Artificial Control Research Articles

Development of a dual-axis solar tracker for efficient sun energy harvesting

Climate change, global warming, and rising fossil fuel pose significant challenges in present days across worldwide. To combat these issues, governments and organizations are promoting renewable energy sources, such as solar power, which converts sunlight into electricity or thermal energy. In the current scenario, static-oriented Photovoltaic (PV) panels are hampered by fluctuations in the sun's trajectory, leading to suboptimal solar energy conversion. To address these issues, this manuscript proposes a low-cost prototype of a two-axis solar tracker that integrates four optical sensor modules as feedback sensors and two direct current geared motors to maximize solar energy harvesting. Comparative analysis between a fixed-oriented PV panel and the solar tracker, using conventional on–off and artificial intelligence-based fuzzy logic control methods, verifies the two-axis solar tracker's performance. The fuzzy logic controller-based solar tracker net energy gains approximately 14.2% more power than the fixed-oriented PV panel daily and boasts an average tracking error of 0.29°, indicating its reliability.

Modified SEPIC converter fed motor drive using ANN controller

Modern day-to-day devices also use an AC source as an input while working with DC voltage. An AC-DC converter is used to get an effective and versatile DC output. Without sacrificing conversion efficiency, however, traditional converters are not suitable for high voltage output. Within this paper a new topology called the Cascaded Boost-SEPIC (CBS) converter is suggested. The converter is validated on the basis of conversion efficiency, minimum THD and maximum power factor. The results obtained evinces the improvement achieved in the power quality. Key Words: AC-DC Converter; ANN (Artificial Neural Network controller); Separately excited DC motor

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods.

Integrating Digital Twins with BIM for Enhanced Building Control Strategies: A Systematic Literature Review Focusing on Daylight and Artificial Lighting Systems

In the architecture, engineering, and construction industries, the integration of Building Information Modeling (BIM) has become instrumental in shaping the design and commissioning of smart buildings. At the center of this development is the pursuit of more intelligent, efficient, and sustainable built environments. The emergence of smart buildings equipped with advanced sensor networks and automation systems increasingly requires the implementation of Digital Twins (DT) for the direct coupling of BIM methods for integral building planning, commissioning, and operational monitoring. While simulation tools and methods exist in the design phase of developing advanced controls, their mapping to construction or post-construction models is less well developed. Through systematic, keyword-based literature research on publisher-independent databases, this review paper gives a comprehensive overview of the state of the research on BIM integration of building control systems with a primary focus on combined controls for daylight and artificial lighting systems. The review, supported by a bibliometric literature analysis, highlights major development fields in HVAC controls, failure detection, and fire-detection systems, while the integration of daylight and artificial lighting controls in Digital Twins is still at an early stage of development. In addition to already existing reviews in the context of BIM and Digital planning methods, this review particularly intends to build the necessary knowledge base to further motivate research activities to integrate simulation-based control methods in the BIM planning process and to further close the gap between planning, implementation, and commissioning.

Artificial intelligence-based position control: reinforcement learning approach in spring mass damper systems

This work examines the use of deep Reinforcement Learning (RL) in mass-spring system position control, providing a fresh viewpoint that goes beyond conventional control techniques. Mass-spring systems are widely used in many sectors and are basic models in control theory. The novel aspect of this approach is the thorough examination of the impact of several optimizer algorithms on the RL methodology, which reveals the optimal control tactics. The research applies a Deep Deterministic Policy Gradient (DDPG) algorithm for continuous action spaces, where the actor and critic networks are important components in assessing the agent’s performance. The RL agent is trained to follow a reference trajectory using the Simulink environment for system modeling. The study provides insights into the agent’s learning approach and performance optimization by evaluating the training process using force-time graphs, reward graphs, and Episode Manager charts. Furthermore, the effect of different combinations of optimizers on the control performance of the agent is examined. The outcomes highlight the importance of optimizer selection in the learning process by revealing significant variations in training times. As a result, a better understanding of the relationship between various optimizers and control performance is provided by this study’s novel application of reinforcement learning in mass-spring system control. The results raise the possibility of more potent methods for controlling complex systems and add to the expanding field of study at the interface of control theory and deep learning.

