Control Programming Research Articles

Sistem Otomatis Pengendalian Pakan Ikan dengan Sumber Energi Matahari Menggunakan Mikrokontroller Arduino

Humans have made many discoveries in the modern era that make work easier, including advances in technology, healthcare, agriculture, and cultivation. Automated methods and equipment are among thedevelopments that make human work easier. As a result, a device for feeding fish that can functionautomatically according to the feeding plan and time is designed using a servo motor controller for the fishfeeder powered by solar energy and an Arduino microcontroller module as the controller program. Solarpanels are power plants that receive power from sunlight and are equipped with a charge controller. Theinverter acts as a DC to AC voltage converter, while the battery stores and distributes the voltage. Thescheduling system used by this tool is determined by Arduino software that has been programmed in astructured manner. It will operate on a pre-set schedule, which requires it to be used between 8 am and 5pm, and its output capacity of 10 feed container valve openings is set consistently and pre-programmed.During the test, the average power consumption was 142.75 watts during the day and 125.72 watts at night,with a total consumption of 108.93 watts at night.

Survey of Optimization Techniques in Interconnected Two Area Power system for the Application of AGC

The increasing need for energy has placed optimization at the center of power engineering research and development. Presenting the theoretical underpinnings of optimization from a practical power system perspective, this review article on Electric Power System Applications of Optimization explores state-of-the-art techniques, novel approaches, and ongoing application difficulties. This work addresses several algorithmic issues that arise from the planning and management of power systems using diverse approaches, as well as the analytical formulation of optimization. This article introduces new features related to market programs, pricing, reliability, and developments in intelligent systems with working algorithms and examples. This paper discusses the latest developments in the fields of adaptive feedback design and approximate dynamic programming. In order to describe new concepts and variations of Adaptive Dynamic Programming, this work begins with fundamental theories and theorems from functional optimization, optimum control, and dynamic programming. Because of its innovative integration of basic optimization principles with application, this review article encourages power engineers to make new discoveries in the provision of energy at the optimal rate.

Quadratic Programming for Continuous Control of Safety-Critical Multiagent Systems Under Uncertainty

This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actuation system describes the dynamic response of the actual velocity to the velocity reference signal. The notion of input-to-output stability (IOS) is employed to characterize the essential velocity-tracking capability of the actuation system. The standard QP algorithms for collision avoidance may be infeasible due to uncertain actuator dynamics. Even if feasible, the solutions may be non-Lipschitz because of possible violation of the full rank condition of the active constraints. Also, the interaction between the controlled integrator and the uncertain actuator dynamics may lead to significant robustness issues. Based on the current development of nonlinear control theory and numerical optimization methods, this paper first contributes a new feasible-set reshaping technique and a refined QP algorithm for feasibility, robustness, and local Lipschitz continuity. Then, we present a nonlinear small-gain analysis to handle the inherent interaction for guaranteed safety of the closed-loop multi-agent system. The proposed method is illustrated by numerical simulation and a physical experiment.

Metabolic effects of training endurance horses in a field setting

Abstract Changes in serum biochemistry and skeletal muscle of horses in response to individual endurance rides of varying distance have been reported, as have the effects of submaximal treadmill training. This study evaluated the effects of a controlled field endurance training programme on energy metabolism. Five Arabian horses 6.8 ± 2.0 years (mean ± standard deviation) were trained for 16 weeks over progressively longer distances. The biceps femoris, triceps long head, and semitendinosus muscles were biopsied before and after 4-weekly runs of 10, 20 and 30 miles, respectively, and before and after 10, 20, 30 and 50 miles of a fourth run at the end of the training programme to measure 3-hydroxyacyl-CoA-dehydrogenase (HAD) and succinate dehydrogenase (SDH) activities and triglyceride (TG) and glycogen concentrations in those biopsies. Blood samples were obtained simultaneously to measure serum TG, free fatty acid (FFA), glycerol, glucagon, insulin, and cortisol concentrations. Data were analysed using RM-ANOVA for effects of training and distance. was considered significant. Training increased resting glycogen and TG concentrations in the biceps (, ), triceps (, ) and semitendinosus (, ), respectively. Both HAD and SDH activities increased in the biceps femoris (; ) and triceps (; ), respectively, but not in the semitendinosus. There was no training effect detected on serum hormone responses to exercise distance although serum glycerol (), glucagon () and cortisol () concentrations increased with increasing run distance, while insulin concentration decreased (). Glucagon and cortisol concentrations were correlated (r = 0.67; ). Serum TG decreased with exercise but at a slower rate following training, and serum FFA were higher during exercise following training when compared to the partially trained state. Overall, endurance training enhanced peripheral mobilisation of fat and its utilisation by working muscle, and resting muscle glycogen stores increased.

