Static Control Research Articles

Enriching central memory T cells using novel bioreactor design for T cell manufacturing

BackgroundThe manufacturing of T cell therapies aims to achieve high yields of product with potent phenotypes. We have developed a novel bioreactor, bioreactor with expandable culture area—dual chamber (BECA-D), which has previously demonstrated functionality for scaled T cell manufacturing. Methods and ResultsMethods and Results: In this study, incorporation of a stirring mechanism into the double-chamber bioreactor design was tested to homogenize the media components between the two chambers. In addition to the improved media homogenization, the stirring culture was observed to have higher yield and enrichment of central memory T cells, a T cell subpopulation that has been associated with improved therapeutic efficacy compared with a static control. BECA-D with a stirring mechanism was evaluated for its performance in culturing T cells in comparison with a static control, BECA-D, and an industry benchmark, G-Rex10 (Wilson Wolf Manufacturing). BECA-D with a stirring mechanism was able to preferentially promote the enrichment of central memory T cells compared with the static cultures, indicative of the effect of the stirring mechanism. ConclusionBy achieving high T cell yields with a favorable subpopulation profile, the mechanical method of incorporating stirring into a double-chamber bioreactor such as BECA-D carries great potential as a useful research and manufacturing tool to support advanced T-cell therapy manufacturing.

Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management

In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.

Estimation and Control of Positive Complex Networks Using Linear Programming

This paper focuses on event-triggered state estimation and control of positive complex networks. An event-triggered condition is provided for discrete-time complex networks by which an event-based state estimator and an estimator-based controller are designed through matrix decomposition technology. Thus, the system is converted to an interval uncertain system. The positivity and the L1-gain stability of complex networks are ensured by resorting to a co-positive Lyapunov function. All conditions are solvable in terms of linear programming. Finally, the effectiveness of the proposed state estimator and controller are verified by a numerical example. The main contributions of this paper are as follows: (i) A positive complex network framework is constructed based on an event-triggered strategy, (ii) a new state estimator and an estimator-based controller are proposed, and (iii) a simple analysis and design approach consisting of a co-positive Lyapunov function and linear programming is presented for positive complex networks.

Subtalar joint pronation: Who is the real enemy, presence or severity: A cross-sectional study?

The study aims to compare static and dynamic postural stability, navicular drop, dorsiflexion range of motion, and jumping performance of individuals with neutral, prone, and hyperprone foot postures. Forty-eight participants between the ages of 18 and 40, were categorized into neutral (n=16), prone (n=16), and hyperprone (n=16) according to foot posture index (FPI). Static and dynamic postural control evaluations (with the Biodex Balance System SD), navicular drop test (NDT) weight-bearing lunge test, countermovement jump test without arm swing, and drop vertical jump tests have been completed. In the results, the average age of participants in the NG, PG, and HPG are 22.31 ± 2.75, 23.87± 3.72, and 22.37 ± 1.28 years and BMI are 22.6 ± 3, 23.4 ± 3.8, and 21.4 ± 2.24 (kg/m²), respectively. The demographic data of the participants showed a homogeneous distribution. There were no significant differences in none of the outcomes except the NDT. Navicular drop amount is positively correlated by the subtalar joint pronation. An increase in subtalar joint pronation does not have a significant effect on static and dynamic stability, jump performance, or dorsiflexion range of motion in healthy individuals.

GenesisRM: A state-driven approach to resource management for distributed JVM web applications

Reducing resource waste while maintaining end-to-end latency service-level objective (SLO) by simultaneously managing CPU bandwidth, memory allocation, and pod number of web applications running on Java virtual machine (JVM) is challenging. The challenges stem from the complexity of the multi-type resource allocation optimization problem, the high sensitivity of JVM performance to resource scaling actions, and the lack of low-level resource scaling mechanisms. We present GenesisRM, a resource management framework with a novel state-driven architecture. Specifically, we design a state control model for JVM web applications that encompasses seven pod states. This model serves as an abstraction layer, decoupling the centralized resource management system into a global state manager and distributed pod managers. The state manager controls the state transitions of the pods based on the overall workload, while the pod managers dynamically allocate resources for each pod according to the state and local workload.Then, we propose a multi-frequency control model with two predictive state controllers and a reactive state controller to manage the state of pods based on the state control model. In addition, GenesisRM brings new mechanisms to scale JVM pods’ low-level resources without damaging their performance. We evaluate our work using a real-world JVM web application benchmark in three different scale server clusters of Pengcheng Laboratory Developer Cloud, and the 21-day experimental results show that GenesisRM saves 31.70% CPU and 17.60% memory compared to the best-performing state-of-the-art solutions while guaranteeing the SLO imposed on end-to-end latency.

