Robust Control Research Articles

Study of Access Control Techniques on the Blockchain-enabled Secure Data Sharing Scheme in Edge Computing

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

Robust Visual Landing Control of Quadrotor on a Moving Platform: A Sampled-Data Approach With Delayed Output and Disturbances

Robust Visual Landing Control of Quadrotor on a Moving Platform: A Sampled-Data Approach With Delayed Output and Disturbances

Adoptive transfer of membrane-restricted IL-12-TCR T cells promotes antigen spreading and elimination of antigen-negative tumor variants

BackgroundAdoptive T-cell therapy has demonstrated clinical activity in B-cell malignancies, offering hope for its application to a broad spectrum of cancers. However, a significant portion of patients with solid tumors experience primary or secondary resistance to this treatment modality. Target antigen loss resulting either from non-uniform antigen expression or defects in antigen processing and presentation machinery is one well-characterized resistance mechanism. Constitutively expressed membrane-anchored interleukin-12 (caIL-12) has demonstrated enhanced antitumor activity and low systemic exposure in multiple preclinical adoptive T-cell treatment models with homogeneous tumor antigen expression. In this study, we assess the therapeutic impact of caIL-12 on target antigen-negative variants in syngeneic mouse models.MethodsTarget antigen-positive tumors were generated by transducing B16F10 melanoma cells (B16) or Lewis Lung Carcinoma cells (LLC) with a construct expressing the OVA antigen, SIINFEKL, tagged to ubiquitin (B16-U-OVA, LLC-U-OVA), while B16 or LLC tumors served as antigen-negative variants. C57BL/6J mice were subcutaneously injected with heterogeneous tumors composed of 80% B16-U-OVA and 20% B16. Bilateral tumors were established by injecting the left flank with B16-U-OVA or LLC-U-OVA tumors and the right flank injected with B16 or LLC tumors. The tumor-bearing mice then underwent 5.5 Gy total body irradiation, followed by adoptive transfer of OT-I TCR-T cells engineered with or without caIL-12.ResultsTCR-T cells (OT-I) delivered caIL-12 to the B16-U-OVA tumor sites and induced robust tumor control and survival benefits in mice bearing a heterogeneous tumor with OVA-negative variants. caIL-12 exerted its effect on OVA-negative B16 variants primarily by priming and activating endogenous antitumor CD8 T cells via antigen spreading. In addition, antigen spreading induced by OT-I-caIL-12 resulted in controlling OVA-negative tumors implanted at distant sites. This therapeutic effect required antigen-specific TCR-T cells and caIL-12 to colocalize at the tumor site, along with endogenous CD8 T cells capable of recognizing shared tumor antigens.ConclusionExpression of caIL-12 by tumor-targeting T cells demonstrated therapeutic effect against target-antigen-negative tumor variants, primarily through the induction of antigen spreading. These findings highlight the potential of caIL-12 to address challenges of antigen escape and tumor heterogeneity that may limit the efficacy of T-cell therapy against solid tumors.

Adaptive Robust Tracking Control With Active Learning for Linear Systems With Ellipsoidal Bounded Uncertainties

Adaptive Robust Tracking Control With Active Learning for Linear Systems With Ellipsoidal Bounded Uncertainties

Lifestyle-specific association between adversity dimensions with group-based mental health trajectories.

Childhood adversity (CA) has a substantial correlation with mental health problems. Keeping a healthy lifestyle is essential for mental health interventions; it is unclear, however, how healthy lifestyle affect the relationship between CA and persistent mental health problems. This longitudinal study (n=1112, 54.5% male) collected the data on CA (measured through three dimensions: threat, deprivation and unpredictability), mental health problems, and lifestyle factors. Group-based multi-trajectory modeling (GBMTM) was utilized to estimate trajectories for three mental health problems (i.e., depression, ADHD and overanxiety). Close friendships, regular physical activity, appropriate sleep duration, shorter screen time, and healthy eating were combined to establish a healthy lifestyle score (which ranges from 0 to 5). Higher scores indicated a healthier lifestyle. Three trajectories of mental health problems were identified: persistently low risk (24.9%), persistently medium-high risk (50.0%), and persistently high risk (25.1%). Multinomial logistic regression showed that high adversity (high-threat: β=2.01, P<0.001; high-deprivation: β=1.03, P<0.001; high-unpredictability: β=0.83, P=0.001; high-overall adversity: β=1.64, P<0.001) resulted in a persistently high risk of mental health problems; these outcomes were maintained after robust control for covariates. Further lifestyle stratification, null associations were observed among children with a healthy lifestyle, irrespective of their gender; however, after controlling for covariates, the above associations remained relatively stable only among boys. The generalizability of our findings is restricted by 1) limited racial diversity and 2) missing data. This finding underscores the benefits of promoting a healthy lifestyle in children to prevent persistent mental health problems caused by CA.

