Control Applications Research Articles

Piecewise affine modeling of parallel boost converter in a DC microgrid and its control application by utilizing a Linear Matrix Inequality approach

Piecewise affine modeling of parallel boost converter in a DC microgrid and its control application by utilizing a Linear Matrix Inequality approach

LIBS as a fast and reliable alternative to µXRF and SEM–EDX for quantitative analysis of aluminium alloy particles in technical cleanliness analysis

The characterization of metal alloy (particle) characterization is a vital tool in today’s high–tech applications for quality and process control in industry, especially for applications in technical cleanliness analysis (TCA). Here, metal alloy fragments down to sizes of 200 µm need to be quantitatively characterized as accurate as possible. Laser-induced breakdown spectroscopy (LIBS) offers several advantages due to its easy use, short measurements times, and high sensitivity. To enable the application of LIBS in this context, the method must yield adequately accurate results. Therefore, in this study we assess the performance of LIBS by comparison with well–established methods – scanning electron microscopy coupled with energy-dispersive X‑ray spectroscopy (SEM-EDX) and µ‑X‑ray fluorescence (µXRF). We analyse particulate certified reference materials consecutively with non–destructive and destructive methods on the quantitative composition regarding the key elements necessary for distinguishing aluminium alloys: Si, Zn, Mn, Mg, Cu and Fe. Sample inhomogeneity and the effect of sampling location and area is shown via microstructure analysis and combined with an assessment of the analysis volume for each used method. By evaluating the data statistically and by comparing quantitative results achieved with different methods, we can postulate a high agreement between LIBS and the studied reference methods, providing a basis for the use of LIBS in industrial analysis of metal alloy particles.

Relative-kinematic formulation of geometrically exact beam dynamics based on Lie group variational integrators

Geometrically exact beam models can accurately describe the statics and dynamics of elastic, slender beams undergoing large deformations. This work introduces a variational integrator for such models based on a relative-kinematic formulation, in which positions and velocities of nodes in the discretized beam are expressed relative to the respective preceding nodes in the kinematic chain. The resulting model is especially suited for applications in robotics and control, since it has a minimum number of states and possesses the same structure as rigid mechanical systems, simplifying the combined modeling of rigid–flexible systems. Moreover, this approach allows to specifically select the modeled deformation modes, avoiding numerical issues associated with stiff, high-frequency modes and greatly increasing numerical efficiency. The proposed model is derived fully variationally in the discrete mechanics framework and under consistent consideration of the underlying Lie group structure, which translates into beneficial numerical properties. We provide a detailed treatment of the inclusion of dissipation and propose two solver strategies for time integration, including a linear-time solver based on recursive rigid-body dynamics algorithms. The model is validated and analyzed in detailed simulation studies underlining its efficiency for the simulation of stiff and slender beams, where the straightforward, but exact reduction to a Kirchhoff beam in the presented frame leads to a substantial speed-up of the simulations.

Design of Experiment and green analytical chemistry principles for developing a robust and sustainable Stability-Indicating HPLC method for Bempedoic acid impurity profiling

Bempedoic acid (BEM), a drug used to lower cholesterol levels in patients with atherosclerosis. Controlling its process-related impurities is crucial due to their potential toxicity. This study details the development of a RP-HPLC method for the analysis of four process-related substances in BEM. To ensure robust chromatographic performance meeting six sigma quality standards, the method was optimized using a combined approach of response surface methodology (specifically, an I-optimal design) and tolerance analysis, integrating predefined specifications for critical method attributes such as resolution, tailing factor, and number of theoretical plates into the developmental process. Chromatographic separation was achieved using a C18 column. The mobile phase consisted of 0.1 % phosphoric acid in water and acetonitrile, utilizing a stepped gradient elution profile. The flow rate was maintained at 0.9 mL/min, with UV detection performed at 210 nm. The column temperature was set to 20 °C. The method demonstrated linearity for BEM process related impurities in the range of LOQ to 30 μg mL−1. The method ability to differentiate the drug substance from its degradation products was confirmed through forced degradation study. The environmental sustainability of the developed method was assessed using multiple Green Analytical Chemistry (GAC) tools. The National Environmental Methods Index (NEMI) showed two green-shaded quadrants, and the Green Certificate modified Eco-scale awarded a score of 70. The Complementary Green Analytical Procedure Index (Complex GAPI) confirmed the method environmentally friendly profile. Additionally, The AGREE tool generated a score of 0.77, indicating strong overall greenness, while the AGREEprep tool, focused on sample preparation, yielded a score of 0.54. Other tools such as the Sample Preparation Metric of Sustainability (SPMS) and the HPLC Environmental Assessment Tool (HPLC-EAT) scored 8.42 and 569, respectively, further demonstrating the method green credentials. This robust and environmentally conscious analytical framework offers significant potential for application in quality control of BEM.

