Control Approach Research Articles

Surface integrity analysis and inspection for nanochannel sidewalls using the self-affine fractal model-based statistical quality control for the atomic force microscopy (AFM)-based nanomachining process

The atomic force microscopy (AFM) technology is a promising method for nanofabrication due to the high tunability of this affordable platform. The quality inspection and control significantly impact the manufacturing effectiveness for realizing the functionality of the achieved nanochannel. Particularly, the surface characteristics of nanochannel sidewalls, which play a significant role in determining the quality of the nanomachined products, can not be accurately captured using conventional surface integrity metrics (e.g., surface roughness). Therefore, it is necessary to propose a method to quantitatively characterize the surface morphology and detect the abnormal parts/regions of the nanochannel sidewall. This paper presents a statistical process control approach derived from the self-affine fractal model to detect the sidewall surface anomalies. It evaluates changes in the self-affine fractal model parameters (standard deviation, correlation length, and roughness exponent), which can be used to signify the changes on the sidewall surface; the statistical distributions of these parameters are derived and used to develop control charts to allow inspection of the sidewall morphology. The approach was tested on the AFM-based nanomachined samples. The results suggest that the presented approach can effectively reflect the abnormal regions on the machined parts, which opens up a new avenue toward guiding the quality control and rework for process improvement for AFM-based nanomachining.

Design of modified complementary terminal sliding mode controller for wind turbine at region II using a two-mass model

Mechanical loads impact the life span of a wind turbine; therefore, the reduction of transient loads in the drive train has gained more emphasis during the design of the controller for power extraction. The trade-off between power extraction and load reduction on the drive train has become a critical concern for wind turbines. Existing control approaches improve energy extraction and impose a more significant transient load on the drive train. Therefore, to address the above issue, a modified complementary terminal sliding mode controller is proposed in this study for wind turbines at below-rated wind speeds. The performance of both the proposed and existing controllers has been tested with a 600 kW FAST simulator. Moreover, each controller has been examined using different wind spectral models, such as Kaimal, Von Karman, Smooth-Terrain, and NWTCUP. The turbulent intensities of these models varied from 5% to 25%, and average wind speeds ranged from 7 m/s to 8.5 m/s. A dSPACE 1202 board was used to test the efficacy of the proposed controller in real-time. This analysis indicates that the proposed controller reduces the transient load by 11.98% and the control input by 9.57% compared to the complementary terminal sliding mode controller. Additionally, the proposed controller improves the energy capture by 1.18%. Finally, this analysis shows that the proposed approach can enhance overall performance and capture maximum power at below-rated wind speeds compared to existing control schemes.

High-Order Fully Actuated Control Approach for Servo Systems Based on Dynamical Compensator and Extended State Observer

High-Order Fully Actuated Control Approach for Servo Systems Based on Dynamical Compensator and Extended State Observer

A novel robust adaptive control for nonlinear uncertain quarter-vehicle suspension system in presence of unknown time delay actuation

This paper presents a novel robust adaptive control approach for nonlinear uncertain vehicle suspension system with time delayed actuation and bounded disturbances. The uncertainty and disturbance as well as the input delay on the system are all limited and unknown. This paper explores a control-oriented nonlinear model to accurately describe the dynamics of the vehicle suspension which incorporates uncertainty, disturbance and actuator delay. The controller is designed based on robust and adaptive approaches, which along with guaranteeing general goals for the suspension system is able to assure the stability of the closed-loop system in the Lyapunov concept. Also, due to the use of smooth functions in the robust controller structure, sudden changes in the behavior of system states are prevented. The simulation and comparison results in MATLAB environment show the efficiency of the proposed robust adaptive method in covering the effects of uncertainty, disturbance and time-variant actuator delay.