Reproduction and generalization of robot trajectory tracking control method using reinforcement learning and neural network techniques

Abstract. This paper introduces a method for controlling the trajectory of a manipulator robot with uncertainty, using reinforcement learning. The control is designed to work even when there are limitations on the inputs to the system. Reinforcement learning and neural networks are employed alongside standard robust control techniques to enhance the fixed-time convergence of the system state. A novel algorithm is proposed to develop a reinforcement learning-based approach. This approach utilizes radial basis function neural networks and nonsingular fast terminal sliding mode control to ensure error convergence within a predetermined time. This paper presents the task of monitoring the intended path followed by robotic arms in the presence of uncertain and unfamiliar disruptions. The experimental results validate that the suggested approach substantially improves both the stability and accuracy of trajectory tracking, making it more feasible for real-world applications in robotic systems.

Robust Control of Chat-PM: A Switched Singular System with Mode-dependent Bounded Nonlinearity

This paper is concerned with the robust control of the composite hybrid aerial-terrestrial precise manipulator (Chat-PM), a quadrotor-based robot with aerial/terrestrial movement and payload transportation capabilities. Given the distinct dynamics between aerial and terrestrial locomotion modes, Chat-PM is modeled as a type of switched singular system. A nonlinear term is contained in the system to model the coupling between translational and rotational movements of Chat-PM, which is proven to be norm-bounded and mode-dependent. Besides, the estimation error of the robotic arm's interaction force/torque is treated as the disturbance of the system. By means of the mixed H2/H∞ approach, numerically testable stability criterion are obtained, based on which the existence conditions for controllers with satisfactory transient and disturbance attenuation performance are provided. Compared to traditional studies assuming the nonlinearity term to be mode-independent, the conservatism in the controller design is reduced. Experimental results are provided to demonstrate the effectiveness of the proposed approach.

Performance and Robustness of Physiological Controllers With Integrated Pressure Sensors on Inflow Cannula.

The control of left ventricular assist devices (LVADs) requires sensors and/or estimators to account for the physiological state of the patient and apply advanced controllers. Sensor characteristics are a challenge when using implantable pressure sensors because they influence the quality of physiological control and the robustness of the controlled system. The objective of this work is to investigate the performance and robustness of LVAD controllers that operate based on LVAD integrated pressure sensors. Four pressure-based LVAD controllers are tested with a HeartMate 3 that has an integrated pressure sensor. Controller sensitivity as well as robustness to sensor drift and noise are evaluated based on controller response to large changes in preload and afterload. A fail-safe strategy for sensor failure used also to investigate the reliable operation of the LVAD in realistic conditions. All tested controllers are sensitive to drifting pressure signals, which can lead to unstable behavior. The results show that two controllers are robust to noise. The other two controllers show high deviation and oscillation in cardiac output even for small noise. Additionally, a noticeable difference of the controller's response between simulated and measured pressure input was observed, indicating the need for a robust controller design. Reliable operation even in the event of a sensor failure was achieved on the basis of a fail-safe control system.

How antitrust enforcement affects corporate ESG performance? Evidence from merger review cases in China

Chinese unique treatment of unreported merger cases (which should be reported according to antitrust law) in the context of merger review provides a distinctive and robust control group for the Difference-in-Differences (DID) research method. We employ the DID method to investigate the impact of China’s merger review antitrust enforcement on Environment, Social and Governance (ESG) performance. We find that the implementation of merger review antitrust enforcement suppresses corporate ESG performance, especially with significant effects observed on the Environment (E) and Governance (G) components. Mechanism analysis indicates that antitrust enforcement poses an impact on corporate profits by reducing market concentration and safeguarding fair market competition. This leads to the emergence of corporate financial performance protection behaviors, resulting in a decrease in corporate ESG performance. Heterogeneous analysis finds that the use of structural remedy in the enforcement process and stronger antitrust enforcement will further intensify the inhibitory effect of antitrust enforcement against business concentration on ESG.

Adaptive fuzzy gain-scheduling robust control for stability of quadrotors

Adaptive fuzzy gain-scheduling robust control for stability of quadrotors

Robust methane production from anaerobic digestion of maize silage: Feeding control using sliding mode control strategy with Anaerobic Digestion Model N°01

This paper introduces a robust control approach based on the Sliding Mode Control (SMC) strategy for the AD process. We use the Anaerobic Digestion Model N°01 (ADM1), the most detailed and comprehensive model for AD, as a virtual AD plant. The proposed control scheme is derived from a reduced model. This control law uses the state variables of the reduced AM2HN model as well as the stoichiometric and kinetic parameters. An interface block between the ADM1 and AM2HN state variables has been designed in a closed loop. The performance of SMC has been enhanced by the use of a low-pass filter, which completely eliminates chattering and provides smooth dilution rates (chatter-free) while rapidly reaching the target methane production (in less than 5 days) in a noisy environment.