Transcriptome analysis reveals regulatory mechanisms of different drought-tolerant Gleditsia sinensis seedlings under drought stress

BackgroundGleditsia sinensis is a significant tree species from both ecological and economic perspectives. However, its growth is hampered by temporary droughts during the seedling stage, thereby impeding the development of the G. sinensis industry. Drought stress and rehydration of semi-annual potted seedlings using an artificial simulated water control method. RNA sequencing (RNA-seq) analyses were conducted on leaves collected from highly resistant (HR) and highly susceptible (HS) seedling families at five different stages during the process of drought stress and rehydration to investigate their gene expression patterns.ResultsThe differentially expressed genes (DEGs) were predominantly enriched in pathways related to “chloroplast” (GO:0009507), “photosynthesis” (GO:0015979), “plant hormone signal transduction” (map04075), “flavonoid biosynthesis” (map00941), “stress response”, “response to reactive oxygen species (ROS)” (GO:0000302), “signal transduction” (GO:0007165) in G. sinensis HR and HS families exposed to mild and severe drought stress. Additionally, the pathways related to “plant hormone signal transduction” (map04075), and osmoregulation were also enriched. The difference in drought tolerance between the two families of G. sinensis may be associated with “transmembrane transporter activity” (GO:0022857), “stress response”, “hormones and signal transduction” (GO:0007165), “cutin, suberine and wax biosynthesis” (map00073), “ribosome” (map03010), “photosynthesis” (map00195), “sugar metabolism”, and others. An enrichment analysis of DEGs under severe drought stress suggests that the drought tolerance of both families may be related to “water-soluble vitamin metabolic process” (GO:0006767), “photosynthesis” (map00195), “plant hormone signal transduction” (map04075), “starch and sucrose metabolism” (map00500), and “galactose metabolism” (map00052). Osmoregulation-related genes such as delta-1-pyrroline-5-carboxylate synthase (P5CS), Amino acid permease (AAP), Amino acid permease 2 (AAP2) and Trehalose-phosphate synthase (TPS), as well as the antioxidant enzyme L-ascorbate peroxidase 6 (APX6), may be significant genes involved in drought tolerance in G. sinensis. Five genes were selected randomly to validate the RNA-seq results using quantitative real-time PCR (RT-qPCR) and they indicated that the transcriptome data were reliable.ConclusionsThe study presents information on the molecular regulation of the drought tolerance mechanism in G. sinensis and provides a reference for further research on the molecular mechanisms involved in drought tolerance breeding of G. sinensis.

Design of 2DOF control system fused with artificial intelligence for power enhancement and mitigation of degradation in fuel cell systems

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model.

Joint control strategy for daylighting and artificial lighting based on mathematical modeling and differential evolution

ABSTRACT Creating a healthy and comfortable light environment in buildings is crucial for enhancing individuals’ well-being and work efficiency. The joint control of daylighting and artificial lighting has become a research hotspot in the field. The application of emerging electrochromic glass and dimmable luminaires addresses the challenge of managing illuminance levels through a combination of daylight and artificial lighting control. The glass control strategy is determined under specific conditions, and the differential evolution (DE) is employed to optimize the dual-channel output percentages of light-emitting diode (LED) luminaires for designated combinations. The results show that the modeled and algorithmically solved average illuminance exhibits an error rate of less than 1% from the target values. The joint control strategy takes into account visual effects of the light environment, with potential benefits for energy efficiency, occupant comfort, and cognitive performance. It contributes to a holistic understanding of lighting control strategies that optimize illuminance in indoor spaces, ultimately fostering a healthier and more productive environment for occupants.

