Control Operations Research Articles

Efficient tensor networks for control-enhanced quantum metrology

Optimized quantum control can enhance the performance and noise resilience of quantum metrology. However, the optimization quickly becomes intractable when multiple control operations are applied sequentially. In this work, we propose efficient tensor network algorithms for optimizing strategies of quantum metrology enhanced by a long sequence of control operations. Our approach covers a general and practical scenario where the experimenter applies N−1 interleaved control operations between N queries of the channel to estimate and uses no or bounded ancilla. Tailored to different experimental capabilities, these control operations can be generic quantum channels or variational unitary gates. Numerical experiments show that our algorithm has a good performance in optimizing the metrological strategy for as many as N=100 queries. In particular, our algorithm identifies a strategy that can outperform the state-of-the-art strategy when N is finite but large.

Modeling and Control of a Dual Stator Multiphase Induction Motor for Electric Vehicle Technology

This paper presents the dynamic modeling and speed control of a Dual Stator Multiphase Induction Motor (DSMIM) in MATLAB/Simulink environment. The torque production and load sharing capability of the model is analyzed under free acceleration conditions when subjected to a frictional load profile. Its speed control operation is realized using the conventional V/f and vector based Indirect Field-Oriented Control (IFOC) methods. The torque and speed tracking ability of the machine with the chosen control techniques were simulated. The better dynamic performance of IFOC controlled DSMIM drive model establishes its utility in Electric Vehicles (EV) technology and research. Credibility of the proposed model in EV application is verified by testing it against the standard Modified Indian Drive Cycle (MIDC). The corresponding torque and speed waveforms obtained under the drive cycle analysis are illustrated. The proposed model well supports sudden load changes with a better load sharing competence.

Application of forecast‐informed reservoir operations at US Army Corps of Engineers dams in California

AbstractThe US Army Corps of Engineers (USACE) prescribes flood control operations for reservoirs it regulates in watershed‐specific water control manuals (WCMs), which can be decades‐old and may not capture changed conditions in the watersheds or include the benefit of state‐of‐the‐science weather and streamflow prediction. Considering the specific characteristics of a reservoir, forecast‐informed reservoir operations (FIRO) may be used to enhance flood risk reduction, improve water availability, and achieve other benefits. The first FIRO pilot project at Lake Mendocino in California focused on determining if water supply reliability could be improved using FIRO without increasing flood risk. The final report concluded that FIRO concepts could indeed improve water supply reliability while enhancing flood risk reduction. Subsequently, USACE chose additional reservoir systems in California with different characteristics as additional pilot study locations to further investigate FIRO concepts. These successful FIRO efforts have provided justification to continue its expansion beyond the initial pilot sites. The lessons learned from the FIRO pilot projects are being used to inform the development of the FIRO Screening Process, a screening level framework intended to scale up the implementation of FIRO. The lessons learned could support FIRO implementation at suitable USACE reservoirs by updating WCMs.

The multisensory control of sequential actions

Many motor tasks are comprised of sequentially linked action phases, as when reaching for, lifting, transporting, and replacing a cup of coffee. During such tasks, discrete visual, auditory and/or haptic feedback are typically associated with mechanical events at the completion of each action phase, as when breaking and subsequently making contact between the cup and the table. An emerging concept is that important sensorimotor control operations, that affect subsequent action phases, are centred on these discrete multisensory events. By predicting sensory feedback at the completion of action phases, and comparing with the actual feedback that arises, task performance can be continuously monitored. If errors are detected, the sensorimotor system can quickly respond with task-protective corrective actions. The aim of this study was to investigate how discrete multisensory feedback at the completion of action phases are used in these control operations. To investigate this question, 42 healthy human participants (both male and female) performed a visually guided sequential reaching task where auxiliary discrete visual, auditory and/or haptic feedback was associated with the completion of action phases. Occasionally however, this feedback was removed in one or two modalities. The results show that although the task was visually guided, its control was critically influenced by discrete auditory and haptic feedback. Multisensory integration effects occurred, that enhanced the corrective actions, when auditory feedback was unexpectedly removed along with haptic or visual feedback. This multisensory enhancement may facilitate the ability to detect errors during sequential actions and amplify task-protective corrective actions.

Factorial inputs and outputs: A virtual plant for industrial control training and operations

The field of manufacturing and production is rapidly changing because of the fourth Industrial Revolution or Industry 4.0 and bridging theory and practice is a major requirement in industrial control and automation training, operations and virtual simulation is one such way to help bridge this kind of gap. One of such simulation program that has gained popularity among companies recently is Factory inputs and outputs (Factory I/O). This paper presents an example of a used case study of FACTORY I/O. The tank scene in the Factory I/O was used for level control and the control algorithm was implemented in the TIA portal using Siemens S7 1500 PLC integrated with the factory I/O to carry out the control of the liquid level in the tank by controlling the fill valve on the level control rig. The S7-PLCSIM TIA portal V15 communicated with the PLC in the Factory I/O drivers through an IP address. Therefore, with virtual simulation system, it is possible to test controller and control configuration before applying it to a physical system, with a methodology matching the fourth industrial revolution in a simulation environment in which theory and practice is bridged for real-time simulations.

Quiescent cells maintain active degradation-mediated protein quality control requiring proteasome, autophagy and nucleus-vacuole junctions

Many cells spend a major part of their life in quiescence, a reversible state characterized by a distinct cellular organization and metabolism. In glucose-depleted quiescent yeast cells, there is a metabolic shift from glycolysis to mitochondrial respiration, and a large fraction of proteasomes are reorganized into cytoplasmic granules containing disassembled particles. Given these changes, the operation of protein quality control (PQC) in quiescent cells, in particular the reliance on degradation-mediated PQC and the specific pathways involved, remains unclear. By examining model misfolded proteins expressed in glucose-depleted quiescent yeast cells, we found that misfolded proteins are targeted for selective degradation requiring functional 26S proteasomes. This indicates that a significant pool of proteasomes remains active in degrading quality control substrates. Misfolded proteins were degraded in a manner dependent on the E3 ubiquitin ligases Ubr1 and San1, with Ubr1 playing a dominant role. In contrast to exponentially growing cells, the efficient clearance of certain misfolded proteins additionally required intact nucleus-vacuole junctions (NVJ) and Cue5-independent selective autophagy. Our findings suggest that proteasome activity, autophagy, and NVJ-dependent degradation operate in parallel. Together the data demonstrate that quiescent cells maintain active PQC that relies primarily on selective protein degradation. The necessity of multiple degradation pathways for the removal of misfolded proteins during quiescence underscores the importance of misfolded protein clearance in this cellular state.

Analysis of potential risks during the control of an unmanned underwater vehicle (UUV)

This article explores the analysis of potential risks associated with controlling unmanned underwater vehicles (UUVs), focusing on operational, environmental, and technological challenges. UUVs play a critical role in marine research, defense, and offshore industries but are subject to unique risks due to limited visibility, communication latency, and unpredictable underwater conditions. While UUVs have revolutionized underwater exploration and navigation, their reliance on advanced technologies introduces unique challenges. The study identifies the primary risk domains of UUV operations, including operational risks, human-machine interaction vulnerabilities, and environmental unpredictability. Using comprehensive scenario analysis and data from practical implementations, the article evaluates key risks such as navigation errors, system malfunctions, and communication disruptions. It further examines how artificial intelligence (AI) can be leveraged to mitigate these risks through real-time trajectory optimization, adaptive learning, and enhanced situational awareness. This research incorporates scenario-based analyses for UUVs to evaluate the most critical risk factors affecting mission safety. Furthermore, it explores how modern technologies can mitigate these risks by enhancing trajectory optimization and monitoring of UUVs during underwater navigation. By providing a holistic understanding of UUV risks, the findings underscore the need for robust control systems and operator training protocols. The research contributes actionable insights for improving UUV safety, efficiency, and reliability, laying the groundwork for further advancements in maritime robotics

Implementation of ESP32-Based Web Host For Control and Monitoring of Robotic Arm

In the modern technological era, the Internet of Things (IoT) plays a significant role in developing robotic control systems, including robotic arms. This study explores the implementation of an ESP32-based webhost to control and monitor robotic arm operations in real time via a web interface connected to a Wi-Fi network. The system is designed to provide ease of access, reliable performance, and low latency, making it suitable for education, technical training, and other applications. Testing revealed that the system delivers fast response times, high stability under moderate loads, and an intuitive interface for users with varying skill levels. These findings present opportunities for further development, including integration with cloud-based technologies and data security enhancements

Forecasting the relative abundance of Aedes vector populations to enhance situational awareness for mosquito control operations.