Robust Driving Control Design for Precise Positional Motions of Permanent Magnet Synchronous Motor Driven Rotary Machines with Position-Dependent Periodic Disturbances

Position-dependent periodic disturbances often limit the accuracy and smoothness of the positional motion of permanent magnet synchronous motor (PMSM)-driven rotary machines. Because the period of these disturbances varies with the motion velocity of the rotary machine, spatial domain control methods such as spatial iterative learning control (SILC) and spatial repetitive control (SRC) have been proposed and applied to improve rotary machine motion control designs. However, problems with learning period convergence and rotary machine dynamics significantly affect transient motion, further constraining the overall motion performance. To address these challenges, this study developed a robust driving control (RDC) that integrates a robust control design with position-dependent periodic disturbance feedforward compensation, rotary machine dynamics compensation, and proportional–proportional integral feedback control. A position-dependent periodic disturbance model was developed using multiple position–sinusoidal signals and identified using a spatial fast Fourier transform. RDC compensates for disturbances and dynamics and considers the effects of model parameter uncertainty and modeling error on the stability of the control system. Several motion control experiments were conducted on a PMSM test bench to compare the RDC, SILC, and SRC. The experimental results demonstrated that although both SILC and SRC can effectively suppress position-dependent periodic disturbances, SILC provides slower position error convergence owing to the learning process, and SILC and SRC result in significant position errors because of the influence of the PMSM-driven rotary machine dynamics. RDC not only suppresses position-dependent periodic disturbances, but also significantly reduces position errors with a reduction rate of 90%. Therefore, the RDC developed in this study effectively suppressed position-dependent periodic disturbances and significantly improved both the transient-state and steady-state position-tracking performances of the PMSM-driven rotary machine.

Why active Willis metamaterials? A controllability and observability perspective

Recently, active Willis metamaterials (AWM) have been the focus of extensive investigations because of their unique electro-elastic coupling characteristics. However, the treatments of this class of materials have been carried out exclusively, in all the available literature, by approaches that do not rely on solid control theory basis. In this paper, the emphasis is placed on revealing very important control features that are inherent to this class of materials because of their Willis coupling characteristics. These features lie in the enhanced controllability and observability properties of the AWM as compared to non-Willis active materials. Such control properties enable the AWM to possess broad sensing and actuation capabilities that can lend this material to be an effective means for monitoring and controlling the behavior of numerous critical applications, such as acoustic cloaking, particularly when integrated with appropriate robust control strategies. A simple example of a piezoelectric-based AWM is presented to demonstrate its effective control capabilities and distinguish this class of materials from conventional materials. In the selected example, the AWM is structured from two dissimilar masses connected by a piezoelectric spring. Lagrange dynamics formulation is utilized to generate the equations governing the Willis coupling, the piezoelectric coupling, and reveal the inherent control features. With this developed controlled-based structure of the AWM, it is shown that the AWM can simultaneously monitor and control both the strain and velocity whereas the conventional active material, which is formed from two similar masses connected by a piezoelectric spring, can only measure and control the strain alone. It is envisioned that the revealed control metrics for the simple one-dimensional AMW example can serve as means for investigating the potential of AMW's of higher dimensionality.

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

Adaptive robust constraint-following control method for improving carbody hunting stability of high-speed trains

The low-frequency carbody hunting motion of high-speed trains (HSTs) frequently occurs due to the complex operating environment and deterioration of wheel–rail contact conditions. It not only affects ride comfort but also poses a risk of derailment safety accidents for HSTs. In such scenarios, traditional passive suspension systems struggle to ensure satisfactory dynamic performance of HSTs. However, active suspension systems can offer an effective solution. To enhance the low-frequency carbody hunting stability and environmental adaptability of HSTs, this study proposes a novel adaptive robust displacement inequality constraint following control (AICFC) method for active suspension utilising the beneficial nonlinearity inspired by bio-inspired structures (BIS). The effectiveness of the proposed AICFC-BIS method is validated through numerical simulations comparing it with other control strategies. Simulation results show that the AICFC-BIS method can effectively suppress low-frequency carbody hunting motion and improve the dynamic performance of HSTs. Compared with conventional passive suspensions and other active suspensions, HSTs equipped with AICFC-BIS active suspension exhibit significantly reduced operational safety risk and improved ride comfort indices while being energy efficient.