EXPERIMENTAL ASSESSMENT OF CONSUMER UNBALANCE AND NONLINEARITY EFFECTS IN THREE-PHASE NETWORKS WITHOUT NEUTRAL CONDUCTOR

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

THE EFFECTS OF HYBRID SLIDING MODE LEARNING CONTROL AND FEEDFORWARD ANGLE DROOP CONTROLLER WITH HIGH DROOP GAIN IN HYBRID MICROGRID WITH LOAD UNCERTAINTY AND NONLINEARITY

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

LMI-based state observer design for supercavitating vehicles with time delay

Due to the existence of the planing force, the control of the supercavitating vehicle is difficult, and the vertical velocity which has a great influence on the planing force cannot be directly measured. Therefore, considering the time-delay effect of the planing force and the external disturbance, the state observer and controller based on linear matrix inequality (LMI) have been designed. By decomposing the planing force into two parts of time delay and non-time delay, and a longitudinal plane Linear Parameter Varying (LPV) time-delay model for supercavitating vehicles is derived. In addition, the controller and state observer for the supercavitating vehicle system with time-delay characteristics and external disturbances are simultaneously designed. The gains of the controller and observer are obtained by combining the theory of linear matrix inequalities and convex polytopes, ensuring the convergence of estimation error. At the same time, taking into account the possibility of exceeding the physical limits of the actuator when the input value is too large, the compensator has been added. Finally, the simulation results demonstrate that the designed controller exhibits good stability and tracking performance.

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Visual Deprivation's Impact on Dynamic Posture Control of Trunk: A Comprehensive Sensing Information Analysis of Neurophysiological Mechanisms.

Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion-extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future.

Effect of body position during weaning from total liquid ventilation in piglets

ObjectiveTo determine if change in body position improves oxygen requirements and respiratory mechanics during the transition from total liquid ventilation (TLV) to gas ventilation. MethodsFourteen piglets underwent TLV, followed by a 2-hour weaning period under conventional gas ventilation. Subjects were randomized to the experimental group (Rotating – R), that was in prone position between the 10th and 30th minute of weaning, or to the static control group (Supine – S). ResultsOxygenation index was lower in the R group at 30 minutes in prone position than that in the S group (1.9 [1.6; 2.8] vs 3.5 [3.1; 5.1], p = 0.001). This difference disappeared when subjects resumed the supine position (4.2 [3.8; 4.7] and 4.7 [3.8; 5.4], p = 0.4, for the R and S groups, respectively). The change in body position did not affect respiratory system compliance or inspiratory capacity. ConclusionProne position improved oxygenation during weaning from TLV. The effect disappeared once piglets returned to the supine position.

Limiting Properties of Linear Control Systems, Synthesis by LQR Methods According to Quadratic Criteria with One Variable Coefficient

The work provides a comparative analysis of the properties of optimal control systems for linear stationary single-channel objects, the distinctive feature of which is that the numerator polynomial of the object’s transfer function is Hurwitz. Systems are synthesized by two main methods of the theory of analytical design of optimal regulators (ADOR) — methods the Letov-Kalman and A. A. Krasovsky, which use quality functionals based on an integral criterion containing only two terms: the square of the object’s control signal and the square of its output coordinate with a weighting coefficient q . The limiting properties of the synthesized systems are studied at q q → ∞ . The common known property of these systems, for brevity called the Letov-Kalman and Krasovsky systems, is the property of their stability at the limiting value of the weight coefficient and, accordingly, an infinitely large increase in the total gain of these systems, which for them ensures obtaining a given value static control error. Other analyzed properties of the systems turned out to be significantly different and even mutually opposite. For example, at limiting values q q → ∞ , the coefficients of the optimal Letov-Kalman controller do not depend on the parameters of the object, while the coefficients of the Krasovsky controller, on the contrary, are determined exclusively by the parameters of the object. We also note that, unlike Letov-Kalman control systems, the time of transient processes of Krasovsky systems at q q → ∞ cannot be reduced less than a certain finite value, despite the absence of restrictions on the magnitude of the control signal. In this work, with the aim of combining in one control system the indicated positive, but contradictory properties of the analyzed systems, the so-called combined ADOR method is proposed. Its main idea is to represent the control signal of an object by two terms, which are then sequentially determined by using two main methods of the ADOR theory, and at the first stage of synthesis there is control using the Letov-Kalman method, which ensures the direct application of the combined synthesis method to unstable objects. This synthesis method, when using a quality functional with one variable weight coefficient, allows for the considered control objects of order n m 5 to design systems with given values of the static error and control time, for which overregulation does not exceed 5.2 %.

