Closed-loop System Research Articles

MiniMed 780G system performance in older users with type 1 diabetes: Real-world evidence and the case for stricter glycaemic targets.

Large-scale studies on the effectiveness of automated insulin delivery (AID) systems in older people with type 1 diabetes are still limited. A multinational, retrospective, real-world study was conducted to examine the performance of the MiniMed™ 780G advanced hybrid closed-loop system in users with type 1 diabetes aged ≥56 years compared with those aged 16-55 years. Data from 35 366 MiniMed™ 780G system users aged 16-55 years and 7415 users aged ≥56 years were included. The main outcome was time in range 70-180 mg/dL (TIR); other continuous glucose monitoring (CGM) metrics were also assessed. Across all users, mean TIR was 77.1% for users aged ≥56 years and 73.1% for those aged 16-55 years (Δ4.0, 95% confidence interval [CI]: 3.8-4.2, p <0.0001). In users employing the optimal system settings (i.e., Glucose Target: 100 mg/dL; active insulin time: 2 h), mean TIR was 81.9% in older and 79.7% in younger users (Δ2.2, 95% CI: 1.5-2.9, p <0.0001). Across all users, mean time below range <70 mg/dL (TBR70) was 1.5% in older and 2.1% in younger users. In older users, TIR and TBR70 remained consistent over 12 months. This real-world analysis demonstrated that older MiniMed™ 780G system users with type 1 diabetes can achieve a TIR >70% without increasing hypoglycaemia risk. Users employing optimal settings showed the best outcomes. The system performed as well as or better than in younger users. These findings support the case that more stringent TIR targets can be achieved safely.

Experimental Operation of a Prototype 750C Advanced Chloride Molten Salt Bellows Valve

To achieve DOE 2030 SunShot targets that reduce the cost of liquid-based solar by an additional 40% to 70% beyond 2018 costs, a more reliable, highly manufacturable flow valve, capable of achieving operational temperatures of &gt;700°C is required [1]. This paper investigates the development of an innovative high-temperature chloride molten salt valve, with operation up to 750°C. This valve is intended to be employed within Gen 3 CSP liquid-based thermal energy storage (TES) systems as well as Gen 4 modular salt reactor (MSR) technologies. This work details the general design and flow testing of a bellows-seal flow control valve (FCV). This design includes an integrated closed-loop thermal control system to ensure robust design for freeze-thaw cycles. The self-contained thermal management STM system, is unique in the salt valve industry since it is an integrated solution to provide a consistent, repeatable alternative to typical heat tracing. Additionally, the design includes the employment of a novel heat pipe valve stem to facilitate enhanced passive thermal management into the valve assembly. This valve stem heat pipe is designed to facilitate natural circulation within the bonnet to ensure robust operation, during both nominal and transient thermal operation. The valve body and trim will be designed using SS316H, consistent with Flowserve Corporation’s existing product base and is code qualified but will utilize clad material for materials corrosion, manufacturing cost reduction and compatibility to ensure design flexibility. A test campaign was performed in this investigation utilizing a novel 750°C ternary chloride (20%NaCl/40%MgCl2/40%KCl by mol. wt. %) molten salt flow loop. A discussion about the design and installation of the valves within this test bed is provided for this investigation. Valve test results from this study assessed Cv curves as well as multiple actuator cycles, under varying thermodynamic and operational mode conditions, which would be characteristic within a commercial molten salt facility. The results indicate nominal operation for the baseline design, though improved performance and reliability is expected with the full designed FCV.

Functional near-infrared spectroscopy for the assessment and treatment of patients with disorders of consciousness

BackgroundAdvances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.AimThis review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.MethodsA systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.ResultsAnalysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).ConclusionAs a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.

