Control Design Research Articles

A Lyapunov-based control design for centralised networked control systems under false-data-injection attacks

ABSTRACT A central processing unit receives data from all agents and transmits control commands in a Networked Control System (NCS) which is centralised. Centralised NCSs have numerous applications in industrial settings due to their efficiency, simplicity and cost-effective design. However, centralised NCSs are vulnerable to false data injection (FDI) attacks. Despite the fact that researchers have developed detection and mitigation defense mechanisms during past several years, most of these methods have focused on systems with linear dynamics. Furthermore, the existing literature only assumes the injection of FDI attacks on measurement signals. In this paper, we assume that an adversary has injected the FDI attack into both state measurements and control signals with nonlinear dynamics while considering communication noises and disturbances. We propose a secure nonlinear control design that mitigates FDI attacks in real time by combining learning and model-based approaches. We used Lyapunov stability analysis to design the controller, estimator and updating laws of the neural network (NN). In addition, we selected a network of two robots with Euler–Lagrange dynamics to illustrate the robustness of the proposed controller and estimator.

Distributed finite-time adaptive consensus output-feedback control of multi-agent systems under switching topologies

The paper investigates the finite-time adaptive consensus control of nonlinear multi-agent systems (MASs) by output-feedback. During the process of control design, the fuzzy logic system (FLS) is employed to approximate nonlinear function, as well as constructing a fuzzy state observer to estimate unmeasured states. Combining the dynamic surface control (DSC) with adaptive backstepping method conquers the complexity explosion issue. Then, semi-global practical finite-time stability (SGPFS) for closed-loop system is obtained under arbitrary switching communication topologies. All the signals of the overall system are uniformly ultimately bounded (UUB). Finally, the availability of developed control approach will be demonstrated through a simulation.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Weighted metrics are required when evaluating the performance of prediction models in nested case–control studies

BackgroundNested case–control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data.MethodsWe proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design.ResultsPerformance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58–0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62–0.69) and 0.65 (0.61–0.69), respectively. The unweighted O/E ratio was 18.38 (17.67–19.06) in the NCC datasets, while it was 1.69 (1.42–1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53–1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment.ConclusionsNested case–control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.

The Effect of Using Context-Based Learning Videos on Fluid Materials on Students Critical Thinking Ability

Starting from the learning media used in schools not being able to prepare students to think critically, using context-based learning videos is one solution to improve students' critical thinking abilities. This research aims to see the effect of context-based learning videos on fluid material on the essential thinking abilities of class XI students at SMA Negeri 5 Solok Selatan. This study uses a quasi-experiment with a posttest-only control design. The research population was all class XI IPA students at SMAN 5 Solok Selatan registered in the 2022/2023 academic year. Purposive sampling was chosen as a sampling technique, so classes XI IPA 1 and XI IPA 2 were taken as research samples. The instrument used to obtain critical thinking ability data was a written test using 8-item essay questions. Normality, homogeneity, and t-tests were used in the data analysis. The measurement results show that experimental class students (80,38) have higher critical thinking skills than the control class (70,56). Statistical analysis with the t-test shows that that tcount > ttable (3,41 > 2.006) means the working hypothesis is accepted. So, it can be concluded that using context-based learning videos on fluid material affects students' critical thinking abilities.

LQR and Fuzzy-PID Control Design on Double Inverted Pendulum

Double inverted pendulum is a non-linear and unstable system. Double inverted pendulum can be stabilized in the upright position by providing control to the system. In this research we compare two types of controllers namely Linear Quadratic Regulator (LQR) and Fuzzy-PID. The objective is to determine the control strategy that provides better performance on the position of the cart and pendulum angle. We modelled the system which is then linearized and given control. From the simulation results, it is proven that LQR and Fuzzy-PID controllers have been successfully designed to stabilize the double inverted pendulum. However, when given a disturbance in the form of noise step, the LQR controller has not been able to achieve the desired reference for up to 20 seconds. In another hand, the Fuzzy-PID controller is able to achieve the desired reference after 8 seconds. Therefore, it can be concluded that the Fuzzy-PID controller when applied to the Double Inverted pendulum system has better performance than the LQR controller.