Effective Application of IoT Power Electronics Technology and Power System Optimization Control

Effective Application of IoT Power Electronics Technology and Power System Optimization Control

Coagulative removal of polyethylene microplastics using polyaluminum chloride in conjunction with laminarin

As emerging pollutants in water, microplastics pose potential risks to aquatic organisms and human health. Previous studies have shown that traditional metal coagulants are effective in removing microplastics from water, but they come with drawbacks such as high chemical dosage and metal residues. Therefore, it is important to explore more efficient coagulation systems. This study utilized laminarin (LA) as a coagulant aid in combination with polyaluminum chloride (PAC) to investigate its enhanced coagulation performance on polyethylene microplastics in water. The performance of coagulation systems for the removal of microplastics was evaluated for different agent dosages, pH, anion and HA content. Experimental results demonstrated that the compounded system (PAC-LA) significantly improved the removal efficiency of PE microplastics compared to the single PAC system, achieving a removal rate of 91.5% while reducing the dosage of PAC. The enhanced coagulation mechanism of LA was analyzed using various techniques including scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and zeta potential analysis. The results indicated that charge neutralization and sweep flocculation were the primary coagulation mechanisms in the presence of PAC alone. The effect of charge neutralization and adsorption was strengthened with the introduction of LA. The coagulation system of PAC-LA showed higher removal rates of PE microplastics under different environmental conditions such as varying pH levels, co-existing anions, and humic acid, suggesting a promising application for microplastic control in water.

Enhancing power quality in solar-wind grid-connected systems through soft computing techniques

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

Real-time anomaly detection in electric motor operation noise

Anomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen.

Eighth IEEE Conference on Control Technology and Applications [Conference Reports

Eighth IEEE Conference on Control Technology and Applications [Conference Reports

Grey-Box Energy Modelling of Energy-Efficient House Using Hybrid Optimization Technique of Genetic Algorithms (GA) and Quasi-Newton Algorithms with Markov Chain Monte Carlo Uncertainty Distribution

Understanding energy demands and costs is important for policy makers and the energy sector, especially in the context of residential heating and cooling systems. To estimate the thermal demand of a residential house, a grey-box modelling method with a resistance–capacitance (RC) analogy was implemented. The architectural properties used to parameterize the grey-box model were derived from a house used for research purposes in Vaughan, Ontario, Canada (TRCA-House A). The house model accounts for solar irradiance on exterior building surfaces, thermal conductivity through all surfaces, solar heat gains through windows, and thermal gains from ventilation. Two parallel short- and long-term calibrations were performed such that model outputs reflected the real-world operation of the house as best as possible. To define the unknown model parameters (such as the conductivity of building materials and some constant parameters), a hybrid optimization scheme including a genetic algorithm (GA) and the Quasi-Newton algorithm was introduced and implemented using Bayesian approximation and Markov Chain Monte Carlo (MCMC) methods. The temperature outputs from the model were compared to the data retrieved from TRCA-House A. The final iteration of the model had an RMSE for interior zone temperature estimation of 0.22 °C when compared to the retrieved interior zone temperature data from TRCA-House A. Furthermore, the annual heating and cooling energy consumption values are within 1.50% and 0.08% of target values, respectively. According to these preliminary results, the introduced model and optimization techniques could be adjusted for different types of housing, as well as for smart control applications on both a short- and long-term basis.

An efficient modified conjugate gradient parameter for solving the system of symmetric nonlinear equations with application in motion control of coplanar robot

An efficient modified conjugate gradient parameter for solving the system of symmetric nonlinear equations with application in motion control of coplanar robot

Synthesis of chiral gold helicoid nanoparticles using glutathione.