METHOD DEVELOPMENT AND VALIDATION OF DIMETHYL SULPHATE CONTENT IN ESOMEPRAZOLE MAGNESIUM DRUG SUBSTANCE BY GC-MS

Dimethyl sulfate is universally used as an alkylating, sulfating and sulfonating agent in organic synthesis; hence it is one of the probable impurities during the synthesis of the esomeprazole magnesium. As per ICH M7, it is genotoxic and mutagenic, so it needs to be controlled as per the acceptable intake of dimethyl sulfate and daily sample dosage. This method validation can be achieved by the hyphenated technique of gas chromatography with mass spectroscopy, which is used to develop the dimethyl sulfate impurity in esomeprazole magnesium drug substance containing salt. The method is achieved with a DB-1 column with electron impact ionization source in sim mode ion under electronic pneumatic pressure control and deliberate oven ramp programming temperature was used. A dissolved, extracted and auto-injection sample was implemented for sample introduction in a splitless mode. Dichloromethane and 2N NaOH was used as a diluent. The calibration curve showed good linearity over the 1.69 to 8.40 ppm (limit: 5.56 ppm) and its correlation coefficient was >0.999. A limit of detection 0.50 ppm and limit of quantitation 1.69 ppm was achieved when the samples were prepared at 50 mg/mL. While recovery proved to be 106.8 to 113.5%, hence it signified the matrix effect.

Disappearance of codeine, morphine and 6-MAM in hair after cessation of abuse

Research on the determination of drugs of abuse in hair has established that drugs can be detected in hair even long after cessation of use. The purpose of this study was to analyze hair samples from chronic opioid users who were beginning a controlled drug cessation program. The study population (n = 15) is involved in a drug rehabilitation program in Santiago de Compostela, Spain. Over a 6-month period, subjects provided hair samples at 2-month intervals, with the first sample collected on the day they began the program. Codeine, morphine, and 6-MAM were analyzed by GC/MS (LOQ = 0.2 ng/mg). Hair tresses were divided into 1 cm segments and analyzed for all analytes 0–1 cm corresponding to the proximal portion to the scalp Following cessation of opioid use, traces of codeine, morphine, and 6-MAM still remained in the newly growing hair segments for a specified period. After 2 months, still 27 % of the users tested positive, and at 4 months, 20 % were positive but only for 6-MAM. However, after 6 months of abstinence, the results were negative for all analytes.

Fourier–Hermite Dynamic Programming for Optimal Control

In this paper, we propose a novel computational method for solving non-linear optimal control problems. The method is based on the use of Fourier–Hermite series for approximating the action-value function arising in dynamic programming instead of the conventional Taylor-series expansion used in differential dynamic programming (DDP). The coefficients of the Fourier–Hermite series can be numerically computed by using sigma-point methods, which leads to a novel class of sigma-point based dynamic programming methods. We also prove the quadratic convergence of the method and experimentally test its performance against other methods.

Microcontrollers programming for control and automation in undergraduate biotechnology engineering education

This paper presents the utilization of the ESP32 microcontroller as a teaching tool for signal acquisition, processing, and control theory in biotechnological engineering. The ESP32 microcontroller, equipped with Bluetooth and WiFi capabilities, offers an affordable and versatile solution for educational purposes. By leveraging the Arduino© software, students can easily learn microcontroller programming and utilize various peripherals such as sensors and actuators. Several practical exercises related to process control have been conducted using this microcontroller. Additionally, remote process monitoring and control are enabled through integration with a database. Furthermore, concepts of artificial intelligence are explored using the Edge Impulse platform to obtain an artificial neural network that can be downloaded onto the ESP32. Positive feedback from students highlights the effectiveness and engagement of utilizing these microcontrollers, and the integration of internet connectivity enhances the overall learning experience.