SCADA implementation in microhydro power plant control and monitoring systems of screw turbine type

Screw turbine-type micro-hydro power plants are still controlled manually by humans when there is a change in the electrical load on the generator output. If there is a change in the electrical load, the generator output voltage will change, thus affecting the rotation of the turbine being used. In this case, humans are needed to manually regulate the flow of water entering the turbine to maintain the stability of the generator output voltage by controlling the sluice gate. The water discharge entering the screw turbine will rotate the generator through a belt/gearbox pulley transmission, thus producing an electrical voltage at the generator output. To maintain the stability of the generator output voltage, it is necessary to control the water discharge entering the screw turbine. The expected goal of this research is to design a control system for a screw turbine type microhydro power plant to stabilize the generator output voltage and monitor the output of a screw turbine type generator using a SCADA (Supervisory Control, and Data Aquisition) system. The method used is the research and development method, namely the system design stage based on secondary data that has been collected, where the system design includes hardware design and software design, followed by making hardware such as control panels and devices. Software, which includes display design and leader diagrams and testing, is carried out by testing the system on software, hardware, and overall system testing. From the results of the discussion and analysis, the screw turbine type microhydro power plant control design system was created, a load control system consisting of 6 groups and sluice gate opening control that uses an ultrasonic sensor to determine the water level. With the results of controlling the sluice gate at a door opening of 30 mm, the generator voltage is 18.42 Volts DC, DC current is 0.0 Ampere at a load of 0 watts, when the load is 30 watts the generator voltage decreases by 18.40 volts, current is 1.37 Ampere. Through the on-line monitoring system, electrical parameters are obtained which are displayed on the SCADA system by looking in real-time at the history of DC voltage and DC current, DC power, and DC energy generated by the screw turbine-type DC generator. The voltage read is 18. 57 volts, current 1.37 Amperes, DC electrical power is 25.4 watts with a DC light load of 30 watts.

Genetic Liability to Cardiovascular Disease, Physical Activity, and Mortality: Findings from the Finnish Twin Cohort.

We investigated whether longitudinally assessed physical activity (PA) and adherence specifically to World Health Organization PA guidelines mitigates or moderates mortality risk regardless of genetic liability to cardiovascular disease (CVD). We also estimated the causality of the PA-mortality association. The study used the older Finnish Twin Cohort (FTC) with 4,897 participants aged 33-60 years (54.3% women). Genetic liability to coronary heart disease, systolic and diastolic blood pressure was estimated with polygenic risk scores (PRSs) derived from the Pan-UK Biobank (N ≈ 400,000; > 1,000,000 genetic variants). Leisure-time PA was assessed with validated and structured questionnaires three times during 1975-1990. The main effects of adherence to PA guidelines and the PRS × PA interactions were evaluated with Cox proportional hazards models against all-cause and CVD mortality. A co-twin control design with 180 monozygotic twin pairs discordant for meeting the guidelines was used for causal inference. During the 17.4-year (mean) follow-up (85,136 person-years), 1,195 participants died, with 389 CVD deaths. One standard deviation higher PRSs were associated with a 17%-24% higher CVD mortality risk but not with all-cause mortality except for the PRS for diastolic blood pressure. Adherence to PA guidelines did not show significant independent main effects or interactions with all-cause or CVD mortality. Twins whose activity levels adhered to PA guidelines over a 15-year period did not have statistically significantly reduced mortality risk compared to their less active identical twin sibling. The findings were similar among high, intermediate, and low genetic risk levels for CVD. The genetically informed FTC data could not confirm that adherence to PA guidelines either mitigates or moderates genetic CVD risk or causally reduces mortality risk.

An examination of executive functioning in adolescents with FASD in a self-regulation intervention

Adolescents with Fetal Alcohol Spectrum Disorder (FASD) often have challenges with executive functioning (EF), which impacts their ability to self-regulate. In this study, 23 adolescents with FASD completed a self-regulation intervention. The intervention was a manualized Teen Adaptation of the Alert Program®. A nonrandomized waitlist control design was used, and participants completed pre- and post-testing using performance-based measures of EF, and rating scales of EF were completed by caregivers. Results were analyzed three ways; 1) intervention and waitlist control group comparison, 2) whole sample pre-and post- test comparison, and 3) using Reliable Change Indexes to examine individual-level clinically relevant changes. No significant intervention effects were found when comparing the intervention and waitlist control groups. A significant difference was found on a measure of verbal inhibition when total sample pre-and post-test scores were compared. Using Reliable Change Index analysis, 30% participants showed reliable change in the direction of improvement on direct measures of EF, and 57% demonstrated reliable change in the direction of improvement on rating scales. This research study underscores the importance of investigating both individual and group level changes when analyzing data, as well as using reliable change to understand clinically meaningful effects that may be otherwise masked. These findings highlight the potential of the SR intervention to positively impact EF in adolescents with FASD. This study contributes to the growing literature that demonstrates the potential of individuals with FASD to benefit from direct intervention.

Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers

Incremental nonlinear dynamic inversion (INDI) is a widely used approach to controlling UAVs with highly nonlinear dynamics. One key element of INDI-based controllers is the control allocation realizing pseudo controls using available actuators. However, the tracking of commanded pseudo controls is not the only objective considered during control allocation. Since the approach only works locally due to linearization and the solution is often ambiguous, additional aspects like control efforts or penalizing the deviation of certain states must be considered. Conducting the control allocation by solving a quadratic program this results in a considerable number of weighting parameters, which must be tuned during control design. Currently, this is conducted manually and is therefore time consuming. An automated approach for tuning these parameters is therefore highly beneficial. Thus, this paper presents and evaluates a model-based approach automatically tuning the control allocation parameters of a tiltrotor VTOL using an optimization algorithm. This optimization algorithm searches for optimal parameters minimizing a cost functional that reflects the design target. This cost functional is calculated based on a test mission for the VTOL which is conducted within a simulation environment. The test mission represents the common operating range of the VTOL. The simulation environment consists of an aircraft model as well as a model of the INDI-based controller which is dependent on the control allocation parameters. On this basis, model-based optimization is conducted and the optimal parameters are identified. Finally, successful real-world tests on a 4-degrees-of-freedom testbench using the identified parameters are presented. Since the control allocation parameters can significantly influence the aircraft’s stability, the 4-DOF testbench for the aircraft is required for rapid validation of the parameters at a minimum amount of risk.

Low-Temperature Nanosecond Laser Process of HZO-IGZO FeFETs toward Monolithic 3D System on Chip Integration.

Ferroelectric field-effect transistors (FeFETs) are increasingly important for in-memory computing and monolithic 3D (M3D) integration in system-on-chip (SoC) applications. However, the high-temperature processing required by most ferroelectric memories can lead to thermal damage to the underlying device layers, which poses significant physical limitations for 3D integration processes. To solve this problem, the study proposes using a nanosecond pulsed laser for selective annealing of hafnia-based FeFETs, enabling precise control of heat penetration depth within thin films. Sufficient thermal energy is delivered to the IGZO oxide channel and HZO ferroelectric gate oxide without causing thermal damage to the bottom layer, which has a low transition temperature (<250 °C). Using optimized laser conditions, a fast response time (<1 µs) and excellent stability (cycle >106, retention >106 s) are achieved in the ferroelectric HZO film. The resulting FeFET exhibited a wide memory window (>1.7V) with a high on/off ratio (>105). In addition, moderate ferroelectric properties (2·Pr of 14.7 µCcm-2) and pattern recognition rate-based linearity (potentiation: 1.13, depression: 1.6) are obtained. These results demonstrate compatibility in HZO FeFETs by specific laser annealing control and thin-film layer design for various structures (3D integrated, flexible) with neuromorphic applications.

Backstepping Synchronization Control for Four-dimensional Chaotic System Based on DNA Strand Displacement

Abstract Backstepping control is an important nonlinear control design method, which realizes the control of complex systems by constructing control law step by step, and has significant advantages for dealing with complex nonlinear systems. This article proposes a synchronization technique for four-dimensional chaotic systems using a combination of backstepping control method and DNA strand displacement technology. By relying on theoretical knowledge of DNA molecules, five basic chemical reaction modules such as trigger reaction, reference reaction, catalytic reaction, annihilation reaction and degradation reaction are given to construct a four-dimensional DNA chaotic system. On the basis of the relevant theory of chaotic dynamics, the constructed system is analyzed by Lyapunov exponent diagram and spectral entropy complexity algorithm, and the results come to the conclusion that the system reveals extremely complex and varied dynamic behaviors. Combining DNA strand displacement technology with backstepping control method, four controllers are developed to ensure that the trajectories of two homogeneous chaotic systems are synchronized. The numerical simulation results validate the feasibility and applicability of the proposed method. The method proposed in this paper may provide some references in the field of DNA molecular chaos synchronization control.

Comprehensive genetic analysis of facioscapulohumeral muscular dystrophy by Nanopore long-read whole-genome sequencing

BackgroundFacioscapulohumeral muscular dystrophy (FSHD) is a high-prevalence autosomal dominant neuromuscular disease characterized by significant clinical and genetic heterogeneity. Genetic diagnosis of FSHD remains a challenge because it cannot be detected by standard sequencing methods and requires a complex diagnosis workflow.MethodsWe developed a comprehensive genetic FSHD detection method based on Oxford Nanopore Technologies (ONT) whole-genome sequencing. Using a case–control design, we applied this procedure to 29 samples and compared the results with those from optical genome mapping (OGM), bisulfite sequencing (BSS), and whole-exome sequencing (WES).ResultsUsing our ONT-based method, we identified 59 haplotypes (35 4qA and 24 4qB) among the 29 samples (including a mosaic sample), as well as the number of D4Z4 repeat units (RUs). The pathogenetic D4Z4 RU contraction identified by our ONT-based method showed 100% concordance with OGM results. The methylation levels of the most distal D4Z4 RU and the double homeobox 4 gene (DUX4) detected by ONT sequencing are highly consistent with the BSS results and showed excellent diagnostic efficiency. Additionally, our ONT-based method provided an independent methylation profile analysis of two permissive 4qA alleles, reflecting a more accurate scenario than traditional BSS. The ONT-based method detected 17 variations in three FSHD2-related genes from nine samples, showing 100% concordance with WES.ConclusionsOur ONT-based FSHD detection method is a comprehensive method for identifying pathogenetic D4Z4 RU contractions, methylation level alterations, allele-specific methylation of two 4qA haplotypes, and variations in FSHD2-related genes, which will all greatly improve genetic testing for FSHD.

F rugal L ight : Symmetry-Aware Cyclic Heterogeneous Intersection Control using Deep Reinforcement Learning with Model Compression, Distillation and Domain Knowledge

Developing countries need to better manage fast increasing traffic flows, owing to rapid urbanization. Else, increasing traffic congestion would increase fatalities due to reckless driving, as well as keep vehicular emissions and air pollution critically high in cities like New Delhi. State-of-the-art traffic signal control methods in developed countries, however, use expensive sensing, computation, and communication resources. How far can control algorithms go, under resource constraints, is explored through the design and evaluation of FrugalLight (FL) in this article. We also captured and processed a real traffic dataset at a busy intersection in New Delhi, India, using efficient techniques on low cost embedded devices. This dataset ( https://delhi-trafficdensity-dataset.github.io ) contains traffic density information at fine time granularity of one measurement every second, from all approaches of the intersection for 40 days. FrugalLight ( https://github.com/sachin-iitd/FrugalLight ) is evaluated on the collected traffic dataset from New Delhi and another open source traffic dataset from New York. FrugalLight matches the performance of state-of-the-art Convolutional Neural Network (CNN) based sensing and Deep Reinforcement Learning (DRL) based control algorithms, while utilizing resources less by an order of magnitude. We further explore improvements using a careful combination of knowledge distillation and domain knowledge based DRL model compression, with employing Model-Agnostic Meta-Learning to quickly adapt to traffic at new intersections. The collected real dataset and FrugalLight therefore opens up opportunities for resource efficient RL based intersection control design for the ML research community, where the controller should have limited carbon footprint. Such intelligent, green, intersection controllers can help reduce traffic congestion and associated vehicular emissions, even if compute and communication infrastructure is limited in low resource regions. This is a critical step toward achieving two of the United Nations Sustainable Development Goals (SDG), namely sustainable cities and communities and climate action.

Conditional adaptive time series compensation and control design for multi-axial real-time hybrid simulation

The structural performance of critical infrastructure during extreme events requires testing to understand the complex dynamics. Shake table testing of buildings to evaluate structural integrity is expensive and requires special facilities that can allow for the construction of large-scale test specimens. An attractive alternative is a cyber-physical testing technique known as Real-Time Hybrid Simulation (RTHS), where a large-scale structure is decomposed into physical and numerical substructures. A transfer system creates the interface between physical and numerical substructures. The challenge occurs when using multiple actuators connected with a coupler (i.e., transfer system) to create translation and rotation at the interface. Tracking control strategies aim to reduce time delay errors to create the desired displacements that account for the complex dynamics. This paper proposes two adaptive control methodologies for multi-axial real-time hybrid simulations that improve capabilities for a higher degree of coupling, boundary, complexity, and noise reduction. One control method integrates the feedback proportional derivative integrator (PID) control with a conditional adaptive time series (CATS) compensation and inverse decoupler. The second proposed control method is based on a coupled Model Predictive Control (MPC) with the CATS compensation. The performance of the proposed methods is evaluated using the virtual multi-axial benchmark control problem consisting of a steel frame as the experimental substructure. The transfer system consists of a coupler that connects two hydraulic actuators generating the translation and rotation acting at the joint. Through sensitivity analysis, parameters were tuned for the decoupler components, CATS compensation, and the control design for PID, LQG, and MPC. Comparative results among different control methods are evaluated based on performance criteria, including critical factors such as reduction in the time delay of bothactuators. The research findings in this paper improve the tracking control systems for the multi-axial RTHS of building structures subjected to earthquake loading. It provides insight into the robustness of the proposed tracking control methods in addressing uncertainty and improves the understanding of multiple output controllers that could be used in future cyber-physical testing of civil infrastructure subjected to natural hazards.

How did the use of the social marketing approach in Egyptian communities succeed in improving breastfeeding practices and infants’ growth?

IntroductionImproving breastfeeding practices does not always link to interventions relying only on improving nutrition awareness and education but needs cultural and behavioral insights. AimThis study aimed to evaluate the changes in core breastfeeding indicators as a result of the use of social marketing (SM) approach for improving breastfeeding practices of Egyptian women and the physical growth of infants aged 6 to 12 months. The core breastfeeding indicators were: Early initiation of breastfeeding within one hour of birth, Predominant and exclusive breastfeeding to 6 months (EBF), Bottle feeding with formula, continued breastfeeding to 1 and 2 years, and responsiveness to cues of hunger and satiety.MethodsA quasi-experimental longitudinal study with a posttest-only control design was done over 3 years in three phases; the first was in-depth interviews and formative research followed by health education and counseling interventions and ended by measuring the outcome. Motivating mothers’ voluntary behaviors toward breastfeeding promotion “feeding your baby like a baby” was done using SM principles: product, price, place, and promotion. The interventions targeted 646 pregnant women in their last trimester and delivered mothers and 1454 women in their childbearing period. The statistical analysis was done by using SPSS program, version 26.ResultsMost mothers showed significantly increased awareness about the benefits of breastfeeding and became interested in breastfeeding their children outside the house using the breastfeeding cover (Gawn) (p < 0.05). Breastfeeding initiation, exclusive breastfeeding under 6 months, frequency of breastfeeding per day, and percentage of children who continued breastfeeding till 2 years, were significantly increased (from 30%, 23%, 56%, and 32% to 62%, 47.3%, 69%, and 43.5% respectively). The girls who recorded underweight results over boys during the first year of life were significantly improved (p < 0.01) after the intervention (from 52.1% to 18.8% respectively). At the same time, girls found to be obese before the intervention (15.6%) became no longer obese.ConclusionsImprovement for the majority of the key breastfeeding indicators and physical growth of infants indicates that raising a healthy generation should start by promoting breastfeeding practices that are respectable to societal norms.