Chiral plasmonic nanostructures are in high demand because of their unique optical properties, which are applicable to polarization control, chiral sensing and biomedical applications. An easy and scalable synthesis method for these nanostructures may facilitate their development further. We have reported the synthesis for 432-symmetric chiral plasmonic nanoparticles by using a seed-mediated colloidal method facilitated by a chiral amino acid and peptides. Among those, 432 helicoid III nanoparticles particularly exhibited well-defined chiral morphologies and exceptional chiroptic properties, evidenced by a Kuhn's dissymmetry factor (g-factor) of 0.2, making them valuable for various applications. Here, we detail the synthesis stages, including the synthesis of seed nanoparticles, the verification of each stage outcome and the calibration of synthesis conditions. We further illustrate the troubleshooting section and video-document the stages to facilitate the reliable reproduction of 432 helicoid III nanoparticles. The procedure requires 8 h to complete and can be carried out by users with expertise in chemistry or materials science.

Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters

This paper presents a trajectory control model using finite state machines for a single-stage soft-switching grid-tied inverter designed with a fast dynamic response. The targeted application is a module-integrated inverter for a single photovoltaic (PV) panel which interfaces distributed energy sources with the grid. To minimize switching lossd provide advanced grid-connected functionality, the soft-switching operation is achieved through a resonant filter using a trajectory control scheme. In recent years, controllers based on digital signal processing platforms have been able to handle complex and high-speed control algorithms with precision for real-time control. In real-time control applications, the finite state machine (FSM) approach enhances responsiveness by minimizing latency with limited memory resources by executing rapid state transitions. The proposed model effectively manages the switching states of the single-stage soft-switching inverters during complex DC/AC bidirectional operations. By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. The control scheme has been digitally implemented on a Texas Instruments (TI) digital signal processor and validated through Hardware-In-the-Loop (HIL) testing.

Analysis of Herbal Constituents and InVivo Pharmacokinetics of Gegen-Huangqi Decoction in Rat Plasma Using HPLC-Q-TOF-MS/MS and HPLC-QQQ-MS/MS.

The traditional Chinese medicine (TCM) formula, gegen-huangqi (GH) decoction, has been employed for over 200 years, notably for its therapeutic effects in treating conditions such as atherosclerosis and diabetes mellitus. Despite its long-standing use, comprehensive studies on the chemical constituents of GH and their invivo pharmacokinetics (PK) remain limited. This study aimed to profile the bioactive compounds present in GH decoction and to explore their PK characteristics using HPLC-Q-TOF-MS/MS. Furthermore, a robust and validated analytical method was developed and applied to assess the PK of 11 plasma compounds using HPLC-QQQ-MS/MS. In total, 79 components were identified within the GH decoction. Pharmacokinetic analysis revealed distinct absorption and elimination profiles for compounds such as puerarin, daidzin, genistin, calycosin-7-O-β-D-glucoside, and ononin, which exhibited profiles of quick absorption and excretion. Conversely, compounds such as daidzein, formononetin, genistein, astragaloside II, astragaloside IV, and calycosin showed more complex invivo metabolic patterns, leading to multipeak concentration-time curves. All compounds, except astragalosides II and IV, were found to undergo significant hepatic clearance. These findings provide valuable insights into the pharmacokinetic behavior of GH decoction, which lays the foundation for further quality control, pharmacological exploration, and potential clinical application of this traditional remedy.

Design Strategy, On-Demand Control, and Biomedical Engineering Applications of Wet Adhesion.

The adhesion of tissues to external devices is fundamental to numerous critical applications in biomedical engineering, including tissue and organ repair, bioelectronic interfaces, adhesive robotics, wearable electronics, biomedical sensing and actuation, as well as medical monitoring, treatment, and healthcare. A key challenge in this context is that tissues are typically situated in aqueous and dynamic environments, which poses a bottleneck to further advancements in these fields. Wet adhesion technology (WAT) presents an effective solution to this issue. In this review, we summarize the three major design strategies and control methods of wet adhesion, comprehensively and systematically introducing the latest applications and advancements of WAT in the field of biomedical engineering. First, single adhesion mechanism under the frameworks of the three design strategies is systematically introduced. Second, control methods for adhesion are comprehensively summarized, including spatiotemporal control, detachment control, and reversible adhesion control. Third, a systematic summary and discussion of the latest applications of WAT in biomedical engineering research and education were presented, with a particular focus on innovative applications such as tissue-electronic interface devices, ingestible devices, end-effector components, in vivo medical microrobots, and medical instruments and equipment. Finally, opportunities and challenges encountered in the design and development of wet adhesives with advanced adhesive performance and application prospects are discussed.