Artificial Intelligence-Based Hierarchical Control Design for Current Sharing and Voltage Restoration in DC Microgrid of the More Electric Aircraft

Artificial Intelligence-Based Hierarchical Control Design for Current Sharing and Voltage Restoration in DC Microgrid of the More Electric Aircraft

A Virtual MPC-Based Artificial Neural Network Controller for PMSM Drives in Aircraft Electric Propulsion System

A Virtual MPC-Based Artificial Neural Network Controller for PMSM Drives in Aircraft Electric Propulsion System

Research on Ai Technology of Intelligent Information Visualization Based on Rtos System Service

In order to improve the visual interaction ability of intelligent information, an intelligent information visualization artificial intelligence technology based on RTOS system service is proposed in this paper. Based on embedded integrated control, this paper constructs the system design scheme, client and host system of intelligent information visualization artificial intelligence RTOS service system. The information monitoring module, artificial intelligence programmable logic control module, artificial intelligence processing module and human-computer interaction module of intelligent information visualization artificial intelligence RTOS service system are established to realize the label identification and identification in the process of intelligent information visualization artificial intelligence RTOS service system based on unified resource locator sipur, The communication system model of intelligent information visualization artificial intelligence RTOS service system under the transmission mode of multimedia communication channel is established to realize the automatic information processing of intelligent information visualization RTOS system. The test results show that the design has good stability, strong intelligent information visualization and interaction ability, low memory and time cost.

Recent advances in the precision control strategy of artificial pancreas.

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Plasma-Driven Sciences: Exploring Complex Interactions at Plasma Boundaries

Plasma-driven science is defined as the artificial control of physical plasma-driven phenomena based on complex interactions between nonequilibrium open systems. Recently, peculiar phenomena related to physical plasma have been discovered in plasma boundary regions, either naturally or artificially. Because laboratory plasma can be produced under nominal conditions around atmospheric pressure and room temperature, phenomena related to the interaction of plasma with liquid solutions and living organisms at the plasma boundaries are emerging. Currently, the relationships between these complex interactions should be solved using science-based data-driven approaches; these approaches require a reliable and comprehensive database of dynamic changes in the chemical networks of elementary reactions. Consequently, the elucidation of the mechanisms governing plasma-driven phenomena and the discovery of the latent actions behind these plasma-driven phenomena will be realized through plasma-driven science.

Avoiding fusion plasma tearing instability with deep reinforcement learning

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance1–4. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators5. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D6, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

Green energy-sourced AI-controlled multilevel UPQC parameter selection using football game optimization

The power quality (PQ) has been significantly affected by the integration of intermittent non-conventional sources (NCS) into the local distribution system in addition to the adoption of power electronic technologies to regulate non-linear loads. This article combines the H-bridge cascade five-level unified power quality conditioner (5L-UPQC) with the wind power generation system (WPGS), solar photovoltaic power generation system (SPVGS), and battery storage system (BSS) as an effective approach to address PQ problems. The utilization of the Levenberg–Marquardt backpropagation (LMBP)-trained Artificial neural network controller (ANNC) in the UPQC is recommended for generating appropriate reference signals for the converters. This eliminates the requirement for conventional complex conversions, such as abc, dq0, and αβ. Moreover, the artificial neuro-fuzzy interface system (ANFIS) is recommended for achieving a DC-link balance. Football game optimization (FBGO) is utilized to determine the optimal shunt and series filter characteristics. The major objectives of the proposed system are to reduce the current waveform irregularities, resulting in a decrease in the total harmonic distortion (THD), an enhancement in the power factor (PF), the mitigation of supply voltage imbalances and disturbances, and the maintenance of a steady direct-current link capacitor voltage (DLCV), despite the variations in the load, solar irradiation, and wind velocity. The efficiency of the suggested strategy is assessed using four case studies that involve different loads, variable wind velocities, and source voltage balancing conditions. Based on the simulation studies and obtained results, the suggested method significantly decreases the THD to values of 2.91%, 3.63%, 3.75%, and 3.50%. Additionally, it achieves a power factor of unity, which is considerably lower compared to other multilevel schemes that use the traditional symmetrical reference frame (SRF) and instantaneous reactive power (pq) methods. This design has been executed using the MATLAB/Simulink program.