Applied static analysis and specialization of cross-core syscalls for multi-core AUTOSAR OS

The development of static real-time control systems often follows a closed-world assumption, allowing extensive RTOS-aware whole-program optimization. For single-core systems, previous work could show the high potential of control-flow-aware static system-call tailoring. However, due to an exponential state explosion in the analysis phase, it cannot simply be extended to a multi-core setting, since the core’s relative timing to each other is undetermined. In this work, we present MultiSSE, a multi-core capable and RTOS-aware static whole-system optimization. First, MultiSSE analyzes the system by determining the relative positions of multiple cores only when necessary. For that, it exploits structural control flow and optionally timing information to handle each core separately as much as possible. Based on the analysis result, a synthesis applies lock elision and system-call optimization to generate specialized multi-core real-time systems for AUTOSAR OS. To enable a static prediction of the run-time reduction, we additionally provide cost models for the optimized cross-core system calls and evaluate the approach with synthetic benchmarks and a real-world quadrotor application. MultiSSE was able to optimize or even completely elide costly cross-core system calls and system objects leading to a reduction of up to 14% of a task’s execution time. In this extended version of a conference publication (Entrup et al. 2023), we provide an advanced description, new cost models, and an end-to-end measurement by developing a synthesis complementing the analysis.

Exercise With Unstable Objects: A Descriptive Survey Among Health Care and Fitness Professionals

Exercise with unstable objects is a popular intervention used by health care and fitness professionals. Understanding different professional beliefs and usage patterns might provide insight for future research and development of evidence-based guidelines. The purpose of this survey study was to document the perceptions, beliefs, and the use of exercise with unstable objects among health care and fitness professionals in the United States. Six hundred and sixteen respondents completed the survey. Most respondents used unstable objects for musculoskeletal postinjury fitness/return to performance (72%). Most believed the main therapeutic effects were enhanced motor control (88%), balance (86%), and somatosensory (85%) function. Most reported using the foam pad (80%) and BOSU (84%). The most used subjective measure was the Activities-specific Balance Confidence scale. Most respondents used single limb stance (85%) to measure static motor control and the single leg squat assessment (51%) for dynamic motor control. Respondents used variables such as dynamic movement, repetition and sets, and time during training. Respondents considered recent injury or surgery and neurological or vestibular conditions as the top precautions. Respondents considered acute injury or surgery and neurological or vestibular conditions as the top contraindications. This survey provides insight into health care and fitness professionals perceptions, beliefs, and use of exercise with unstable objects.

ROBUST OUTPUT FEEDBACK CONTROL FOR HETEROGENEOUS AUTONOMOUS VEHICLE PLATOONS

An heterogeneous vehicle platoon controller for autonomous vehicles is proposed in this paper. The traction force of each vehicle is controlled according to the state of the predecessor vehicle, which can be known by LiDAR/RADAR, and the state of the leader vehicle, which is received by vehicle-to-vehicle (V2V) communication. Platoon string stability is evaluated under Lyapunov and H8 conditions. An iterative procedure is proposed in order to deal with the non-convexity of the static output feedback control design problem. Simulation results demonstrate that the proposed heterogeneous vehicle platoon controller achieves a good overall performance without compromising the safety of any vehicle. The separation between each follower and its predecessor is kept in appropriate ranges, always close to the desired reference. Furthermore, the transient errors are not amplificated when propagated downstream the platoon, therefore string stability is assured. Keywords: Autonomous vehicles, heterogeneous vehicle platoon, vehicle platoon control, string stability, output feedback control, robust control, Linear Matrix Inequality

Polynomial Fuzzy Observer-Based Feedback Control for Nonlinear Hyperbolic PDEs Systems.

