Scheduling Control Research Articles

Rancang Bangun Prototipe Home Automation Menggunakan Teknologi Internet Of Things

Internet of Things (IoT) technology is often used to monitor and control electronic equipment remotely using the internet. The implementation of Internet of Things technology can be applied to home automation. The implementation of IoT in a home automation system (home automation) can be used to control home lights, home temperature, and electronic plates in the home, and it can even be used to determine the whereabouts of home occupants. In this research, we will create a home automation prototype with the Internet of Things concept, which can be built using the NodeMCU module integrated with a web application. The data communication system in home automation will communicate with the MQTT server and can send data (publish) and receive data (subscribe). The home automation system can carry out manual or scheduled monitoring and control. It can provide notifications whenever a change in the condition or status of the device is being monitored or controlled. Changes in conditions can also occur through sensor readings connected to the NodeMCU module.

Multiple schedules of conjugate reinforcement and extinction: A translational model for assessing automatically reinforced behavior.

This study evaluated the extent to which a conjugate reinforcement schedule (CONJ) involving a contingency between pedaling a stationary bike and viewing a preferred movie could serve as a translational preparation for the analysis of automatically reinforced behavior. In part, researchers examined whether providing participants with either an accurate or an inaccurate rule about the extinction (EXT) component of a multiple schedule (MULT) contributed to the development of control by the MULT (CONJ EXT) schedule. Results show schedule control emerged for four of five participants who received the accurate rule and none of the five participants who received the inaccurate rule. In addition, participants who received accurate rules typically increased pedaling during CONJ components that followed two consecutive EXT components, suggesting that they experienced deprivation for audio and visual stimulation generated by pedaling. These preliminary findings suggest that researchers could use this translational preparation to identify matched interventions for some automatically reinforced behavior.

Enhancing traffic signal control with composite deep intelligence

Traffic signal control has always been a hot topic in the field of intelligent transportation. With the increasing complexity of urban traffic conditions due to urbanization, how to develop effective scheduling strategies to adapt to the changing traffic demands has become a key problem in current intelligent transportation. In light of this, this paper focuses on the traffic signal control problem at intersections and proposes a composite intelligent traffic signal control model based on heterogeneous graph neural networks with dual attention mechanisms and deep reinforcement learning. For the first time, the model incorporates the dual attention mechanism in graph neural networks into the traffic signal control, integrating graph neural networks with deep reinforcement learning techniques and traffic intersection scenarios. This allows for the construction of traffic condition models and the scheduling control of traffic resources, catering to the perception and decision-making needs in complex traffic environments. Firstly, the graph relationship representation of intersection resources is established, constructing the graph information structure for traffic flow and signal states. Then, a heterogeneous graph neural network is designed, incorporating both node-level and semantic-level dual attention mechanisms to characterize the traffic state and explore the relationships, enabling the extraction of explicit and implicit information in traffic intersections. Lastly, a deep reinforcement learning algorithm that combines Double Deep Q-Network (DDQN) and Dueling DQN is implemented to improve the algorithm's generalization and execution efficiency, enhancing the adaptability and stability of traffic signal scheduling in complex environments. Simulation tests are conducted on the SUMO simulation platform using real-world application datasets. Compared to four other similar traffic control model, the proposed model demonstrates performance advantages of more than 13% in terms of average reward, average delay, queue length, and waiting time. This validates the effectiveness of the proposed model.

Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers

In order to determine the fatigue state of air traffic controllers from air talk, an algorithm is proposed for discriminating the fatigue state of controllers based on applying multi-speech feature fusion to voice data using a Fuzzy Support Vector Machine (FSVM). To supplement the basis for discrimination, we also extracted eye-fatigue-state discrimination features based on Percentage of Eyelid Closure Duration (PERCLOS) eye data. To merge the two classes of discrimination results, a new controller fatigue-state evaluation index based on the entropy weight method is proposed, based on a decision-level fusion of fatigue discrimination results for speech and the eyes. The experimental results show that the fatigue-state recognition accuracy rate was 86.0% for the fatigue state evaluation index, which was 3.5% and 2.2%higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

Design of acceleration control schedule for adaptive cycle engine based on direct simulation model

