Optimal Control Research Articles

Feasibility Study of Efficient and Adaptive Interval Type-2.0 Fuzzy Logic Controller for Hybrid Electric Vehicle Performance Optimization

Feasibility Study of Efficient and Adaptive Interval Type-2.0 Fuzzy Logic Controller for Hybrid Electric Vehicle Performance Optimization

Comparative Efficacy of Dexlansoprazole, Pantoprazole, Esomeprazole, and Rabeprazole in Achieving Optimal 24-Hour Intragastric pH Control: A Randomized Crossover Study Using Ambulatory pH Monitoring.

Proton pump inhibitors (PPIs) regulate gastric acid reflux. Dexlansoprazole's efficacy in prolonging acid suppression compared to conventional PPIs and placebo requires evaluation. A prospective, randomized, placebo-controlled, five-way crossover pilot study was conducted on healthy volunteers comparing the potency of dexlansoprazole to conventional PPIs in which five patients were randomized into five treatment cohorts, including dexlansoprazole 60 mg, pantoprazole 40 mg, esomeprazole 40 mg, rabeprazole 20 mg, and placebo, assessing 24-hour intragastric pH using Z/pH Recorder (ZepHr®, Diversatek, Inc., Milwaukee, WI) and analyzing statistical differences via paired t-test. Dexlansoprazole showed significantly longer durations with pH > 4.0 compared to placebo(P < 0.001) and all other PPIs (P < 0.05) over 24 hours. Although not significant in the first 0-12-hour period, dexlansoprazole maintained significantly higher pH levels in the last 12-24-hour period compared to pantoprazole (P = 0.001) and esomeprazole (P = 0.044) but not with rabeprazole (P = 0.075). Additionally, during the 24-hour pH monitoring measured at 30-minute intervals, dexlansoprazole (mean pH = 3.98 ± 0.11) consistently showed higher values than pantoprazole (mean pH = 3.48 ± 0.12), rabeprazole (mean pH = 3.66 ± 0.05), esomeprazole (mean pH = 3.66 ± 0.05), and placebo (mean pH = 2.52 ± 0.12), indicating its superior potency. Dexlansoprazole's dual-delayed release mechanism demonstrates superior acid suppression compared to traditional PPIs and placebo in this pilot study. Larger studies are needed to further evaluate its long-term efficacy and safety.

Social determinants of health and diabetes self-care management in South Africa

ObjectiveDiabetes is an incapacitating condition affecting millions of people in South Africa. Maintaining optimal glycaemic control is crucial in preventing diabetes complications, highlighting the importance of diabetes self-care. This study examined how Social Determinants of Health (SDoH) are associated with self-care management practices in individuals with diabetes in South Africa using the framework developed by the Healthy People 2020 initiative.MethodsThis study utilised cross-sectional Project Mind baseline data collected in 2017. Self-care management was coded on a scale from ‘0’ (never) to ‘7’ (daily adherence). For analysis, this scale was dichotomised into two categories: low self-care (scores 0–5) and high self-care (scores 6–7). Furthermore, adherence with these daily self-care activities was categorised into three levels: no adherence, partial adherence (inconsistent or partial adherence to activities), and full adherence (consistent adherence to all self-care activities).ResultsThe analytical sample (n = 539) was predominantly female (76%), with a mean age of 54 years, urban residents (60%), unemployed (70%), and attained secondary education (11.3%). In determining the attainment of a higher scale of self-care, age (AOR = 1.02, CI=[0.99,1.05]) and secondary education (AOR = 1.13, CI=[1.02, 2.03]) were associated with an increase in the scale of self-care. Conversely, urban residency (AOR = 0.50, CI=[0.29,0.88]) and being obese (AOR = 0.43, CI=[0.19,1.00]) were associated with a lower scale of self-care. Although not statistically robust, food insecurity decreased while being a woman and having a stable house showed an increased association. Travelling longer distances to access healthcare was positively associated with no adherence, and urban residency has a negative association with full adherence relative to partial adherence.ConclusionsThe associations between SDoH and diabetes self-care management within a South African context highlight the need for a more holistic understanding and approach to interventions. Future endeavours should examine these determinants more broadly and formulate integrative strategies to ameliorate diabetes self-care.

Emerging RNAi Therapies to Treat Hypertension.

Hypertension (HTN), often dubbed the "silent killer," poses a significant global health challenge, affecting over 1.3 billion individuals. Despite advances in treatment, effective long-term blood pressure (BP) control remains elusive, necessitating novel therapeutic approaches. Poor control of BP remains a leading cause of cardiovascular morbidity and mortality worldwide and is becoming an even larger global health problem due to the aging population, rising rates of obesity, poorer dietary patterns and overall cardiometabolic health, and suboptimal rates of patient adherence and optimal BP control. Ribonucleic acid interference (RNAi) technology, which leverages the body's natural gene-silencing mechanism, has emerged as a promising strategy for several diseases and has recently been tested for its antihypertensive effects. We systematically reviewed peer-reviewed articles from databases including PubMed, EMBASE, and Scopus for studies examining RNAi's role in managing HTN, focusing on mechanisms, clinical utility, and safety profile. Key early-phase trials of some RNAi-leading candidate drugs are detailed. Also highlighted are challenges such as target specificity, delivery mechanisms, durability of effect, and immunogenicity. We conclude by summarizing how RNAi has a significant potential role in HTN therapy due to their unique benefits, such as long-term duration of action, infrequent dosing, and lack of major side effects.

Integrated optimization control strategy for vehicle longitudinal and lateral assisted driving

In order to coordinate the control objectives of the longitudinal and lateral auxiliary driving system of the vehicle and the vehicle dynamics control requirements, this paper takes the four-wheel independent drive electric vehicle as the control object, and combines deep learning and model predictive control to establish the vehicle generalized control force prediction-dynamic optimization distribution control strategy. The upper-level algorithm calculates the generalized control force that meets the vehicle control requirements according to the reference objectives of the longitudinal and lateral auxiliary driving strategies; the middle-level module includes the vehicle expected generalized force neural network prediction model and the active set numerical solution algorithm; the lower level calculates the tire slip rate and sideslip angle according to the expected tire force, realizes the tire force control requirements by controlling the motor torque and the braking system, and adjusts the vehicle movement direction through the steering system. This paper uses simulation tests and hardware-in-the-loop tests to verify the algorithm. The results show that the established algorithm can simultaneously meet the longitudinal and lateral control requirements of the vehicle, with a lateral path following error of 0.0117 m and a vehicle speed error of 0.59 km/h.

ADP-based adaptive control of ship course tracking system with prescribed performance

In this paper, an adaptive dynamic programing (ADP)-based adaptive control problem is studied for the ship course tracking system with prescribed performance. Firstly, the complex sea conditions will bring difficulty to ship course tracking when unmanned ship sailing on sea. In order to overcome this difficulty, a prescribed performance feedforward term is designed in the controller to improve the course tracking ability of ship. Secondly, by defining a cost function, an ADP technology is exploited to achieve an optimal control of ship course tracking system. Finally, the stability of the closed loop system is turned out by Lyapunov stability theory and all signals are uniformly ultimately bounded (UUB). Simulation results validate the effectiveness of the proposed method.

Optimization control strategy for diesel urea-selective catalytic reduction (SCR) urea injection based on the interior point method (IP) + higher order SCR model

In order to ensure that the NOx and NH3 emissions of diesel engines comply with the Euro VII emission standards, this paper optimizes the urea injection using a SCR model combined with the interior point method. First, the equations of the higher-order SCR model were simplified by the variable substitution method and the line method and solved by the backward difference formula to improve computational accuracy. The Levenberg-Marquardt algorithm was used to calibrate the chemical reaction rate parameters to optimize model performance. Second, an optimization cost function was designed to optimize the upstream NH3 concentration in combination with the interior point method and the higher-order SCR model to meet the requirements of Euro VII emission standards. Third, the model effect was verified by performing WHTC hot-start testing on an engine complying with the National VI emission standard. The results show that the average error between the measured NOx concentration and the calculated is 1.49 ppm, which verifies the high accuracy of the model. Finally, the effects of the interior point method, NSGA-II, and MOPSO algorithms in optimizing the upstream NH3 concentration were compared. The results showed that the interior point method outperformed the other algorithms in terms of optimization time and effect, with an optimization time of 149 s, a NOx conversion rate of 97.36 %, and an average downstream NH3 concentration of 4.65 ppm. This achieved a high NOx conversion rate and low NH3 slip in accordance with the requirements of the Euro VII regulation.

Advanced Model with a Trajectory Tracking Stabilisation System and Feasible Solution of the Optimal Control Problem

In this study, we consider the extended optimal control problem and search for a control function in the class of feasible functions for a real control object. Unlike the classical optimal control problem, the control function should depend on the state, not time. Therefore, the control synthesis problem for the initial-state domain should be solved, instead of the optimal control problem with one initial state. Alternatively, an optimal trajectory motion stabilisation system may be constructed. Both approaches—control and trajectory motion stabilisation system syntheses—cannot be applied to real-time control, as the task is too complex. The minimum threshold of quality criteria is searched for in the space of mathematical expression codes. Among other problems, the search space is difficult to define and the gradient is hard to determine. Therefore, the advanced control object model is used to obtain a feasible control function. The advanced model is firstly obtained before solving the optimal control problem and it already includes a trajectory motion stabilisation system; in particular, this stabilisation system is synthesised in advance at the control system design stage. When the optimal control problem appears, it is solved in real time in the classical statement, and a control function is searched for as a function of time. The advanced control object model also uses the reference model to generate the optimal trajectory. The search for the optimal control function is performed in real time and considers the synthesised stabilisation system of motion along a determined trajectory. Machine learning control via symbolic regression, namely, the network operator method, is used to directly solve the control synthesis problem. An example solution of the optimal control problem, with an advanced model moving in the environment with obstacles for a group of two mobile robots, is presented. The obtained solution is a control function for a reference model that generates a trajectory from a class of trajectories stabilised with the object’s control system.

Double-loop importance sampling for McKean–Vlasov stochastic differential equation

This paper investigates Monte Carlo (MC) methods to estimate probabilities of rare events associated with the solution to the d-dimensional McKean–Vlasov stochastic differential equation (MV-SDE). MV-SDEs are usually approximated using a stochastic interacting P-particle system, which is a set of P coupled d-dimensional stochastic differential equations (SDEs). Importance sampling (IS) is a common technique for reducing high relative variance of MC estimators of rare-event probabilities. We first derive a zero-variance IS change of measure for the quantity of interest by using stochastic optimal control theory. However, when this change of measure is applied to stochastic particle systems, it yields a P×d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P \ imes d$$\\end{document}-dimensional partial differential control equation (PDE), which is computationally expensive to solve. To address this issue, we use the decoupling approach introduced in (dos Reis et al. 2023), generating a d-dimensional control PDE for a zero-variance estimator of the decoupled SDE. Based on this approach, we develop a computationally efficient double loop MC (DLMC) estimator. We conduct a comprehensive numerical error and work analysis of the DLMC estimator. As a result, we show optimal complexity of OTOLr-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}\\left( \ extrm{TOL}_{\ extrm{r}}^{-4}\\right) $$\\end{document} with a significantly reduced constant to achieve a prescribed relative error tolerance TOLr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{TOL}_{\ extrm{r}}$$\\end{document}. Subsequently, we propose an adaptive DLMC method combined with IS to numerically estimate rare-event probabilities, substantially reducing relative variance and computational runtimes required to achieve a given TOLr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{TOL}_{\ extrm{r}}$$\\end{document} compared with standard MC estimators in the absence of IS. Numerical experiments are performed on the Kuramoto model from statistical physics.

Data-driven predictor of control-affine nonlinear dynamics: Finite discrete-time bilinear approximation of koopman operator

This paper introduces a novel and efficient data-driven approach for approximating a finite discrete-time bilinear model of control affine nonlinear dynamical systems with a tunable parameter that balances model dimension and prediction accuracy. An approximation of the Koopman operator based on the evolutions of the nonlinear system measurements used to lift a control-affine nonlinear system to a higher dimensional model. However, higher dimensional spaces can result in a long learning time and the curse of dimensionality in control analysis. The proposed approach addresses these challenges by introducing a convex optimization which identifies informative observable functions. This technique allows for the adjustment of a parameter to strike a balance between model dimension and accuracy in prediction. The main contribution of this study is to introduce a reduced dimensional bilinear model for a nonlinear complex system. This achievement is made possible by implementing convex sparse optimization, enabling the exploration of informative estimated Koopman eigenfunctions while minimizing the number of system measurements required. The optimization problem is solved using the alternating direction method of multipliers. The effectiveness of the proposed method is evaluated on three different nonlinear systems: a numerical nonlinear system, a Van der Pol oscillator, and a Duffing oscillator. In the last simulation, an estimation of the Koopman linear model is considered as a special case, and the policy iteration algorithm is employed to evaluate optimal control designed for different reduced-dimensional models.

Research on Decoupling Duty Cycle Optimization Control Method of a Multiport Converter for Dual-Port Direct Drive Wave Power Generation System

Dual-port direct drive wave energy power generation systems (DP-DDWEPGS) have received widespread attention due to their smooth and zero-free output power, compared to single-port direct drive wave energy power generation systems (SP-DDWEPGS) which have the disadvantage of large out-put power fluctuations. To further enhance the performance of the DP-DDWEPGS, optimal power capture control is proposed to achieve maximum power point tracking. Meanwhile, a multiport converter is applied to the DP-DDWEPGS to solve the problem caused by an excessive number of switching devices in the overall system converter. The multiport converter fulfills all the functional requirements of the DP-DDWEPGS while reducing the number of switching devices. However, switch multiplexing of the multiport converter also introduces coupling relationships between each port and the wave force exhibits time-varying characteristics, necessitating advanced control methods with superior fast-tracking capability. Therefore, in this paper, a decoupling duty cycle optimization model predictive control for DP-DDWEPGS is proposed. Based on the characteristics of switching multiplexing, NSC finite control set model predictive control (FCS-MPC) decouples the current prediction and the cost function, reduces the number of candidate voltage vectors in each operation, and shortens the operation time by 70%. To address the issues of high ripple value and increased error due to decoupling in FCS-MPC, duty cycle optimization control is added, greatly reducing the fluctuations in electromagnetic force and power of the permanent magnet linear generator (PMLG). Based on the established simulation model, the feasibility and superiority of the multiport converter and decoupling duty cycle optimization model predictive current control method are verified.

Output feedback fault-tolerant Q-learning for discrete-time linear systems with actuator faults

This paper presents an innovative output feedback fault-tolerant Q-learning algorithm that can be implemented online without relying on explicit system models or fault details. In the face of actuator faults, finding optimal Fault-Tolerant Control (FTC) solutions that can stabilize the faulty system poses significant challenges. The proposed approach is implemented online without the need for system dynamics and actuator fault information. Furthermore, it operates without relying on full state measurements, utilizing only the input-output data of the faulty system. An innovative representation of the output feedback Fault-Tolerant Q-function (FTQF) is established using input-output data. Subsequently, a model-free optimal output feedback FTC policy is obtained from the developed FTQF. Then, a fault-tolerant Q-learning algorithm is formulated to iteratively acquire the optimal FTC policy in real-time, eliminating the necessity for system dynamics and actuator fault information. The proposed algorithm exhibits immunity to excitation noise bias, even without considering a discounting factor. Furthermore, the proposed Q-learning approach is proven to be effective in stabilizing faulty closed-loop system. Finally, the proposed algorithm's efficiency is validated through numerical simulations of F-16 autopilot aircraft dynamics.

Nanozyme-enhanced enzyme control: Computational strategies for precise and sustained enzymatic activity in tissue engineering

Nanomaterials are revolutionizing tissue engineering by significantly enhancing enzyme-mediated chemical processes and cellular activities, thereby advancing the development of more effective therapeutic strategies. This research delves into the intricate dynamics of enzyme diffusion within tissue matrices, providing a comprehensive computational modeling framework aimed at optimizing enzyme concentration control in tissue scaffolds. Through advanced numerical algorithms and computational analysis, the study simulates optimal control strategies for regulating nanozyme levels, ensuring precise and sustained enzymatic activity within the scaffolds. A novel aspect of this work is the integration of videographic records, which offers an enriched understanding of the complex interactions between nanomaterials and biological tissues, providing detailed insights into the system’s operational dynamics. The study bridges the gap between experimental methodologies and theoretical models, aligning with the cutting-edge research in chemical physics and physical chemistry by offering novel approaches to tissue engineering challenges. The findings underscore the critical role of nanomaterials in achieving precise enzymatic control, which is pivotal for the development of advanced biomimetic systems and improved therapeutic outcomes. This work contributes to the ongoing frontier research in physical phenomena within chemistry, biology, and materials science, by providing significant new insights into enzyme delivery mechanisms and their application in biomedical contexts. The research not only highlights the importance of innovative methodologies in tissue engineering but also sets the stage for future experimental validations and potential clinical applications

Designing a Bidirectional Power Flow Control Mechanism for Integrated EVs in PV-Based Grid Systems Supporting Onboard AC Charging

This paper investigates the potential use of Electric Vehicles (EVs) to enhance power grid stability through their energy storage and grid-support capabilities. By providing auxiliary services such as spinning reserves and voltage control, EVs can significantly impact power quality metrics. The increasing energy consumption and the global imperative to address climate change have positioned EVs as a viable solution for sustainable transportation. Despite the challenges posed by their variable energy demands and rising numbers, the integration of a smart grid environment with smart charging and discharging protocols presents a promising avenue. Such an environment could seamlessly integrate a large fleet of EVs into the national grid, thereby optimizing load profiles, balancing supply and demand, regulating voltage, and reducing energy generation costs. This study examines the large-scale adoption of EVs and its implications for the power grid, with a focus on State of Charge (SOC) estimation, charging times, station availability, and various charging methods. Through simulations of integrated EV–PV charging profiles, the paper presents a lookup-table-based data estimation approach to assess the impact on power demand and voltage profiles. The findings include multiple charging scenarios and the development of an optimal control unit designed to mitigate the potential adverse effects of widespread EV adoption.

Global stability and optimal control of an age-structured quit-smoking model with quit-smoking duration

In this paper, we introduce an age-structured quit-smoking model that incorporates the duration of smoking cessation, a crucial parameter related to smoking cessation counseling and disease treatment. We identify the threshold value [Formula: see text], which determines the existence and stability of the smoking-free steady state. Our findings show that the smoking-free steady state is locally and globally stable if [Formula: see text], whereas it becomes unstable, and the unique steady state with present smokers emerges and is locally and globally stable if [Formula: see text]. We propose an optimal control strategy, and our results show its ability to effectively decrease the number of smokers and promote smoking cessation. To validate our findings and to study the influence of varying parameters on the model’s dynamics, numerical simulations are carried out. A concise conclusion is then shared at the end of the paper.

Finite-time optimal control for a class of nonlinear systems with performance constraints via critic-only ADP: Theory and experiments

This paper addresses the optimal control problem within the framework of adaptive dynamic programming (ADP) for a class of nonlinear systems subjected to performance constraints. A new finite-time optimal control scheme is developed to stabilize the system by using the critic-only neural network ADP method. Compared with the existing ADP-based optimal control methods with uniformly ultimately bounded stability, the provided control scheme ensures that the controlled system's state and neural network weight estimation error are finite-time stable. It can ensure optimality, prescribed performance, and finite-time stability of the closed-loop control system simultaneously through an integration of ADP, the prescribed performance control technique, and Lyapunov theory. The designed adaptive neural network weight update law can relax the persisting excitation condition. The proposed control scheme is implemented on a robotic experiment platform to achieve trajectory tracking and verify its effectiveness.

Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem's non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.

Optimal trajectory control for the yaw system vibration and crawling jitter of a wind turbine

Optimal trajectory control for the yaw system vibration and crawling jitter of a wind turbine

Glucose metrics and device satisfaction in adults with type 1 diabetes using different treatment modalities: a multicenter, real-world observational study.

To evaluate glucose metrics, device satisfaction and diabetes impact in adults with type 1 diabetes using different treatment modalities in a real-life setting in Italy. This was a multicentre, nationwide, cross-sectional study. Candidates were consecutively evaluated for eligibility during their routine medical visit at the diabetes centre. Researchers collected comprehensive demographic, socioeconomic, anamnestic and clinical data, and administered the Diabetes Impact and Device Satisfaction scale. From 2021 to 2022, a total of 428 subjects, 45% males, with a median age of 32 years (IQR 23-47) were recruited in 11 participating centres from all over Italy. No differences in age, physical activity, and diabetes impact were found for the different treatment modalities. HCL/AHCL and SAP groups reported higher device satisfaction vs. MDI + SMBG and MDI + CGM (p < 0.001). Subjects treated with HCL/AHCL exhibited significantly higher TIR and significantly lower time spent in hypoglycemia level 1, time spent in hyperglycemia, CV and GMI compared to MDI + CGM, and significantly higher TIR and significantly lower time spent in hypoglycemia level 2, time spent in hyperglycemia, and CV compared to SAP. Significant reduction in hypoglycemia level 2 was also found with PLGM compared to SAP. High education attainment was associated with optimal metabolic control. Real-life use of advanced technologies for type 1 diabetes is associated with improved glucose metrics and device satisfaction. Education level also contributes to success of treatment.

Optimal Speed Control of Hybrid Stepper Motors through Integrating PID Tuning with LFD-NM Algorithm

Optimal Speed Control of Hybrid Stepper Motors through Integrating PID Tuning with LFD-NM Algorithm