Nonlinear modeling and control of hydrogen generating system

ABSTRACT This work attempts to develop a nonlinear control algorithm for a Proton Exchange Membrane (PEM) electrolyzer unit working as a hydrogen generating system. Initially, the system dynamics is studied using the model equations resulting from first principle balance at the anode and cathode. The system model is split into six ancillaries viz. anode, cathode, membrane, thermal, voltage, and storage. The first principles models are simulated using MATLAB/Simulink and verified with the data available in the open literature. This work is intended to analyze the performance of an observer-based controller for the system. The originality of this work lies in the integration of PEM electrolyzer and hydrogen storage system followed by controlling the electrolyzer temperature and storage system pressure. The system is automated with a nonlinear control strategy known as globally linearizing controller (GLC). An adaptive state observer (ASO) has been formulated to estimate unknown state variables. To select the optimal parameters, optimization algorithms, namely, particle swarm optimization (PSO) and Ali Baba and forty thieves (AFT) have been used. For the comparative controller performance, the closed-loop simulation of ASO-GLC has been performed. Upon comparing the closed-loop system response, the GLC performance is found better than a well-tuned PI controller.

Effect of intermittent ratio on the performance of a closed-loop heat pump drying system with a novel ternary refrigerant and on the drying characteristics of corn kernels

Effect of intermittent ratio on the performance of a closed-loop heat pump drying system with a novel ternary refrigerant and on the drying characteristics of corn kernels

Electrochemical biosensors for depression: Diagnosis and therapeutic monitoring.

Electrochemical biosensors have revolutionized the detection of biomarkers related to depression and the quantification of antidepressant drugs. These biosensors leverage nanomaterials and advanced assay designs to achieve high sensitivity and selectivity for clinically relevant analytes. Key neurotransmitters implicated in depression, such as serotonin, dopamine, and glutamate, can be accurately measured via biosensors, providing insights into the effects of antidepressant treatments on neurotransmission. Biosensors can also detect biomarkers of inflammation, oxidative stress, and neuronal health that are altered in depression. Real-time biosensing techniques such as fast-scan cyclic voltammetry enable monitoring of dynamic neurotransmitter changes during depressive episodes and pharmacological interventions. Advancements incorporating graphene, gold nanoparticles, and other nanomaterials have enhanced biosensor performance, enabling the detection of low biomarker concentrations. Closed-loop biosensing systems hold promise for precision medicine by automating antidepressant dosage adjustments on the basis of neurotransmitter levels. A wide range of depression biomarkers, including apolipoprotein A4, heat shock protein 70, brain-derived neurotrophic factor, microRNAs, proteins, and combinatorial biomarker panels, have been detected via sophisticated biosensor platforms. Emerging biosensors show selectivity for antidepressant drugs such as serotonin-norepinephrine reuptake inhibitors, tricyclic antidepressants, and selective serotonin reuptake inhibitors in biological samples. This review emphasizes the transformative potential of electrochemical biosensors in combating depression. By facilitating earlier and more accurate diagnoses, these biosensors can revolutionize patient care and enhance treatment outcomes.

Feasibility study of emotion mimicry analysis in human–machine interaction

Health apps have increased in popularity as people increasingly follow the advice these apps provide to enhance physical and mental well-being. One key aspect of improving neurosensory health is identifying and expressing emotions. Emotional intelligence is crucial for maintaining and enhancing social interactions. In this context, a preliminary closed-loop feedback system has been developed to help people project specific emotions by altering their facial expressions. This system is part of a research intervention aimed at therapeutic applications for individuals with autism spectrum disorder. The proposed system functions as a digital mirror, initially displaying an animated avatar’s face expressing a predefined emotion. Users are then asked to mimic the avatar’s expression. During this process, a custom emotion recognition model analyzes the user’s facial expressions and provides feedback on the accuracy of their projection. A small experimental study involving 8 participants tested the system for feasibility, with avatars projecting the six basic emotions and a neutral expression. The study results indicated a positive correlation between the projected facial expressions and the emotions identified by participants. Participants effectively recognized the emotions, with 85.40% accuracy demonstrating the system’s potential in enhancing the well-being of individuals. The participants were also able to mimic the given expression effectively with an accuracy of 46.67%. However, a deficiency in the performance of one of the expressions, surprise, was noticed. In the post processing, this issue was addressed and model enhancements were tailored to boost the performance by ~ 30%. This approach shows promise for therapeutic use and emotional skill development. A further wider experimental study is still required to validate the findings of this study and analyze the impact of modifications made.