The impact of artificial intelligence adoption for business-to-business marketing on shareholder reaction: A social actor perspective

While AI applications are becoming ever more important in B2B marketing operations, there is a lack of research to examine whether and how shareholders react to firms' AI-enabled B2B marketing initiatives. Accordingly, the purpose of this study is to explore this process by theoretically building on the social actor perspective of the firm and investigating the impact of AI-enabled B2B marketing initiatives on shareholder reaction measured by abnormal stock returns. By adopting a propensity score matching (PSM) method to generate an artificial control group of firms without adopting AI-enabled B2B marketing initiatives, we conduct an event study based on 174 sample firms (87 treatment firms and 87 matched control firms) publicly listed in the US between 2011 and 2020. The test results suggest that firms implementing AI for B2B marketing receive greater stock returns than their industry peers without AI implementation. In addition, the stock return is more remarkable for firms operating in turbulent environments and with less complex customer bases. A qualitative focus group discussion was conducted to further complement and enrich the findings. This study provides the first empirical evidence regarding the shareholder reaction to AI-enabled B2B marketing initiatives. The results reveal the significance of the fit between AI-enabled B2B marketing values and firms' business environments. It encourages future studies to investigate AI implementation from the social actor perspective.

Extensively Drug-Resistant Pseudomonas aeruginosa Outbreak Associated With Artificial Tears.

Carbapenemase-producing, carbapenem-resistant Pseudomonas aeruginosa (CP-CRPA) are extensively drug-resistant bacteria. We investigated the source of a multistate CP-CRPA outbreak. Cases were defined as a US patient's first isolation of P. aeruginosa sequence type 1203 with carbapenemase gene blaVIM-80 and cephalosporinase gene blaGES-9 from any specimen source collected and reported to the Centers for Disease Control and Prevention during 1 January 2022-15 May 2023. We conducted a 1:1 matched case-control study at the post-acute care facility with the most cases, assessed exposures associated with case status for all case-patients, and tested products for bacterial contamination. We identified 81 case-patients from 18 states, 27 of whom were identified through surveillance cultures. Four (7%) of 54 case-patients with clinical cultures died within 30 days of culture collection, and 4 (22%) of 18 with eye infections underwent enucleation. In the case-control study, case-patients had increased odds of receiving artificial tears versus controls (crude matched OR, 5.0; 95% CI, 1.1-22.8). Overall, artificial tears use was reported by 61 (87%) of 70 case-patients with information; 43 (77%) of 56 case-patients with brand information reported use of Brand A, an imported, preservative-free, over-the-counter (OTC) product. Bacteria isolated from opened and unopened bottles of Brand A were genetically related to patient isolates. Food and Drug Administration inspection of the manufacturing plant identified likely sources of contamination. A manufactured medical product serving as the vehicle for carbapenemase-producing organisms is unprecedented in the United States. The clinical impacts from this outbreak underscore the need for improved requirements for US OTC product importers.

Enhancing a star algorithm for robot path planning

This paper describes the importance of robot path planning in artificial intelligence and control theory, and proposes three improvements to the A-algorithm: bi-directional A-search, improved heuristic functions and pruning strategies. The performance of the different algorithms in terms of computation time, path length and number of nodes is compared through experiments. Moreover, it is emphasised in the article that in practical applications suitable algorithms and their improvements are selected according to the characteristics of the specific problem and reasonable evaluation criteria are used to measure the performance of the algorithms.

Design of a novel control hysteresis algorithm for photovoltaic systems for harmonic compensation

Solar photovoltaic (PV) system design and integration with an existing AC grid is growing very fast in recent years and used by many of them as they are pollution-free, structure is limited and maintenance free. From the factors considering, the performance of PV system depends upon the inverter output voltage tested for linear, non-linear, with harmonic, and without harmonic loads. Generated due to the nonlinear loads. Better inverter control techniques are developed to maintain grid power quality. This article discusses the analysis and comparison of pulse width modulation (PWM) converters with unique and state of the art nonlinear control schemes and various modulation approaches. The primary objective of this research is controlling an active filtering hysteresis PWM converter with no sensors. A simple structure with hysteresis current control method total harmonic distortion (THD) is lower when compared with the sinusoidal pulse width modulation (SPWM) method. The said claims are supported by employing computer simulations using MATLAB/Simulink and different control approaches, such as proportional plus integral and artificial neural network controllers.

Artificial neural network controller for grid current quality improvement in solid-state transformers

Artificial neural network controller for grid current quality improvement in solid-state transformers