A low-complexity solution for LMIs of hardly constrained nonlinear MIMO tracking control systems

This research is dedicated to the discrete-time tracking control of nonlinear multi-input multi-output (MIMO) Newtonian mechanics systems with an equal number of inputs and outputs, in the presence of input saturation and high-order dynamics of the actuation system. Firstly, the continuous-time model of the plant and actuation system has been transformed into the discrete-time domain using Adams–Bashforth and Adams–Moulton discretization techniques. In the next step, considering saturation boundaries, the modified position and velocity references are derived using the discrete-time reaching law-based control approach. These modified references are obtained through an optimization algorithm that calculates the closest achievable position and velocity trajectories with respect to the desired reference trajectory. Modifying desired trajectories in MIMO systems results in the formation of a system of linear matrix inequalities (LMIs). Solving such systems of inequalities requires the use of complex methods, and this difficulty increases with the increase in the system’s degrees of freedom (DOFs). The proposed strategy offers a more straightforward approach to solving these inequalities. Once the modified references are calculated, the corresponding position and velocity control signals can be obtained using the mentioned reaching law strategies. The final step of the proposed algorithm is to combine the resulting position and velocity control signals using a Gaussian-weighted averaging method to form the main control signal. A desirable performance for the control system can be achieved by appropriately adjusting the standard deviations of the Gaussian weighing functions. To evaluate the performance characteristics of the proposed method, it has been applied in the tracking control procedure of a nonlinear 3-DOF mechanical system. The numerical simulation results demonstrate the effectiveness of the method.

Formation Control of Multi-agent Systems Over Directed Topologies: A Sliding Mode Control Approach With Prescribed-time Convergence

Formation Control of Multi-agent Systems Over Directed Topologies: A Sliding Mode Control Approach With Prescribed-time Convergence

Changes in Healthcare Utilization After the 2022 Seoul Metropolitan Flood: Applying a Generalized Synthetic Control Approach.

Floods can have adverse health effects and impose a burden on healthcare systems. However, the potential consequences of floods on specific medical causes in densely populated metropolitan cities has not been characterized yet. Therefore, we evaluate the changes in healthcare utilization patterns after the 2022 Seoul flood using nationwide health insurance data. Based on the flood inundation map, districts within the flooded municipalities of Seoul were classified as severe-(n=12), mild-(n=22), or non-(n=38) flood-affected districts. Capitalizing on the timing of the flood as a natural experiment, a generalized synthetic control method was applied to estimate changes in the number of disease-specific hospital visits in flood-affected districts during 2weeks after the flood. We found excess hospital visits for external injuries (20.2 visits, 95% CI: -6.0, 45.2) and fewer visits for pregnancy and puerperium (-3.0 visits, 95% CI: -5.1, -0.5) in residents of flooded districts. When comparing severe- and non-flood districts, the increase in hospital visits for external injuries (56.2 visits, 95% CI: 17.2, 93.2) and a decrease in hospital visits related to pregnancy and puerperium (-5.3 visits, 95% CI: -8.4, -1.6) were prominent in residents living in severe-flood affected districts. Disease specific analysis showed an increase in hospital visits for injuries to the elbow and forearm, ankle and foot injuries, and chronic lower respiratory diseases in severe-flood-affected districts. However, these impacts were not observed when comparing the mild- and non-flood-affected districts. Our study suggests an immediate and substantial change in medical demand following flood exposure, highlighting the importance of public health responses after flood events.

Adaptive VSG control of flywheel energy storage array for frequency support in microgrids

The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids. Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches, this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA). First, by leveraging the FESA model, a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units. Furthermore, energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit. The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation. In addition, VSG technology is applied for the grid-connected control of the FESA, and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback. This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation. Subsequently, a parameter design method is developed through a small-signal stability analysis. Consequently, considering the SOC of the FESA, an adaptive control strategy for the inertia damping and the P/ω droop coefficient of the VSG control is proposed to optimize the grid support services of the FESA. Finally, the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

A shared robot control system combining augmented reality and motor imagery brain–computer interfaces with eye tracking

Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals.
Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency.
Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected.
Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications
in the future.