Microneedles at the Forefront of Next Generation Theranostics.

Theranostics, combining therapeutic and diagnostic functions, marks a revolutionary advancement in modern medicine, with microneedle technology at its forefront. This review explores the substantial developments and multifaceted applications of microneedles, which have evolved from basic transdermal drug delivery devices to sophisticated diagnostic and therapeutic platforms. Microneedles enhance access to biomarkers via interstitial fluid, enabling real-time monitoring of physiological conditions, such as glucose and hormone levels, thus facilitating continuous health tracking. The evolution of microneedle design from solid to dissolvable forms broadens their utility from mere drug delivery to complex sensing and therapeutic applications, including insulin delivery for diabetes management, vaccination, and gene therapy. This paper delves into the integration of microneedles with wearable technologies, highlighting their role in closed-loop systems that combine real-time monitoring with dynamic, precise therapeutic delivery. By addressing gaps in the literature regarding their integrated diagnostic and treatment capabilities, this review underscores the pivotal role of microneedles in personalizing medicine. It concludes with a visionary perspective on the future trajectory of microneedle technology, emphasizing its potential to revolutionize therapeutic strategies through enhanced efficacy, safety, and patient compliance.

Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems

This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.

Finite-time control of switched stochastic delayed neural networks with Φ-dependent average dwell time

This study focuses on the finite-time stability analysis and control for a class of switched stochastic delayed neural networks (SSDNNs), introducing the [Formula: see text]-dependent average dwell time ([Formula: see text]DADT) switching method as a novel approach for analyzing the finite-time stability of switched stochastic neural networks with time-varying delay. By applying [Formula: see text]DADT and multiple Lyapunov–Krasovskii functionals, some sufficient conditions for the finite-time stability are established. Then, a generalized partially delay-dependent controller is designed to ensure that the closed-loop system achieves stability. Finally, a numerical example is provided to illustrate the effectiveness.

Sustainable supply chain practices: Driving efficiency, reducing waste, and promoting circular economy models

As the world increasingly focuses on sustainability, supply chain management has become a vital lever for the realization of environmental, social, and economic goals. Also, linear supply chain models that are traditional in nature (such as extract, consume, and dispose) lead to serious environmental degradation along with an enormous amount of resource depletion. On the other hand, sustainable supply chain initiatives focus on waste reduction, resource efficiency, and circular economy approaches that prioritize reuse, recycling, and recovery. Integrating sustainability into these supply chain strategies can improve operational efficiency, drive costs down, and meet stakeholder expectations for business environmental stewardship. These strategies involve implementing green procurement practices, optimizing logistics to minimize their carbon footprints, and incorporating new technological solutions (like IoT and blockchain) to improve sustainability transparency and traceability. These practices allow organizations to trace materials throughout their lifecycle, support accountability, and promote closed-loop systems supporting circularity. In addition, the collaboration between stakeholders will determine sustainable initiatives; suppliers, manufacturers, and consumers to name a few. Implementing sustainable supply chain practices is not without its challenges, including high initial costs, technological barriers, and complex regulatory landscapes. Solving for these issues takes policy incentives, investment in innovation, and stakeholder engagement. The role of the key findings and challenges of sustainable strategies for supply chain practices in the development of a circular economy model. By examining case studies as well as emerging trends, it provides key takeaways for organizations striving to navigate the delicate balance between efficiency and sustainability, with the end goal of fostering a resilient and eco-conscious future for all.