Predictive control of interconnected fractional‐order systems with communication delays: An application for load frequency control of power system

AbstractThis research focuses on constrained model predictive control (CMPC) problem for interconnected fractional‐order systems (IFOS). Communication delays and imposed disturbances are considered in the design procedure of the control system. The dynamic equations of the IFOS are reformulated to handle the interaction delays and to obtain a model for the overall system. Then, a constrained model predictive controller is designed for the input and output (I/O) oriented model of the IFOS. To solve this control problem, a quadratic programming algorithm is utilized to obtain the optimal solution. Since most of the power systems exhibit an interconnected structure and consist of several subsystems, this study considers the load frequency control (LFC) problem for an interconnected fractional‐order power system in the presence of communication delays to evaluate the proposed technique. Numerical simulations are provided for two different cases, and the results are reported.

Application of Life Cycle Assessment for Torrent Control Structures: A Review

Mountain areas are prone to the occurrence of extreme events, especially torrential floods, amplified by climatic and environmental changes. In this context, it is mandatory to increase resilience and guide decision-makers toward more effective measures. Life cycle assessment (LCA) is considered as a decision support tool that can provide the qualitative and quantitative criteria required by the Do No Significant Harm, thus contributing to a more accurate assessment of environmental impacts of the torrent control structures. This study aimed to investigate the current state of the LCA applications in the torrent control to provide practitioners perspectives for new research and a pathway for optimized LCA analysis. Our analysis reveals that in the torrent control area, these studies are still limited. Most of the papers considered Ecoinvent as the main database source and cradle to grave as the main system boundary. This study suggests that restoring the functional capacity of dams and other torrent control structures instead of demolition or decommissioning from the end-of-life stage will ensure an orientation towards more sustainable and circular strategies. Although strong partnerships and consistent efforts are needed, general findings reveal that LCA is a useful tool for moving towards more sustainable construction practices.

Optimizing CNC milling parameters for manufacturing of ultra-sharp tip microneedle with various tip angles.

Microneedle technology has emerged as an advanced method for transdermal drug delivery, which focuses on diverse fabrication techniques to develop microneedles with various models and geometries. This study explores the application of Computer Numerical Control (CNC) milling technology to create microneedle master molds with extremely sharp tips. We examined the effects of two key machining parameters, feed rate and ramp angle, on the tip sharpness of the microneedles. Our results showed that increasing both the feed rate and ramp angle could significantly reduce machining time. However, a higher feed rate also led to larger tip diameters and notable tip defects. Conversely, changes in the ramp angle at a constant feed rate had minimal impact on tip size. We identified an optimal condition balancing cutting time and tip sharpness at a feed rate of 100mm/min and a ramp angle of 1.5°. Additionally, we assessed the CNC's capability to produce needles with different tip angles. The findings confirm that needles with varying tip angles maintained tip diameters below 10μm, with needles having a 50° tip angle exhibiting the sharpest tips at approximately 3.3μm. Further compression, insertion and diffusion tests were conducted to evaluate the performance of needles with different geometries.

Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy

Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP to enhance the learning efficiency and control performance of reinforcement learning in robot manipulation tasks by focusing on the forms of control actions and their smoothness. A novel approach, DDPG-DMP, is proposed to address the efficiency and feasibility issues in the current RL approaches that employ DMP to generate control actions. The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. A novel inverse controller is further introduced to adaptively learn the translation from observed states into various robot control signals through DMPs, eliminating the requirement for human prior knowledge. Evaluated on five robot arm control benchmark tasks, DDPG-DMP demonstrates significant advantages in control performance, learning efficiency, and smoothness of robot actions compared to related baselines, highlighting its potential in complex robot control applications.

Design and synchronization control application of a new five-dimensional memristor CNN conservative hyperchaotic system

Abstract Incorporating memristors into a cellular neural network (CNN) and introducing chaotic characteristics can generate highly complex and unpredictable dynamic behaviors. To advance this research area, this paper proposes a new five-dimensional memristor CNN conservative hyperchaotic system and systematically analyzes its dynamic properties. The analysis content includes equilibrium point analysis, Poincaré sections, Lyapunov exponent spectra, bifurcation diagrams, two-parameter Lyapunov exponent spectra, complexity assessment, homogeneous and heterogeneous extreme multistability, etc In addition, the simulation circuit for the new system is designed and constructed. The digital circuit of the new system is implemented using a microcontroller (MCU). After running simulations, the experimental results from the analog circuit, digital circuit, and numerical simulation are consistent with each other, demonstrating the feasibility of the circuit implementation. Finally, two different synchronization control strategies are employed to achieve synchronization control within a finite time.