Data-adaptive hybrid control for power quality improvement using H-bridge circuit

The extraction of fundamental current and controlling the H-bridge circuit, called shunt active power filter (SAPF), for power quality support have always been major research concerns. The effectiveness of a SAPF depends on its fundamental component estimation. In this context, empirical mode decomposition (EMD) is applied to SAPF operations due to its effectiveness in complex signal analysis. Being a data-driven, adaptive tool, EMD decomposes the distorted nonlinear signal into signal- and noise-dominated signals, called intrinsic mode functions (IMFs). However, its exploitation has been hindered by its mode mixing issue. As a remedy, a second-order generalized integrator (SOGI) is integrated with the EMD approach to extract the required fundamental active component of distorted load current. Thus, this work investigates the effect of an EMD-based SOGI hybrid control approach in extracting the fundamental active component of distorted load current. The MATLAB/Simulink results demonstrate the effectiveness of the proposed hybrid control strategy for nonlinear load current generated by the diode bridge rectifier. Furthermore, the simulated results are validated through a real-time simulation result using the OPAL-RT OP4510 test bench. Results are analyzed under sinusoidal and distorted voltage scenarios at the point of common coupling. The simulation results showed that the proposed hybrid control approach provides better active filtering efficiency, support for reactive power, and improved total harmonic distortion, which meets the IEEE 1547-2018 standard.

Soft soap and linalool as potential management tools for Philaenus spumarius (Hemiptera: Aphrophoridae), vector of Xylellafastidiosa

Philaenus spumarius (Hemiptera: Aphrophoridae) is one of the most polyphagous xylem-feeding pests. It recently acquired bad fame when it was identified as the main vector of the bacterium Xylella fastidiosa, the causal agent of the Olive Quick Decline Syndrome that, within ten years, has affected and led to the death of more than 21 million olive trees in the Southern Italian region Apulia alone. Despite the agricultural practices and synthetic insecticides available, as not sufficiently effective, more strategies to target all the developmental instars of the pest and eco-friendly formulations, especially in organic orchards, are urgently needed. Therefore, the main goals of this work were to test the toxicity of a solution of potassium salts of fatty acids (soft soap) on P. spumarius juveniles and assess the attractiveness exerted by linalool on adults in a planar olfactometer. According to our results, the average neanids/nymphs’ mortality was 82.2 ± 10.4% 24h after spraying the soft soap on them at the labelled dose. Regarding the adults, linalool was significantly attractive to both sexes in a concentrations range between 0.01 and 0.12 μL linalool L−1 air. Our purpose is to potentially suggest a dual, integrated control approach against P. spumarius, with the soft soap as a biopesticide to reduce the immature stages of the pest and attractive, linalool-activated traps to numerically cull the population of the meadow spittlebug as part of a broader management system.

Integrating supervised and reinforcement learning for predictive control with an unmodulated pyramid wavefront sensor for adaptive optics

We propose a novel control approach that combines offline supervised learning to address the challenges posed by non-linear phase reconstruction using unmodulated pyramid wavefront sensors (P-WFS) and online reinforcement learning for predictive control. The control approach uses a high-order P-WFS to drive a tip-tilt stage and a high-dimensional mirror concurrently. Simulation results demonstrate that our method outperforms traditional control techniques, showing significant improvements in performance under challenging conditions such as faint stars and poor seeing, and exhibits robustness against variations in atmospheric conditions.

Discrete time stability of augmented Lagrangian formalism based underactuated inverse dynamics control method

Stability problems of robotic systems arise sometimes suddenly, seemingly for no reason. The digital time sampling is often the main cause of these instabilities. Discrete models, which are capable of the prediction of stability, are available for low-degree-of-freedom template models of linear position and force control. However, for the inverse dynamics control of underactuated systems, the literature has a lack of generally applicable results related to the effect of time discretization. Several control approaches are available in the literature out of which a widely used one, the augmented Lagrangian formalism and its stability properties are analysed in this work. Theoretical stability properties are obtained for a generally usable, linear, underactuated, two-degree-of-freedom constrained template model. The actuator dynamics, the finite difference approximation of the feedback velocity and the filtering of the feedback data are considered in the model. These phenomena strongly affects the stability properties. The theoretically obtained stability maps are experimentally validated on an underactuated crane-like indoor robot. The position and orientation accuracy of the robot were assessed: the absolute position error was below 30 mm and the orientation error was below 3°.