Synthesis of a parametrically robust controller with an extended state observer

This paper considers the problem of control of a parametrically uncertain dynamic system using an extended state observer. The control system is implemented using a two-loop scheme, where the outer loop ensures the fulfillment of the selected criterion for controlling the state vector, and the inner loop compensates for or reduces the influence of the vector of the total equivalent disturbance. The goal of the study is to develop a procedure for synthesizing an extended state observer taking into account the parametric uncertainty of the plant and the requirements for the closed loop of the combined control system specified in the frequency domain. Methods of control theory, robust control, and computer modeling are used for this study. The effect of the parametric uncertainty of the plant on its controlled motion is presented as a structured disturbance described in the form of a block-diagonal matrix. The concept of structured singular values is used to determine the measure of the system's robustness. Using the methodology of optimization of structured singular values, a procedure is proposed for synthesizing the extended state observer when considering a closed loop of a combined control system taking into account the parametric uncertainty of its mathematical model. The requirements ensuring the specified performance and stability of the closed loop of the control system taking into account the spectral properties of disturbances and sensor noise are formulated in the frequency domain using frequency-dependent weighting functions. For the synthesis of combined controllers based on the minimization of structured singular values, a D-G-L-K iteration algorithm is developed. The efficiency of the proposed approach is illustrated by a numerical example. Recommendations are given for the synthesis of parametrically robust combined controllers. The practical value of the obtained results lies in the fact that the procedure developed reduces the conservatism of robust combined control systems and, as a result, improves the control performance in the case of parametric uncertainty of the plant.

Prospects and challenges of mobile banking in Bangladesh: A case study

Mobile banking is a technology advancement that enables convenient, anytime, anywhere access to financial services. When using mobile banking, customers can access banking services via their phones without having to wait in line. It is a branchless kind of banking. The purpose of the study was to identify the prospects and challenges facing Bangladeshi mobile banking in the twenty-first century. Over 80 respondents were selected using Yaro Yamani formula from a sample of bank clients and bankers and given a standardized questionnaire. The study's goal has been perceived through the application of the descriptive research approach. Based on the study's findings, people's interest in mobile banking is growing daily due to its advantages. Certain banks do not presently offer all mobile banking services, and not all respondents are aware of all the mobile banking options available. Fast data transfer and cost-effective usage are two factors that entice customers. Some of the disadvantages of mobile banking are that it is very expensive, private, complex, etc. Lowering operational risks and increasing consumer confidence in Bangladesh's mobile banking services can be achieved through robust technical information control and security procedures that ensure the integrity and confidentiality of financial transactions.

Dynamic output feedback model predictive control for polytopic uncertain systems under decode and forward cooperative relay protocol

This paper addresses the robust model predictive control (RMPC) problem for a class of polytopic uncertain systems under the decode and forward cooperative relay protocol (DFCP). In order to combat signal fading from sensors to controllers and improve communication reliability, spatial diversity is achieved through cooperative terminal relay signals. At each transmission instant, the terminal only selects a better signal for transmission and a switched system is constructed. The aim of this paper is to design a set of desired dynamic output feedback controllers in the framework of RMPC such that the switched system with the underlying DFCP is exponentially stable. By taking the influence of the randomness of DFCP derived from a three nodes model composed of DF relay, a new objective function based on the mathematical expectation of the traditional quadratic function of MPC is established. Then, the orthogonal complement technique is applied to handle the inherent couplings of variables to formulate a solvable optimisation problem. Moreover, the sufficient conditions are provided to guarantee the recursive feasibility of the MPC strategy and the stability of the established closed-loop system in terms of the average dwell time (ADT) method. Finally, three experiments are given to illustrate the effectiveness of the proposed method.

Development and characterization of high-throughput serological assays to measure magnitude and functional immune response against S. Paratyphi A in human samples.

Typhoid and Paratyphoid fever cause a global health burden, especially for the children of Southern Asia. The impact of the disease is further exacerbated by the dramatic increase of antimicrobial resistance. While vaccines against Salmonella Typhi have been developed and successfully introduced, an effective vaccine targeting S. Paratyphi A is still lacking. Several efforts are currently ongoing to develop vaccines targeting both S. Typhi and S. Paratyphi A. In order to analyze the immune response induced by vaccination and in sero-epidemiological studies, easy to perform and high throughput immunoassays are needed. Here we present the setup and characterization of a customized ELISA assay and of a luminescent-based serum bactericidal assay (L-SBA) to measure the quantity of S. Paratyphi O antigen specific antibodies and their functional activity against S. Paratyphi A. Robust quality control criteria have been put in place both for ELISA and SBA and assays have been fully characterized in terms of quantitation limit, limit of blanks, specificity, linearity and precision. Assays are being employed to analyze samples from clinical trials, enabling the assessment of immunogenicity during clinical vaccine development.

Development of mathematical modeling of a mobile robot's motion control system

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters – roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25–30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions.