Control Approach Research Articles

A voltage sensorless technique for a shunt active power filter adopting band pass filter under abnormal supply conditions

Shunt active power filters (SAPFs) are effective in addressing power quality issues such as harmonics and voltage unbalance. During abnormal grid conditions, there will be a substantial variation in both the actual and reactive power of the system. Various SAPF control approaches were employed to address these issues. This paper presents an improved bandpass filter technique that serves multiple purposes: extracting the reference current harmonic for SAPF, providing reactive current for power factor correction, and functioning as a backup synchronization method in the event of a loss of bus voltage measurement. Furthermore, it should be minimally affected by the unbalance in supply voltage. The case study model and the proposed reference technique have been executed and validated using the MATLAB software environment.

Enhanced control of grid-connected multi-machine wind power generation systems using fuzzy backstepping approaches

This research paper presents an approach for enhancing the performance of a multi-machine wind power generation system (WPGS) through the combination of nonlinear and intelligent control techniques. The suggested approach focuses on optimizing the WPGS, which utilizes permanent magnet synchronous generators (PMSGs) interconnected with the grid. To enable grid connection, a back-to-back configuration is employed for both the rectifier and inverter in a fifteen-switch configuration, with control achieved through pulse width modulation. The designed control algorithm is designed to effectively regulate the system and reduce the active and reactive power ripples. Initially, the WPGS model is provided. Subsequently, the fuzzy backstepping control (FBC) law is detailed, incorporating the Lyapunov stability technique. Fuzzy logic is used to adapt the gains in the backstepping approach, ensuring the control system can accommodate disturbances and system variations. As a result, this adaptive control approach enables optimal effusiveness of the entire system in both static and dynamic operational regimes. Experimental tests were conducted using MATLAB to compare the efficacy of the designed strategy against the traditional backstepping control approach. The obtained results demonstrated the robustness and effective reference tracking capabilities of the designed FBC approach, along with the complete elimination of speed overshoot across diverse wind conditions. These findings validate the designed control approach's effectiveness and superiority over the BC approach under varying conditions.

To make or buy commercialization: A synthetic control approach

To make or buy commercialization: A synthetic control approach

Active vibration control of metal foam reinforced agglomerated graphene nanoplatelets nanocomposite truncated conical shells with piezoelectric layers

This study examines active vibration control (AVC) in a truncated conical shell composed of metal foam reinforced with agglomerated graphene nanoplatelets and integrated with piezoelectric layers functioning as sensors and actuators. Both complete and partial agglomeration states are considered based on the Eshelby–Mori–Tanaka approach, and the mechanical properties of the porous core are characterized using a closed-cell metal foam model. The governing equations of motion are derived using Hamilton’s principle, based on the two-dimensional axisymmetric elasticity theory, and solved through the finite element method coupled with the Newmark method. Vibration mitigation is achieved using a fractional-order fuzzy proportional–integral–derivative (PID) controller. A comprehensive parametric study is conducted, examining the effects of geometric dimensions, various boundary conditions, reinforcement parameters (including weight fraction, distribution patterns, and agglomeration parameters), and porosity parameters (including pore size, and porosity pattern) on the vibration properties of the shell. Numerical simulations are performed to assess the effectiveness of the proposed AVC system in reducing structural vibrations compared to a constant velocity feedback control approach. The velocity feedback controller reduced central deflection from 12.65 to 5.089 μ m , while a fractional-order fuzzy PID controller further improved it to 2.348 μ m , representing a 53.86% enhancement over the velocity feedback controller.

Munich’s selective diesel vehicle ban and its impact on nitrogen dioxide concentrations: A quasi-experimental study

BackgroundThe current limit on NO2 concentrations of 40 μg/m3, set by the European Union, has been regularly exceeded in Munich, Germany. This limit will likely be reduced towards the WHO recommended target of 10 μg/m3. Against this backdrop, the city implemented a selective diesel vehicle ban within the existing low-emission zone in February 2023, targeting Euro 4 and older diesel vehicles. Our study investigated the effect of Munich’s selective diesel vehicle ban on NO2 concentrations, focusing on the half-year period following its implementation. MethodsOur study utilized a synthetic control approach (primary analysis) and a controlled interrupted time series approach (secondary analysis). These quasi-experimental methodologies create a ‘counterfactual’ no-intervention scenario, enabling comparison between observed and counterfactual scenarios to estimate an intervention effect. We employed historical controls, using routine data from multiple monitoring stations located within and outside the low-emission zone for 2014 to 2022, and considered possible confounders. ResultsNO2 concentrations within Munich’s low-emission zone showed overall declining trends from August 2014 to July 2023. Effects of the selective diesel vehicle ban were small and wide confidence intervals indicate large uncertainty in the magnitude and direction of the effect. At Landshuter Allee, the average intervention effect was −2.67 μg/m3 (95 %-CI = [-12.72; 7.38]), at Stachus it was −2.74 μg/m3 (95 %-CI = [-9.91; 4.42]), and at Lothstrasse it was −1.03 μg/m3 (95 %-CI = [-7.75; 5.69]). The secondary analysis confirmed these findings, reinforcing uncertainty about the effect of the intervention. ConclusionOur study suggests that Munich’s selective diesel vehicle ban had a limited effect on lowering NO2 concentrations. Possible explanations include the ban’s focus on Euro 4 and older diesel vehicles, many exemptions to the selective ban, and unclear enforcement. This highlights that comprehensive approaches and ongoing, well-designed monitoring and evaluation are crucial for addressing urban air pollution and protecting public health.

Non-volatile Fermi level tuning for the control of spin-charge conversion at room temperature

Current silicon-based CMOS devices face physical limitations in downscaling size and power loss, restricting their capability to meet the demands for data storage and information processing of emerging technologies. One possible alternative is to encode the information in a non-volatile magnetic state and manipulate this spin state electronically, as in spintronics. However, current spintronic devices rely on the current-driven control of magnetization, which involves Joule heating and power dissipation. This limitation has motivated intense research into the voltage-driven manipulation of spin signals to achieve energy-efficient device operation. Here, we show non-volatile control of spin-charge conversion at room temperature in graphene-based heterostructures through Fermi level tuning. We use a polymeric ferroelectric film to induce non-volatile charging in graphene. To demonstrate the switching of spin-to-charge conversion we perform ferromagnetic resonance and inverse Edelstein effect experiments. The sign change of output voltage is derived by the change of carrier type, which can be achieved solely by a voltage pulse. Our results provide an alternative approach for the electric-field control of spin-charge conversion, which constitutes a building block for the next generation of spin-orbitronic memory and logic devices.

Reinforcement Learning for Fuselage Shape Control during Aircraft Assembly

Critical safety requirements necessitate ultra-high precision quality control during the assembly of large aerospace components to reduce the mismatch between parts to be joined. Traditional methods use heuristic shape adjustment or surrogate model-based control. These methods are limited by reliance on accurate model learning and inadequate robustness to varying initial assembly conditions. To address these limitations, this paper proposes a model-free reinforcement learning approach for adaptive fuselage shape control during aircraft assembly. The trained reinforcement learning agent directly adjusts the aircraft components in response to their part variations and enables an autonomous system (like AlphaGo) to learn the optimal shape control policy. Specifically, the reinforcement learning environment is built on the finite element simulator. A reward function is developed to capture the optimization objective and introduces a scheme to enforce the original constraints. The proximal policy optimization algorithm is modified to speed up the learning progress and achieve better final performance. In the case study, the root-mean-square gap between components is reduced by 98.4% on average compared with their initial shape mismatch. Our proposed method outperforms the benchmark methods with smaller final shape errors, smaller maximum forces, and lower variations across different test samples.

Non‐Uniform Electric Field Manipulation of Chromogenic Peptide Amphiphile Assemblies

This work investigates the influence of dielectrophoretic forces on the structural features and the resulting aggregates of a chromogenic model system, peptide‐diacetylene (D3GV‐DA) amphiphiles. Here, we systematically investigate how non‐uniform electric fields impact the (i) peptide‐directed supramolecular assembly stage and (ii) topochemical photopolymerization stage of polydiacetylenes (PDAs) in a quadrupole‐based dielectrophoresis (DEP) device, as well as the (iii) manipulation of D3GV‐DA aggregates in a light‐induced DEP (LiDEP) platform. The conformation‐dependent chromatic phases of peptide‐PDAs are utilized to probe the chain‐level effect of DEP exposure after the supramolecular assembly or after the topochemical photopolymerization stage. Steady‐state spectroscopic and microscopy analyses show that structural features such as the chirality and morphologies of peptidic 1‐D nanoassemblies are mostly conserved upon DEP exposure, but applying mild, non‐uniform fields at the self‐assembly stage is sufficient for fine‐tuning the chromatic phase ratio in peptide‐PDAs and manipulating their aggregates via LiDEP. Overall, this work provides insights into how non‐uniform electric fields offer a controllable approach to fine‐tune or preserve the molecularly preset assembly order of DEP‐responsive supramolecular or biopolymeric assemblies, as well as manipulate their aggregates using light projections, which have future implications for the precision fabrication of macromolecular systems with hierarchical structure‐dependent function.

Development and experimental validation of high-order sliding mode control for quadratic boost converters in fuel cell electric vehicles

The global energy evolution is at a critical juncture, necessitating an urgent transition towards clean and sustainable energy sources, notably green hydrogen, to combat climate change and the inevi-table depletion of fossil fuels. This transition is underscored by the emergence of fuel cell electric vehicles (FCEVs) as a promising solution for clean transportation, demanding advanced DC-DC power converter technologies to ensure their efficiency and reliability. This paper addresses the topic of control for the Quadratic Boost Converter, which presents a challenge due to its non-minimum phase characteristic and nonlinear nature. To tackle this, a nonlinear controller is developed using dual-loop control employing STA-SMC and Lyapunov theory as the control ap-proach. Simulation and experimental validation evaluate the controller's performance, testing ro-bustness against varying load currents and under time-varying reference voltages to assess its ef-fectiveness.

Acupuncture for cancer-related insomnia: systematic review and meta-analysis of randomised controlled trials

BackgroundAcupuncture has been demonstrated to be a safe and effective treatment for insomnia in patients without cancer. A lack of evidence, however, supports its application in the treatment of cancer-related...

An adaptive null proportion estimator for false discovery rate control

SUMMARY False discovery rate (FDR) is a commonly used criterion in multiple testing and the BenjaminiHochberg (BH) procedure is a standard approach for FDR control. To boost power, the adaptive BH procedure has been proposed by incorporating null proportion estimators, among which Storey’s estimator has gained substantial popularity. The performance of Storey’s estimator hinges on a critical hyper-parameter, where a pre-fixed configuration may lack power and existing data-driven hyper-parameters compromise the FDR control. In this work, we propose a novel class of adaptive hyper-parameters and establish the FDR control of the associated adaptive BH procedure using a martingale argument. Within this class of data-driven hyper-parameters, we further present a specific configuration designed to maximize the number of rejections and characterize its convergence to the optimal hyper-parameter under a mixture model. Our proposal exhibits significant power gains particularly in cases with a conservative null distribution common in composite null testing or a moderate proportion of weak non-nulls typically observed in biological experiments with enrichment processes.

Determination of naringin and neohesperidin in "Quzhiqiao" (Immature fruit of Citrus paradisi 'Changshan Huyou') using graphene-based molecularly imprinted technology

A sensitive and selective molecularly imprinted polymer-graphene oxide (MIP@GO) electrochemical sensor was fabricated for the determination of naringin and neohesperidin in "Quzhiqiao" (immature fruit of Citrus paradisi 'Changshan Huyou'). The MIP@GO was prepared by electropolymerization using naringin and neohesperidin as templates. Under optimized conditions, the sensor exhibited linear ranges of 0.1–50 μM for naringin and 0.2–60 μM for neohesperidin, with low detection limits of 0.03 μM and 0.05 μM, respectively. The imprinting factors for naringin and neohesperidin were 3.8 and 2.5, respectively. The sensor demonstrated excellent selectivity, reproducibility (RSD < 3.6 %), stability (>94.5 % retention after 30 days), and reusability (>92.8 % retention after 10 cycles). The MIP@GO sensor was successfully applied to Fructus aurantii samples, with recoveries ranging from 95.5 % to 104.2 %. This work presents a rapid, cost-effective, and highly sensitive approach for the quality control of Quzhiqiao and related citrus products.

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

A learning‐based hierarchical energy management control strategy for hybrid electric vehicles

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

Disturbance observer based adaptive predefined‐time sliding mode control for robot manipulators with uncertainties and disturbances

AbstractThis article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

Implementation of NLC‐SHM based novel PWM scheme on packed U‐cell multilevel inverter