This article explores the observer-based feedback control problem for a nonlinear hyperbolic partial differential equations (PDEs) system. Initially, the polynomial fuzzy hyperbolic PDEs (PFHPDEs) model is established through the utilization of the fuzzy identification approach, derived from the nonlinear hyperbolic PDEs model. Various types of state estimation and controller design problems for the polynomial fuzzy PDEs system are discussed concerning the state estimation problem. To investigate the relaxed stability problem, Euler's homogeneous theorem, Lyapunov-Krasovskii functional with polynomial matrices (LKFPM), and the sum-of-squares (SOSs) approach are adopted. The exponential stabilization condition is formulated in terms of the spatial-derivative-SOSs (SD-SOSs). Additionally, a segmental algorithm is developed to find the feasible solution for the SD-SOS condition. Finally, a hyperbolic PDEs system and several numerical examples are provided to illustrate the validity and effectiveness of the proposed results.

Adaptive distributed tracking control for Markov jump multiagent systems with a non-strict leader

This paper investigates the leader-following mean square distributed tracking control problem (DTCP) for a Markov jump multiagent systems (MJMASs) with the general linear dynamical model over an undirected graph. The non-strict control input of the leader is non-zero, unknown, and unavailable to any followers. First, the jump characteristics of the system for the single agent is modeled by a continuous Markov chain. Second, in order to address the leader–follower mean square DTCP, the distributed mode-dependent static controller and the fully distributed mode-dependent adaptive controller are designed for all followers based on the information from the agents’ neighbors and itself, respectively. Then, to further analyze the stability of the MJMASs, two categories of decomposed related Lyapunov functions are constructed using Lyapunov stability theory. The sufficient conditions for the existence of the distributed controller are obtained by derivation. Finally, two simulation cases are presented to verify the validity and feasibility of the proposed strategies.

A multilayer neural‐network‐based fault estimation and fault tolerant control scheme for uncertain system

AbstractThis work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions.

Nonlinear thermo-electro-mechanical responses and active control of functionally graded piezoelectric plates subjected to strong electric fields

Functionally graded piezoelectric plates (FGPPs) are often exposed to extreme thermal environments and subjected to large amplitude excitation and strong electric fields. Accurately predicting the nonlinear behaviors of FGPPs under large thermo-electro-mechanical loads remains a considerable challenge. This paper proposes a comprehensive nonlinear coupled analysis model that considers geometric nonlinearity, piezoelectric nonlinearity, and the temperature dependence of piezoelectric parameters. The total Lagrangian incremental finite element equations for FGPPs under thermo-electro-mechanical loads are derived using Hamilton's principle, by employing the first-order shear deformation theory and the von Kármán nonlinear strain-displacement relationship. The accuracy of the model is validated through comparative studies. Based on this multi-nonlinear model, we further investigate the effects of geometric nonlinearity, piezoelectric nonlinearity, and temperature dependence on the nonlinear static bending, dynamic responses, and active control of FGPPs. This study demonstrates that considering these factors enables accurate prediction of the static, dynamic, and active control behaviors of FGPPs.

An In Vitro Orbital Flow Model to Study Mechanical Loading Effects on Osteoblasts.

Flow induced by an orbital shaker is known to produce shear stress and oscillatory flow, but the utility of this model for studying mechanical loading effects in osteoblasts is not well defined. To test this, osteoblasts derived from the long bones of adult male C57BL/6J mice were plated on 6-well plates and subjected to orbital shaking at various frequencies (0.7, 1.4, and 3.3 Hz) for 30 and 60 min in serum-free differentiation media. The shear stress on cells produced by 0.7, 1.4, and 3.3 Hz shaking frequencies were 1.6, 4.5, and 11.8 dynes/cm2, respectively. ALP activity measured 72 h after shaking (orbital flow) showed a significant increase at 0.7 and 1.4 Hz, but not at 3.3 Hz, compared to static controls. Orbital flow-induced mechanical stress also significantly increased (25%) osteoblast proliferation at a 0.7 Hz flow compared to static controls. Additionally, expression levels of bone formation markers Osf2, Hif1a, Vegf, and Cox2 were significantly increased (1.5- to 3-fold, p < 0.05) in cells subjected to a 0.7 Hz flow compared to non-loaded control cells. We also evaluated the effect of orbital flow on key signaling pathways (mTOR, JNK, and WNT) known to mediate mechanical strain effects on osteoblasts. We found that blocking mTOR and WNT signaling with inhibitors significantly reduced (20-30%) orbital flow-induced ALP activity compared to cells treated using a vehicle. In contrast, inhibition of JNK signaling did not affect flow-induced osteoblast differentiation. In conclusion, our findings show that the flow produced by an orbital shaker at a lower frequency is an appropriate inexpensive model for studying the molecular pathways mediating mechanical strain effects on primary cultures of osteoblasts in vitro.