Experimental Evaluation of a Takagi–Sugeno Fuzzy Controller for an EV3 Ballbot System

In this paper, experimental results about the performance of a Takagi–Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration of the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi–Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3BRS’s TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3BRS’s body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3BRS’s body at its upright position. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation.

Fuzzy PID Control Design of Mining Electric Locomotive Based on Permanent Magnet Synchronous Motor

Achieving precise stopping of electric locomotives is crucial for the realization of intelligent and unmanned auxiliary transportation systems. Presently, human drivers play a central role in ensuring accurate stopping, presenting obstacles to automation and cargo location precision, especially within the coal mining sector. This article centers on achieving the precise stopping of electric locomotives under various conditions through the utilization of permanent magnet synchronous motor-driven locomotives. This approach introduces a novel stopping control method that integrates a fuzzy proportional–integral–derivative (F-PID) controller with a vector control model for permanent magnet synchronous motors (PMSM). Subsequently, we develop the F-PID controller using the PMSM technique, incorporating new fuzzy rules for each subsystem to enhance control accuracy and efficiency. Finally, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed to validate the effectiveness of the proposed control method. The results show that the method consistently achieves precise stopping under diverse working conditions, with an error of less than 0.3 m, confirming its robustness and reliability.

A Nonlinear Control Design for Cooperative Adaptive Cruise Control with Time-Varying Communication Delay

Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, the performance, stability, and safety of CACC could be affected by the transmission of outdated data caused by communication delays. This paper proposes a Lyapunov-based nonlinear controller to mitigate the impact of time-varying delays in the communication channel of CACC. This paper uses Lyapunov–Krasovskii functionals in the stability analysis to ensure semi-global uniformly ultimately bounded tracking. The efficaciousness of the proposed CACC algorithm is demonstrated in simulation and through experimental implementation.

Joint Phase Shift Design and Resource Management for a Non-Orthogonal Multiple Access-Enhanced Internet of Vehicle Assisted by an Intelligent Reflecting Surface-Equipped Unmanned Aerial Vehicle

This paper integrates intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAV) to enhance the transmission performance of the Internet of Vehicles (IoV) through non-orthogonal multiple access (NOMA). It focuses on strengthening the signals from cell edge vehicles (CEVs) to the base station by optimizing the wireless propagation environment via an IRS-equipped UAV. The primary goal is to maximize the sum data rate of CEVs while satisfying the constraint of the successive interference cancellation (SIC) decoding threshold. The challenge lies in the non-convex nature of jointly considering the power control, subcarrier allocation, and phase shift design, making the problem difficult to optimally solve. To address this, the problem is decomposed into two independent subproblems, which are then solved iteratively. Specifically, the optimal phase shift design is achieved using the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the graph theory is applied to determine the subcarrier allocation policy and derive a closed-form solution for optimal power control. Finally, the simulation results show that the proposed joint phase shift and resource management scheme significantly enhances the sum data rate compared to the state-of-the-art schemes, thereby demonstrating the benefits of integrating the IRS-equipped UAV into NOMA-enhanced IoV.