Multi-sensor monitoring of powder melting states via melt pool optical emission signals during laser-based powder bed fusion

The instability of the powder melting states is a common problem in laser-based powder bed fusion of metals (PBF-LB/M). Optical signal monitoring based on melt pool emissions has great potential to solve this problem, but there is a lack of research on the inherent correlations between the optical emissions and powder melting states. In this paper, a synchronous monitoring method of multi-sensor signals based on automatic trigger by numerical control programming was proposed. Then the relationships between the characteristic changes in melt pool signals and the powder melting state variations were carefully examined based on the statistical method. The results showed that the data collection method achieved synchronous monitoring of multi-sensor signals in the target process. The combination of the melt pool light intensity and melt pool area can effectively capture powder melting state variations owing to the changes of powder thickness. Among them, the melt pool light intensity can accurately monitor powder melting state variations caused by the powder thickness of 30 μm to 210 μm. While the melt pool area is susceptible to powder melting state variations caused by the powder thickness of 210 μm to 300 μm. Meanwhile, two emission characteristics (mean intensity and standard deviation) of the optical emissions were closely related to the melting states and stability of the powder.

Two-stage dispatch optimal model of virtual power plant considering pumped storage

When virtual power plant (VPP) participates in power grid dispatch, it not only faces the problem of market bidding but also has the security and stability problems of voltage, frequency, power flow, and so on. The optimal dispatch strategy of VPP, which participates in a hybrid market, is proposed. The two-stage optimal model is established. An economic dispatch optimal model based on robust control and stochastic linear programming is established to maximize the profit of VPP. Aiming to minimize the difference between the output power of each distribution energy resource and the optimal results of economic dispatch, a safety dispatch optimal model based on a particle swarm optimization algorithm is established. The simulation results with the measured data of the Ningxia power grid show that the two-stage optimal dispatch model is effective.

The Design of a Reaction Flywheel Speed Control System Based on ADRC

The reaction flywheel is a crucial operational component within a satellite’s attitude control system. Enhancing the performance of the reaction flywheel speed control system holds significant importance for satellite attitude control. In this paper, an active disturbance rejection control (ADRC) approach is introduced to mitigate the impact of uncertain disturbances on reaction flywheel speed control precision. The reaction flywheel speed control system is designed as an ADRC controller due to the current challenge of measuring unknown disturbances accurately in the reaction flywheel system. To derive the rotor’s speed observation value and the estimated total disturbances value, the sampled data of the reaction flywheel rotor position and torque control signal are fed into the extended state observer. The estimated total disturbances value is compensated on feedforward control, which could mitigate significantly the effects of various nonlinear disturbances. The paper initially establishes the rationale behind the reaction flywheel ADRC controller through theoretical analysis, followed by analysis of the differences of performance of reaction flywheel control by the ADRC controller and the PID controller in MATLAB/SIMULINK. Simulation results demonstrate the evident advantages of the ADRC controller over the PID controller in terms of speed command tracking capability and disturbances suppression ability. Subsequently, the ADRC controller program and the PID controller program are implemented on the reaction flywheel control circuit, and experiments are conducted to contrast speed command tracking and disturbance suppression. Importantly, the experimental outcomes align with the simulation results.

การพัฒนาชุดฝึกทักษะการเขียนโปรแกรมควบคุมด้วยสัญญาณแอนาล็อกและดิจิทัล

การพัฒนาชุดฝึกทักษะการเขียนโปรแกรมควบคุมด้วยสัญญาณแอนาล็อกและดิจิทัล

Utilizing Virtual Laboratory to Improve CNC Distance Learning of Vocational Students at Higher Education

This research develops learning materials for Computer Numerical Control (CNC) programming courses based on virtual reality technology. The impact of the Covid-19 pandemic on social activities, one of which is the educational aspect, has changed the learning pattern to a massive one where the learning process that should be carried out face-to-face, but now must carry out distance learning. Therefore, alternative learning media is needed to support the practicum process from a distance by utilizing virtual reality technology that is simulated using digital devices. The Software engineering models, namely: requirements, design, implementation, verification, and maintenance phases are the five primary stages used in this study, which uses the waterfall development model research method. The feasible of the virtual laboratory obtained the results of material validation, learning media, and small group trials of 12 diploma students of Mechanical Engineering at Universitas Negeri Padang. The findings of the study found that small group trials in CNC virtual laboratory can be successfully utilized in CNC programming courses. This research is expected to have implications for improving learning outcomes in CNC programming courses by using virtual laboratory technology in creating a conducive and quality learning atmosphere, so that the learning material provided can be accepted by students.

Torymus sinensis and its close relatives in Europe: a multilocus phylogeny, detailed morphological analysis, and identification key

The introduction of the biological control agent Torymus sinensis Kamijo (Hymenoptera, Chalcidoidea, Torymidae) to control the populations of the chestnut gall wasp Dryocosmus kuriphilus Yasumatsu (Hymenoptera, Cynipidae) is considered one of the successful programs in biological control. The species was involved in interspecific hybridisation in Japan and the specimens imported into Europe were derived from this hybrid lineage, showing signs of introgression. The discovery of mitochondrial haplotypes or possible Enolase haplotypes from T. beneficus or of specimens with shorter ovipositor does not necessarily imply that T. beneficus is present in Europe, only that the European specimens are of hybrid origin. Of the native European Torymus species associated with D. kuriphilus, the molecular and morphometric results indicate Torymus notatus (Walker) as the closest species to T. sinensis. The two are part of the same species-group (cyaneus group), are nested together in the multivariate ratio analysis and are the closest genetically based on all three nuclear markers: Enolase (1.5% divergence), Wingless (2%) and ITS2 (13%). However, on the mitochondrial marker COI the closest species is Torymus rubi (Schrank) at 9.9% divergence. As such, T. notatus is the most likely candidate for accidental interspecific hybridisation if this is to happen in Europe. We provide an illustrated identification key for the European species of Torymus associated with D. kuriphilus, an important but lacking tool for biological control programs.

Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control

Sequential Convex Programming (SCP) has recently gained significant popularity as an effective method for solving optimal control problems and has been successfully applied in several different domains. However, the theoretical analysis of SCP has received comparatively limited attention, and it is often restricted to discrete-time formulations. In this paper, we present a unifying theoretical analysis of a fairly general class of SCP procedures for continuous-time optimal control problems. In addition to the derivation of convergence guarantees in a continuous-time setting, our analysis reveals two new numerical and practical insights. First, we show how one can more easily account for manifold-type constraints, which are a defining feature of optimal control of mechanical systems. Second, we show how our theoretical analysis can be leveraged to accelerate SCP-based optimal control methods by infusing techniques from indirect optimal control.

Receding Horizon Actor–Critic Learning Control for Nonlinear Time-Delay Systems With Unknown Dynamics

With the development of modern mechatronics and networked systems, the controller design of time-delay systems has received notable attention. Time delays can greatly influence the stability and performance of the systems, especially for optimal control design. In this article, we propose a receding horizon actor–critic learning control approach for near-optimal control of nonlinear time-delay systems (RACL-TD) with unknown dynamics. In the proposed approach, a data-driven predictor for nonlinear time-delay systems is first learned based on the Koopman theory using precollected samples. Then, a receding horizon actor–critic architecture is designed to learn a near-optimal control policy. In RACL-TD, the terminal cost is determined by using the Lyapunov–Krasovskii approach so that the influences of the delayed states and control inputs can be well addressed. Furthermore, a relaxed terminal condition is present to reduce the computational cost. The convergence and optimality of RACL-TD in each prediction interval as well as the closed-loop property of the system are discussed and analyzed. Simulation results on a two-stage time-delayed chemical reactor illustrate that RACL-TD can achieve better control performance than nonlinear model predictive control (MPC) and infinite-horizon adaptive dynamic programming. Moreover, RACL-TD can have less computational cost than nonlinear MPC.

Knowledge, attitude, and practice of nurse aids and service staff about nosocomial infection control: A case study in Iran

Background: Hospital-acquired infection (HAI) or nosocomial infection is a major public health concern. In this study, the status of knowledge, attitude, and practice (KAP) of service staff and nurse aids in reference to HAI was investigated. Methods: This descriptive-analytic study was carried out to investigate KAP in two steps design and measurement. Twenty-five hospitals were included in this study. To collect data, a questionnaire comprised of 8 divisions and 45 questions was designed and validated, and the three sections of KAP were listed. The questionnaire was distributed to 405 nurse aids and service staff to determine their level of KAP towards HAI. The data were analyzed using SPSS 18. Results: The mean±standard deviation values of KAP of the included staff were 7.07±1.97, 23.08±4.38, and 35.83±8.25, respectively. A statistically significant difference was observed in the level of KAP of personnel among different hospitals (P<0.001). The type of employment status and education was found to be significantly influential on knowledge (P=0.027, P<0.001) and level of education. Marital status, education and career experience were found to be significantly influential on attitude (P=0.034, P=0.004 and 0.045). Level of education, job category, career experience, and gender were the significant influential factors on practice (P<0.05). Conclusion: The knowledge and practice levels were low in many nurse aids and service staff. An educational plan and compiled programs in hospital infection control seem to be necessary to effectively control the major concern of HAI in hospitals in Iran.

Advances, challenges and outlooks in frost-free air-source heat pumps: A comprehensive review from materials, components to systems

Air-source heat pumps (ASHPs) have dominated the heat pump market, accounting for over 90% of the market share, due to their potential to reduce emissions, low operational costs, and ease of maintenance. However, ASHPs are often inefficient in the summer and prone to frosting in the winter. Frost-free air-source heat pumps (FFASHPs) have recently emerged as a promising solution for both heating and cooling, achieving high efficiency and eliminating frost. This review provides a comprehensive summary of the latest advancements in FFASHPs, focusing on materials, components, and systems. Specifically, we begin by discussing the frost-free mechanism of FFASHPs, followed by a systematic presentation of the solutions used in FFASHPs in terms of their materials and properties. Additionally, we summarize breakthroughs in heat and mass exchangers, including the heat and mass transfer characteristics, models, and coefficients. We also discuss regeneration methods of solutions in terms of driving force, regeneration rate, and efficiency. Furthermore, we demonstrate the principle and potential of energy savings, regional suitability, and design and operational optimization of the system. Finally, we provide insights and perspectives on FFASHPs to further improve their efficiency and promote their application, including the development of a thermophysical property and corrosion database for solutions, intelligent screening of solutions, uniform correlation expressions of heat and mass transfer coefficients, comprehensive analysis and comparison of different regeneration approaches, obtaining dynamic properties of FFASHPs for predictive control and dynamic programming, and developing design standards for FFASHPs.

Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. <i>Note to Practitioners</i>&#x2014;This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

Randomized control trial of a childhood obesity prevention family-based program: "Abriendo Caminos" and effects on BMI.

Hispanic individuals are at increased risk for obesity and other chronic health conditions. This article evaluates the effect of a family-based, childhood obesity primary prevention intervention in a community setting. A multi-site, randomized controlled trial community program with assessments at pre (T0), post-program (T1), and 6-months post-program (T2). Participating families were recruited from five sites. Only families of Mexican or Puerto Rican heritage with a least one child between 6 and 18 years were included in the study, without weight restrictions. Families were randomized to the intervention and control arms. Intervention families received six-2 h weekly workshops. Control families received printed generic nutrition and wellness information. Heights and weights were measured at the 3-time points to calculate BMI z-scores, BMI-percentiles, and weight status using age- and sex-specific growth charts, according to the CDC guidelines. There were no differences in BMI-z scores between children in the intervention (n = 239) and control groups (n = 187) at T0. BMI z-scores decreased in the intervention group (-0.03, 95% CI, -0.066, -0.003, p = 0.032) at T1, but not in the control group at T1. Changes in BMI z-scores were not statistically significant at T2. The Abriendo Caminos intervention effectively prevented unhealthy weight gain in Hispanic children in the short-term, but not at 6-months post-intervention. Younger children and girls benefited more from the program at 6-months post-intervention. Additional efforts are needed to sustain long-term changes. Culturally-tailored programs can provide families with the knowledge to produce short-term changes and a potential pathway for sustainable changes in implementing healthy behaviors.