Closed-loop systems for plants expressing animal proteins: a modernized framework to safeguard the future of agricultural innovation

Closed-loop systems for plants expressing animal proteins: a modernized framework to safeguard the future of agricultural innovation

Control Co-Design with Performance-Robustness Trade-Off using Tube-Based Stochastic Model Predictive Control

Abstract Control Co-Design (CCD) has been demonstrated to achieve superior solutions for closed-loop systems. However, limited work has addressed CCD problems under probabilistic disturbances. This paper addresses this gap by formulating a finite-horizon optimal control problem with chance constraints and proposing a novel CCD approach. This approach integrates tube-based Stochastic Model Predictive Control with constraint-tightening techniques to optimize performance and robustness while preventing instability and infeasibility. A nested CCD framework is introduced, along with a constrained multi-objective optimization algorithm that enables the performance-robustness trade-off. A method for quantifying the robustness of closed-loop systems under stochastic disturbances is presented. The proposed CCD approach is demonstrated on a numerical example and an engineering case of the satellite attitude control system. Results show that CCD can generate more well-spread Pareto fronts that cannot be reached by other design strategies. This helps designers explore more potential solutions with different dynamic characteristics. Selected non-dominated solution trajectories are visualized for qualitative comparisons. Future work will extend this to nonlinear applications.

Feedback linearization-based model reference adaptive control for multirotor UAVs under uncertainties from fuel and payload

This paper proposes a feedback linearization-based model reference adaptive control (FL-MRAC) scheme to handle a multirotor unmanned aerial vehicle (UAV) equipped with a gasoline-battery-type hybrid system under uncertainties. The gasoline-battery-type hybrid propulsion systems are increasingly used to increase operational time. This multirotor system suffers from uncertainties that may originate from offcentered payloads, or fuel consumption during flight. These uncertainties can change model parameters such as mass, moment of inertia, and center of gravity. Therefore, a new control strategy is applied using feedback linearization as a baseline design augmented with a model reference adaptive control strategy on a multirotor UAV. To formulate the structure of uncertainties, the mathematical model of the multirotor UAV with fuel and payload is constructed considering the effect of the mass change rate and the off-diagonal inertia tensor. FL-MRAC improves the performance of attitude and altitude tracking using the closed-loop reference model under model parameter uncertainties and time-varying nature. To guarantee stability, the uniform ultimate boundedness of the closed-loop system is proved considering the time-varying parameters. Numerical simulations are performed using the root-mean-square error and the fast Fourier transform. In Case 1, FL-MRAC with a closed reference model exhibits a more accurate tracking performance and transient response than FL-MRAC with an open reference model under the uncertainties of time-varying parameter uncertainties. In Case 2, the proposed method of tracking performance demonstrates an improved attitude and altitude tracking performance compared to sliding mode control and nonlinear disturbance observer under time-varying parameter uncertainties and external disturbances due to fuel sloshing effect.

Synchronization control of complex networks with bit-rate constraint via dynamic event-triggered strategy

A sampling-based dynamic event-triggered strategy is proposed for the synchronization control of continuous complex networks with uncertainties and delayed coupling, taking into account a bit-rate constraint in node communication. First, a synchronization error system is established by integrating the dynamics of complex networks with those of an isolated node. To conserve limited network bandwidth resources, a sampling-based dynamic event-triggered strategy is designed. By introducing bit-rate constraints to characterize the communication limitations of the system, the available bit rate for each node is allocated using an average allocation protocol. Subsequently, a binary encoding–decoding method is developed to transmit the triggered data, and the decoded signals are utilized to design the controller. Then, a closed-loop synchronization error system is derived. Given that coding errors are inevitable, an exponentially ultimately bounded definition of synchronization error is introduced. The coupling and transmission delays within the system are addressed by constructing Lyapunov-Krasovskii (L-K) functions. Utilizing Lyapunov stability theory and other technical theorems, the exponentially ultimately bounded condition for the closed-loop system is obtained. Based on this condition, the co-design of the controller gain and triggering parameters is achieved. In addition, the explicit relationship between synchronization error and bit rate is presented. Finally, the effectiveness of the proposed method is validated through three numerical examples.

Adaptive-constrained admittance control for physical human–robot interaction

In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments.