AbstractThis article introduces a novel integrated nearest level controller (NLC) combined with selective harmonic mitigation (SHM) control approach aimed at efficiently suppressing lower‐order harmonics has been introduced. The proposed methodology is implemented on the PUC‐7 inverter topology, leveraging the combined benefits offered by both NLC and SHM controllers through a hybrid model. The proposed method demonstrates the capability to reduce lower‐order harmonics up to the 49th harmonic from the load voltage. The particle swarm optimization algorithm is employed to determine optimal switching angles and their corresponding nearest levels, thereby reducing implementation time, computational complexity, and overall system intricacy. The performance analysis of the proposed control strategy across various scenarios, including fluctuations in modulation index and monitoring diverse parameters such as load dynamics has been performed. This approach yields a significant reduction in total harmonic distortion in both load voltage and current. A comparative analysis between NLC, selective harmonic mitigation (SHM), and the integrated NLC‐SHM approach is carried out on the PUC‐7 inverter. The integrated SHM‐NLC control is implemented and validated using the MATLAB Simulink environment, complemented by hardware‐based experimentation, to ascertain its effectiveness.

Finite‐time lag bipartite synchronization of double‐layer networks with non‐linear coupling strength and random coupling delays via T‐S fuzzy logic theory

AbstractThis article discusses finite‐time lag bipartite (FETLB) synchronization of double‐layer networks with non‐linear coupling strength and multiple time delays. The cooperative and competitive interactions between nodes are considered based on signed graphs. To address the non‐linear couplings strength, the T‐S fuzzy logic theory is used. An intermittent control approach is introduced to achieve FETLB synchronization, effectively minimizing control costs. Moreover, by the Lyapunov functional method, we derive criteria for achieving FETLB synchronization and provide estimations for the synchronization settling time. In addition, numerical simulation affirms the validity of the theoretical findings, showcasing the practical application of the synchronization results in secure communication.

A Review of the Arrival, Effects, and Management of Bagrada hilaris in South America: The Case of Chile

Biological invasions are partly defined by their ability to spread rapidly within invaded regions, posing threats to biodiversity and local species assemblages. The painted bug, Bagrada hilaris (Hemiptera, Pentatomidae) (Burmeister, 1835), originally described as being from India, is an important pest worldwide, mainly due to the serious economic losses incurred and the large number of zones invaded. Since its initial detection in Chile in 2016, the invasive and destructive paint bug has started its invasion to the east and invaded new areas in South America. Without adequate control methods, the insect may threaten brassica crop production, resulting in yield losses greater than 70% in recently infested areas. The extraordinarily wide range of the B. hilaris invasion has necessitated studies describing its biology and ecology, as well as highlighting the urgent need for effective and long-term management techniques. Hence, thoroughly revising the data on this pest in new areas that have been invaded, as well as in the area of origin, is expected to enable the development of management programs. This review incorporates information on B. hilaris in South America, including the invasion, ecology, and potential management approaches, which may allow for efficient integrated pest management, mainly in Chile. Lastly, researchers are expected to break the bottlenecks of some key issues, offering valuable perspectives for identifying strategies that warrant additional research as potential biological control agents for B. hilaris in South America, utilizing either augmentative or conservation biological control approaches, as well as semiochemicals and cultural practices.

A Practical, Adaptive, and Scalable Real-Time Control Approach for Stormwater Storage Systems

Traditionally, urban stormwater infrastructure systems consist of passive infrastructure that is not actively controlled in response to rainfall events. Recently, real-time control (RTC) has been considered as a means to significantly increase the capacity and lifespan of these systems. This paper introduces the target flow control systems (TFCS) approach, which can use real-time control of systems of storages to achieve the desired flow conditions at the locations of interest. The first distinctive feature of this approach is that it does not require calibration to catchment-specific data, unlike existing approaches. This means that the TFCS approach is generally applicable to different catchments and is able to respond to future changes in runoff due to land use and/or climate change. The second distinctive feature is that the approach only requires storage-level information measured in real time with the aid of low-cost pressure sensors. This means that the approach is practical and relatively easy to implement. In addition to the introduction of the novel TFCS approach, a key innovation of this study is that the approach is tested on three case studies, each with different physical configurations and stormwater management objectives. Another key innovation is that the TFCS approach is compared to five RTC approaches, including three of the best-performing advanced approaches from the literature. Comparisons of multiple RTC approaches that consider both performance and practicality across multiple case studies are rare. Results show that the TFCS approach is the only one of the five control approaches analysed that has both the best overall performance and the highest level of practicality. The outcomes highlight the potential of the TFCS approach as a practical RTC approach that is applicable to a wide range of catchments with different stormwater management objectives. By maximizing the performance of existing stormwater storages, the TFCS approach can potentially extend the lifespan of existing infrastructure and avoid costly upgrades due to increased runoff caused by land use and climate change.