Prescribed‐time resilient current‐sensorless DC power converter control

AbstractWhile conventional control techniques for stabilizing power electronic converters with constant‐power load (CPL) often rely on complete system state information, the associated challenges, including the cost and increased size due to the installation of multiple sensors, necessitate exploration of alternative methodologies. To address these concerns, a novel observer‐based sliding‐mode control approach is proposed. This approach aims to enhance cost‐effectiveness and reliability in the context of a DC‐DC buck converter supplying power to an unknown CPL. The core concept involves designing a state observer to estimate the system's current based on voltage measurements. A non‐certainty equivalent adaptive update law is then introduced to estimate the unknown CPL, incorporating feedback from voltage measurements and current estimation. To guarantee stability in the face of uncertainties in system parameters, a thorough analysis utilizing the Lyapunov theorem is conducted on the closed‐loop system. The efficacy and feasibility of the proposed control algorithm are substantiated through experimental results on the OPAL‐RT platform, showcasing its promising performance.

Gut Microbiota and Diabetes

Type 2 diabetes is rapidly increasing worldwide and is closely linked to factors such as obesity, genetic predispositions, and lifestyle choices. Recent studies emphasize the significant role of gut microbiota in the development and progression of diabetes and other related metabolic diseases. It is also known that the effects and side effects of antidiabetic drugs can be influenced by gut microbiota. Recently, various therapeutic strategies have aimed at modulating gut microbiota for health improvement. Key approaches include the use of probiotics to enhance the balance of beneficial bacteria and prebiotics, which are dietary fibers that promote the growth of these beneficial bacteria. Additionally, the potential of fecal microbiota transplantation in restoring a balanced microbiota composition in individuals with metabolic diseases are explored. This review highlights the promising potential of these interventions in the prevention and management of type 2 diabetes, suggesting that gut microbiota could be a novel and effective approach in diabetes treatment and control.

The treatment of acid mine drainage (AMD) using a combination of selective precipitation and bio-sorption techniques: A hybrid and stepwise approach for AMD valorization and environmental pollution control

In this study, selective precipitation using magnesium oxide (MgO) and bio-sorption with banana peels (BPs) were explored for the treatment and valorization of acid mine drainage (AMD). The treatment chain comprised two distinct stages of which selective precipitation of chemical species using MgO (step1) and polishing of pre-treated AMD using BPs (step 2). In stage 1, 2.0 L of AMD from coal mine were used for selective precipitation and recovery of chemical species using MgO. The results revealed that chemical species of concern were precipitated and recovered at different pH gradients with Fe(III) precipitated at pH ≤ 4, Al at pH ≥ 4-5, Fe(II), Mn and Zn at pH ≥ 8 while Ca and SO42─ were precipitated throughout the pH range. In stage 2, the pre-treated AMD water was polished using BPs. The results revealed an overall increase of pH from 1.7 to 10, and substantial removal of chemical species in the following removal efficiency: Al, Cu and Zn (100% each), ≥ Fe and Mn (99.99% each), ≥ Ni (99.93%), and ≥ SO42─ (90%). The chemical treatment step removed pollutants partially, whereas the bio-sorption step acted as a polishing stage by removing residual pollutants.

Fuzzy Logic-based Current Control Logic for Proportional Flow Control Valve System of Medical Ventilators

The critical control parameters in ventilators that provide life support to patients are the flow of air and oxygen. Ventilators typically use Proportional Flow Control Valves (PFCV) and flow sensors to achieve precise flow accuracy. Maintaining accurate flow from the PFCV is essential to minimize the thrust exerted on the patient's lungs. Fuzzy logic is particularly well-suited for this task, as it effectively captures the subjective human judgments often required in clinical settings. This study aimed to develop a fuzzy logic algorithm to control pressure support ventilation. The research describes the fuzzy logic algorithm and presents experimental studies that evaluated the flow accuracy of a commercially used PFCV in ventilators. Additionally, the reduction in output flow due to thermal effects was investigated. A correlation between the solenoid coil current (measured as voltage across the coil) and the actual output flow through the PFCV was experimentally established using a current sensing circuit. Based on the characteristic curves obtained from the test setup, a Fuzzy Logic system was developed to minimize the error in the PFCV's flow. The implementation of PFCV using this Fuzzy Logic-based current control approach demonstrated a significant reduction in error, from 10% to 3%, in airflow delivery across its operating range, with additional sample results discussed. This reduction in flow error is crucial for assessing the suitability of PFCVs in infant ventilators. The described methodology can be experimentally applied to any PFCV variant before its use in medical ventilator applications to enhance delivered flow accuracy.