Aedes-borne diseases represent a major public health threat and mosquito control operations represent a key line of defense. Improving the real-time awareness of mosquito control authorities by providing reliable forecasts of the relative abundance of mosquito vectors could greatly enhance control efforts. To this aim, we developed an analytical tool that forecasts Aedes aegypti relative abundance 1 to 4 weeks ahead. Forecasts were validated against mosquito surveillance data (2,760 data points) collected over multiple years in four jurisdictions in the US. The symmetric absolute percentage error was in the range 0.43-0.69, and the 90% interquantile range of the forecasts had a coverage of 83-92%. Our forecasts consistently outperformed a reference "naïve" model for all analyzed study sites, forecasting horizon, and for periods with medium/high Ae. aegypti activity. The developed tool can be instrumental to address the need for evidence-based decision making.

External archive guided radial-grid multi objective differential evolution

Differential evolution (DE) is a robust evolutionary algorithm for solving single-objective and multi-objective optimization problems (MOPs). While numerous multi-objective DE (MODE) variants exist, prior research has primarily focused on parameter control and mutation operators, often neglecting the issue of inadequate population distribution across the objective space. This paper proposes an external archive-guided radial-grid-driven differential evolution for multi-objective optimization (Ar-RGDEMO) to address these challenges. The proposed Ar-RGDEMO incorporates three key components: a novel mutation operator that integrates a radial-grid-driven strategy with a performance metric derived from Pareto front estimation, a truncation procedure that employs Pareto dominance in conjunction with a ranking strategy based on shifted similarity distances between candidate solutions, and an external archive that preserves elite individuals using a clustering approach. Experimental results on four sets of benchmark problems demonstrate that the proposed Ar-RGDEMO exhibits competitive or superior performance compared to seven state-of-the-art algorithms in the literature.

Predicting Operational Events in Mechanized Weed Control Operations by Offline Multi-Modal Data and Machine Learning Provides Highly Accurate Classification in Time Domain

Monitoring of operations has become a critical activity in forestry, aiming to provide the data required by planning and production management. Conventional methods, on the other hand, come at a high expense of resources. A neural network was trained, validated, and tested in this study based on multi-modal data to classify relevant operational events in mechanized weed control operations. The architecture of a neural network was tuned in terms of the number of hidden layers and neurons, and the regularization term was set at various values to obtain optimally tuned models for three data modalities: triaxial acceleration data coupled with speed extracted from GNSS signals (AS), triaxial acceleration (A), and speed alone (S). In the training and validation phase, the models based on AS and A achieved a very high classification accuracy, accounting for 92 to 93% when considering four relevant events. In the testing phase, which was run on unseen data, the classification accuracy reached figures of 91 to 92%, indicating a good generalization ability of the models. The results point out that multimodal data are able to provide the features for distinguishing events and add spatial context to the monitored operations, standing as a suitable solution for offline, partly automated monitoring. Future studies are required to see how the capabilities of online, real-time technologies such as deep learning coupled with computer vision can add more context and improve classification performance.

Managing Residual Heat Effects in Femtosecond Laser Material Processing by Pulse-on-Demand Operation

Femtosecond laser processing combines highly accurate structuring with low residual heating of materials, low thermal damage, and nonlinear absorption processes, making it suitable for the machining of transparent brittle materials. However, with high average powers and laser pulse repetition rates, residual heating becomes relevant. Here, we present a study of the femtosecond laser pulse-on-demand operation regime, combined with regular scanners, aiming to improve throughput and quality of processing regardless of the scanner’s capabilities. We developed two methods to define the needed pulse-on-demand trigger sequences that compensate for the initial accelerating scanner movements. The effects of pulse-on-demand operation were studied in detail using direct process monitoring with a fast thermal camera and indirect process monitoring with optical and topographical surface imaging of final structures, both showing clear advantages of pulse-on-demand operation in precision, thermal effects, and structure shape control. The ability to compensate for irregular scanner movement is the basis for simplified, cheaper, and faster femtosecond laser processing of brittle and heat-susceptible materials.

What motivates the choice to custom hire pest management spraying services?

This article presents a model of how farmers choose to custom hire for pest control. The decision-making process is illustrated through a discrete choice experiment conducted via a pilot survey of soybean growers in Michigan, Illinois, and Indiana. Farmers responded to a hypothetical pest infestation by choosing between custom operators, spraying on their own, or leaving the field to its fate. Among farmers who choose to spray, the mean willingness to pay for marginal increases in timeliness (as defined as the chance of late spraying) ranges from 37 to 52 cents per acre. We also find that farmers more averse to risk are more sensitive to custom operator timeliness and that farmers with better-developed social networks are less sensitive to the risk of delay. The results of this study can motivate future research into the drivers of on-farm decision-making, especially as it relates to custom hire behavior in pest control and other field operations. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A Magnetic Climbing Robot for Steel Bridge Inspection

Abstract. The maintenance of traditional steel bridges presents many significant challenges due to the fact that bridges of different ages and standards vary in design, structural complexity and technical difficulty. In light of these considerations, it is clear that maintenance personnel must possess a wealth of professional expertise and experience in order to effectively address the diverse and intricate maintenance challenges that arise. Moreover, the maintenance process entails high-risk operations such as high-altitude work and welding, which pose a threat to the safety of construction personnel. Therefore, this study introduces a magnetic adsorption robot designed to address the low efficiency and high costs of traditional maintenance methods. This robot features a dual robotic arm mechanism and modeled using the Denavit Hartenberg (D-H) parameter method. Electromagnetic adsorption is utilized to achieve wall adhesion, and direct power supply is provided through DC power to control weight. The robot can perform flexible remote control operations through the HC-05 Bluetooth module. The experimental results show that the robot exhibits robust adhesion and obstacle crossing ability on steel surfaces, thereby enabling unimpeded movement within the steel structure. In addition, the robot is now capable of performing overhaul procedures via remote control.

Carbon Dioxide as a Sustainable Reagent in Circular Hydrometallurgy.

This review highlights the use of CO2 as a reagent in hydrometallurgy, with emphasis on the new concept of circular hydrometallurgy. It is shown how waste CO2 can be utilised in hydrometallurgical operations for pH control or regeneration of acids for leaching. Metal-rich raffinate solutions generated after removal of the valuable metals can serve as feedstocks for mineral carbonation, providing alternative avenues for CO2 sequestration. Furthermore, CO2 can also be used as a renewable feedstock for the production of chemical reagents that can find applications in hydrometallurgy as lixiviant, as precipitation reagent or for pH control. Mineral carbonation can be combined with chemical reactions involving metal complexation reagents, as well as with solvent extraction processes for the concurrent precipitation of metal carbonates and acid regeneration. An outlook for future research in the area is also presented.

Research on Energy Management Technology of Photovoltaic-FESS-EV Load Microgrid System

This study focuses on the development and implementation of coordinated control and energy management strategies for a photovoltaic–flywheel energy storage system (PV-FESS)-electric vehicle (EV) load microgrid with direct current (DC). A comprehensive PV-FESS microgrid system is constructed, comprising PV power generation, a flywheel energy storage array, and electric vehicle loads. The research delves into the control strategies for each subsystem within the microgrid, investigating both steady-state operations and transitions between different states. A novel energy management strategy, centered on event-driven mode switching, is proposed to ensure the coordinated control and stable operation of the entire system. Based on the simulation results, the PV system cannot cope with the load demand power when it is increased to a maximum of 2800 W, the effectiveness of the individual control strategies, the coordinated control of the subsystems, and the overall energy management approach are confirmed. The main contribution of this research is the development of a coordinated control mechanism that integrates PV generation with FESS and EV loads, ensuring synchronized operation and enhanced stability of the microgrid. This work provides significant insights into optimizing energy distribution and minimizing losses within microgrid systems, thereby advancing the field of energy management in DC microgrids.

Molecular Identification of Species Belonging to Culex vishnui Subgroup (Diptera: Culicidae), Vectors of Japanese Encephalitis Virus, in Taiwan.

Classification of mosquitoes with overlapping features remains problematic when using traditional morphological identification alone. In this study, we used molecular methods to elucidate the taxonomic status of Culex tritaeniorhynchus, Culex annulus, and Culex pseudovishnui species as vectors of the Japanese encephalitis virus belonging to the Culex vishnui subgroup and gene flow among them. In this study, 76, 59, and 3 samples of Cx. annulus, Cx. tritaeniorhynchus, and Cx. pseudovishnui, respectively, were collected around Taiwan. Phylogenetic analysis and genetic divergence were based on genomic sequence variations in ribosomal DNA and the internal transcribed spacer (rDNA) and cytochrome c oxidase subunit I (COI). Our results revealed that Cx. annulus and Cx. vishnui are genetically similar and share a gene pool among the species from Taiwan and other Asian countries. However, two hidden taxa of Cx. tritaeniorhynchus, which clustered together according to the rDNA sequences, were discovered based on the COI sequences. In addition, Cx. pseudovishnui has different gene pools from those of the strains from other countries, implying that the population from Taiwan is probably either a unique strain or a sibling species. This study provides molecular information on the taxonomic status of the species in the Cx. vishnui subgroup in Taiwan and gene flow between these species, providing valuable information for vector control operations and the delineation of the evolutionary process.

FUZZY-PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER WITH INTERACTIVE DECISION TREE

The STATCOM is extensively used in the power system to address the power quality issues by actively compensating the reactive power requirements of the load. However, the STATCOM performance depends on the underlying controller operation to handle sudden load changes and disturbances optimally with faster response. To solve this issue, this paper presents the intelligent algorithm-optimized fuzzy rule-based Proportional Integral Derivative (PID) controller to enhance the performance of the STATCOM. The proposed controller consists of a fuzzy-PID controller capable of handling non-linear dynamics and sudden changes in the load. The interactive decision tree (IDT) technique detects weaker metrics and improves their influence in control scenarios. The Biogeography-based optimization (BBO) algorithm minimizes the controller's integral absolute error to tune the fuzzy-PID controller parameters. The algorithm terminates once all the metrics are tuned to satisfaction with the operator’s consent. The proposed IDT-based fuzzy PID controller is tested on a power system containing a nonlinear load, and its performance is compared with the existing controller and simulated using the MATLAB/Simulink tool. The proposed controller provides fast control action, reduces the reactive power from the source by 42.5%, and improves the power factor by 1.5%.

Optimization of the integro-differential hyperbolic model

We consider a model of processes in viscoelastic media. The force arising inside the body may depend not only on the current values but also on the entire time history of the motion (for example, polymers, emulsions, and suspensions exhibit memory properties). Such processes often lead to the necessity of studying hyperbolic integro-differential equations with Volterra integral term. The main goal is to prove the existence and uniqueness of generalized solutions to the corresponding initial-boundary value problem, as well as to investigate the existence of optimal control for systems described by these models. The main results are obtained using the method of a priori inequalities in negative norms. In particular, we justify inequalities in negative norms for the integro-differential operator in certain functional spaces. The work begins with a short description of relevant results with similar formulations and referencing works with physical justification of the model. Afterwards, we describe the problem statement, constraints on equation parameters, and functional spaces used for the investigation. Below are the main results, in particular definitions of generalized solutions and properties on the well-posed of the initial-boundary value problem as well as the existence of optimal control. Control is carried out through the right-hand side of the equation using a certain special operator. It is possible to examine specific examples of such control operators (illustrating various control mechanisms) and the corresponding control spaces. By utilizing proven inequalities and relying on general results of S.I. Lyashko from the theory of a priori inequalities in negative norms, we establish sufficient conditions for the existence of optimal control. In particular, we impose the restrictions on the admissible set of controls and the quality criterion.

Composite Materials For Creation Of Anti-Icing Elements And Other Aircraft Systems

The relevance of the development is related to the safety of aircraft flights, which is significantly affected by aircraft icing. The reliability of the anti-icing system (AIC) is a prerequisite for all modern aircraft, as well as for high-altitude unmanned aerial vehicles (UAVs). Icing of aircraft and UAVs is a very urgent problem, as evidenced by a large number of foreign publications and patents on proposals for methods of removing ice from metal and composite structures using nanomaterials. Icing can lead to a decrease in the lifting power of the aircraft and an increase in drag, and also affects the operation of the aircraft controls, reduces visibility from the cockpit, increases vibration and load on individual elements of the airframe, and negatively affects the operation of engines