Almost Disturbance Decoupling Control Strategy for a Class of Underactuated Nonlinear Systems with Disturbances

This paper introduces an almost disturbance decoupling approach to address the disturbance problem for a class of underactuated nonlinear systems. First, a residual system is constructed using a virtual controller and partial differential equations. Next, the unknown disturbances are addressed using an almost disturbance decoupling approach, where Young’s inequality is applied to handle the disturbance terms in the residual system. An almost disturbance decoupling controller is designed for the residual system. The locally asymptotic stability of the closed-loop system is rigorously proven using Lyapunov’s theorem. Finally, simulation results on an IWP system with unknown disturbances and experimental results on an overhead crane are presented to validate the effectiveness of the proposed almost disturbance decoupling control method.

Data-Based Spacecraft Rendezvous Control by SINDy

In this paper, we demonstrate a new model identification-based control method for spacecraft rendezvous with limited amount of data of data. We use the method of sparse identification for nonlinear dynamics (SINDy) to identify the high-order nonlinear gravitational acceleration in the motion dynamics of spacecraft rendezvous. The control has a feedforward plus feedback structure. The feedforward control uses the nonlinear functions identified by SINDy to compensate the nonlinear high-order acceleration terms, while the feedback control designed by solving a standard linear quadratic regulator (LQR) problem is used to guarantee the stability of the closed-loop system. Numerical results show the effectiveness of the proposed control scheme for spacecraft rendezvous mission. With few state samplings, the dynamics identified based on SINDy achieve better state prediction performance than Clohessy–Wiltshire (CW) equations and the dynamics identified by BP neural networks. Considering the computation and storage burden brought by the data amount in identification, the proposed method is promising in improving the autonomy of spacecraft in missions with complex and uncertain dynamics environment.

Applications of Implantable Microelectrodes in Brain-Computer Interfaces

Implantable microelectrodes represent a crucial advancement in Brain-Computer Interfaces (BCIs), enabling a wide array of applications across neuroscience and clinical domains. This paper presents a comprehensive overview of implantable microelectrodes, and three kinds of microelectrode arrays are explained in detail. The paper also deals with the integration of microelectrodes with BCI, with an emphasis on critical areas such as neuromodulation, including deep brain stimulation and neural feedback control, motor recovery, and three varieties of clinical applications in people with Parkinson's disease, epilepsy and amputees. Additionally, this work delves into the advanced hardware enhancing application of Implantable microelectrodes. This includes the development of microelectrode arrays, closed-loop systems, and the latest materials and microfabrication techniques. Despite the challenges in data processing, managing immune responses, and optimizing electrode-tissue interfaces, implantable micro-electrodes offer tremendous potential for advances in neuroscience, medicine, and rehabilitation engineering, paving the way for enhanced understanding and treatment of neurological disorders.

Exploring the Effectiveness of Continuous Glucose Monitoring in Improving Quality of Life and Reducing Hypoglycemic Episodes in Children with Type 1 Diabetes: A Narrative Review

Type 1 diabetes mellitus (T1DM) poses unique challenges in pediatric populations, requiring meticulous glycemic management to mitigate risks such as hypoglycemia and long-term complications. Continuous glucose monitoring (CGM) has emerged as a transformative tool, providing real-time data to support proactive diabetes management. This narrative review evaluated the effectiveness of CGM in improving quality of life (QoL) and reducing hypoglycemic episodes among children with T1DM. A comprehensive literature review methodology was employed to synthesize evidence on CGM’s impact, challenges, and future potential. CGM enhances glycemic control by providing continuous data streams and predictive alarms, addressing the limitations of traditional self-monitoring methods. Psychosocial benefits include reduced anxiety, greater independence, and improved communication between families and healthcare providers. However, barriers such as cost, adherence, and accessibility impede its widespread adoption. Emerging technologies, including artificial intelligence integration and advanced hybrid closed-loop systems, promise to overcome these challenges, further enhancing CGM’s usability and accuracy. Future research should explore CGM’s long-term outcomes, including its impact on psychological well-being and academic performance, while prioritizing equitable access. Addressing these factors will enable CGM to fulfill its potential as a cornerstone of pediatric diabetes management, significantly improving the lives of children with T1DM and their families. Keywords: Continuous Glucose Monitoring (CGM), Type 1 Diabetes Mellitus (T1DM), Pediatric Diabetes Management, Quality of Life (QoL), Hypoglycemic Episodes.