Ozone-Resistant Bacteria, an Inconvenient Hazard in Water Reclamation: Resistance Mechanism, Propagating Capacity, and Potential Risks.

Resistant bacteria have always been of research interest worldwide. In the urban water system, the increased disinfectant usage gives more chances for undesirable disinfection-resistant bacteria. As the strongest oxidative disinfectant in large-scale water treatment, ozone might select ozone-resistant bacteria (ORB), which, however, have rarely been reported and are inexplicit for their resistant mechanisms and physiological characteristics. In this study, six strains of ORB were screened from a water reclamation plant in Beijing. Three of them (O7, CR19, and O4) were more resistant to ozone than all previously reported ORB or even spores. The ozone consumption capacity of extracellular polymeric substances and cell walls was proved to be the main sources of bacterial ozone resistance, rather than intracellular antioxidant enzymes. The transcriptome results elucidated that strong ORB possessed a combined antioxidant mechanism consisting of the enhanced transcription of protein synthesis, protein export, and polysaccharide export genes (LptF, LptB, NodJ, LivK, LviG, MetQ, MetN, and GltU). This study confirmed the existence of ORB in urban water systems and brought doubts to the idea of a traditional control strategy against chlorine-resistant bacteria. A salient "trade-off" effect between the ozone resistance and propagation ability indicated the weakness and potential control approaches of ORB.

Air pollution below US regulatory standards and cardiovascular diseases using a double negative control approach

Growing evidence suggests that long-term air pollution exposure is a risk factor for cardiovascular mortality and morbidity. However, few studies have investigated air pollution below current regulatory limits, and causal evidence is limited. We use a double negative control approach to examine the association between long-term exposure to air pollution at low concentration and cardiovascular hospitalizations among US Medicare beneficiaries aged ≥65 years between 2000 and 2016. The expected values of the negative outcome control (preceding-year hospitalizations) regressed on exposure and negative exposure control (subsequent-year exposure) are treated as a surrogate for omitted confounders. With analyses separately restricted to low-pollution areas (PM2.5 < 9 μg/m³, NO2 < 75.2 µg/m3 [40 ppb], warm-season O3 < 88.2 μg/m3 [45 ppb]), we observed positive associations of the three pollutants with hospitalization rates of stroke, heart failure, and atrial fibrillation and flutter. The associations generally persisted in demographic subgroups. Stricter national air quality standards should be considered.

Sustainable waste management practices in the informal sector: Towards industrial symbiosis

Industrial pollution is considered to be routed in the waste and byproducts of the production process. Traditional pollution control approaches try to eliminate and/or treat the pollutants which are often technically complicated and expensive. In this regard, industrial ecology and industrial symbiosis have emerged as effective strategy to eliminate industrial pollution. This principle requires the generated waste/by-products absorbed in the same or other industrial process cycles and thus the material cycle remains closed. Industrial pollution appears as a big problem in the global south countries, where industrialization is considered as the main thrust of economic development. Usually, in such countries formal pollution control approaches are primarily directed to the formal sectors (such as state owned and legally registered industries), informal sectors are often left behind. Although the role of informal sector is increasingly being recognized for sustainable development, their significance in pollution abatement is a less discussed topic. This article attempts to investigate the informal industrial sector in Dhaka, Bangladesh with empirical evidence. Adopting a qualitative approach with field investigation of the informal industries and detail interviews, this study identified that the informal industries are closely linked in clusters according to the manufacturing process and continue material/byproduct/waster exchange primarily from the need to minimize cost. The studied patterns of waste management practice indicate existence of industrial symbiosis without adequate academic/technical knowledge and designed efforts. This suggests that the informal sector can meaningfully contribute to sustainable development offering insights for the application of similar approaches in the formal sector.