathologies.

Post-dengue fatigue syndrome: A comprehensive review of an emerging clinical entity

Post-dengue fatigue syndrome (PDFS) is an emerging clinical entity characterised by persistent, debilitating fatigue and other symptoms following acute dengue infection. This review synthesises current evidence on the epidemiology, clinical features, pathophysiology and management of PDFS. Studies suggest that 20–25% of hospitalised dengue patients may develop PDFS, though population-based incidence is likely lower. Risk factors include older age, female sex and possibly host genetic factors. The hallmark symptom is profound fatigue, often accompanied by musculoskeletal pain, cognitive difficulties, sleep disturbances and mood changes. While some patients recover within weeks, others experience symptoms persisting for months or years. The pathophysiology of PDFS remains unclear but may involve persistent immune activation, hypothalamic-pituitary-adrenal axis dysfunction and autonomic nervous system disturbances. Diagnosis is based on the clinical history and exclusion of alternative causes. Management typically involves a multidisciplinary approach, including patient education, graded exercise therapy, cognitive behavioural therapy and symptomatic treatment. However, evidence for specific interventions is limited. Prognosis is variable, with some patients experiencing prolonged disability. Significant research gaps remain, including the need for standardised diagnostic criteria, biomarkers and controlled trials of potential therapies. As dengue’s global burden increases, further investigation of PDFS is crucial to improve recognition and develop evidence-based treatments for this impactful sequela of dengue infection.

MPPT control for PMSG based wind turbine: Adaptive dynamic programming approach

This paper proposes an online adaptive dynamic programming-based controller for the variable-speed wind turbine system, ensuring it tracks the maximum power point under uncertain conditions. This control scheme is composed of two components: the adaptive optimal control component and the high-order disturbance observer-based adaptive sliding mode control component. The adaptive optimal control part is designed using the online adaptive dynamic programming technique, which is one of the algorithms of the reinforcement learning method. This control component has the role of achieving the optimal characteristics for the nonlinear system. Likewise, the high-order disturbance observer is established to estimate system uncertainties and external disturbances. These approximated disturbances are then fed to the adaptive sliding mode control component for system disturbance compensation. As a result, the system, which is affected by the unknown disturbances, achieves robust optimal performance. In addition, the convergence of the adaptive laws and the stability of the overall system are ensured through the Lyapunov theory. Finally, comparative simulations are conducted to validate the advantages of the proposed control scheme in comparison to the existing methods.