To design the optimum acceleration control schedule for the Adaptive Cycle Engine (ACE) in the full flight envelope, this paper establishes a direct simulation model of the ACE transient state. In this model, geometric parameters are used to replace the component state parameters. The corresponding relationship between geometric parameters and component state parameters is determined by sensitivity analysis. The geometric variables are controlled when the geometric adjustment speed exceeds the limit, and at the same time the corresponding component state parameters are iterated. The gradient optimization algorism is used to optimize the ground acceleration process of ACE, and the control schedule in terms of operating point of compression components and corrected acceleration rate is used as the full-envelope acceleration control schedule based on the similarity principle. The acceleration control schedules of the triple-bypass mode and the double-bypass mode are designed in this paper. The acceleration processes under various flight conditions are simulated using the acceleration control schedules. Compared with the acceleration process with the linear geometric adjustment schedule, the acceleration performance of ACE is improved by the acceleration control schedule, with the impulse of the acceleration process of the triple-bypass mode being increased by 8.7%–12.3% and the impulse of the double-bypass mode acceleration process being increased by 11.8%–14.1%.

Gain scheduled task space control of multi DOF machine tools with non-linear parallel kinematics

Machine tools with multiple degrees of freedom and parallel kinematics offer tremendous opportunities for new kind of processes but at the same time also a lot of control challenges. They are subject to uncertainties in the form of kinematic errors, elasticities and thermal deflections. Their kinematic non-linearity requires the application of model-based control concepts whose full effectiveness can only be unlocked through controls in the task space. In particular, kinematic singularities occur in forming machines such as presses, which make closed-loop control at corresponding operating points considerably more difficult. In this paper, we use the example of a servo-driven press with three ram degrees of freedom to show how parallel kinematic machine tools can be controlled via inverse differential kinematics models in task space and to what extent the controls can benefit from scheduled gain factors. The results are validated both simulatively and experimentally. In the experimental tests, three-dimensional tool paths are traced that can be used to carry out orbital forging processes. Furthermore, the example of a punch hole rolling process demonstrates that the ram pose is controlled more accurately, which also has positive effects on the resulting workpiece geometry. The performance of the developed gain scheduling task space control is compared with that of a robust and non-robust control with static gains.

IMPLEMENTING SMART AMBIENT BRIGHT LIGHT (SABL) IN NURSING HOME RESIDENTS WITH DEMENTIA: CHALLENGES AND STRATEGIES

Abstract Effective non-pharmacological interventions are critically needed to reduce agitation for nursing home residents with dementia. Lighting interventions (LIs) improve agitation among persons with dementia. However, LIs have not been widely implemented in “real-world” care settings, and a feasible implementation approach to ambient LIs has not been established. This study describes challenges and strategies in implementing the smart ambient bright light (SABL) to provide auto-controlled, consistent indoor lighting that incorporates natural daylight. This SABL includes tunable LED lights, photosensors, and controllers and has a pre-programmed 24-hour control schedule for illuminance. The SABL will be installed in participants’ bedrooms, dining areas, and activity rooms for four weeks. Key challenges include nursing home leadership team’s concerns about compliance with regulations and increased staff workload. Our implementation strategies include 1) engaging the nursing home stakeholders early on to address their concerns, 2) setting up an advisory board at each facility to involve the administrator, director of maintenance, and frontline healthcare workers to ensure the implementation protocol is congruent with their regulations, policy, and staffing, 3) tailoring lighting schedule based on the care routine and resident schedules, and 4) monitoring the SABL operation on-site and remotely. Moreover, the study incorporates a mixed methods design to interview staff/administrators and evaluate the intervention’s acceptability, feasibility, and appropriateness from their perspectives. This is the first study that addresses the implementation strategies of LIs. Findings will establish evidence-based implementation strategies and the best design for SABL to reduce agitation for nursing home residents with dementia.

Integrated control strategy of semi-active suspension and in-wheel motors for handling limit of electric race car

An integrated control strategy is proposed to deal with extreme driving conditions for race cars using magnetorheological (MR) semi-active suspension and in-wheel motors. A novel controllable boundary is introduced as the vehicle state supervisor based on the phase portrait of yaw rate and side slip angle. Compared with the traditional stable region, it can better cope with the aggressive behaviors of racing drivers. The proposed integrated control algorithm consists of a controllable boundary-based hierarchy direct yaw moment controller (HDYC) and acceleration-based scheduling MR damper controller. The HDYC will generate the correct yaw moment based on the sliding mode method when vehicle states exceed the critical area of the controllable region. The MR damper controller includes vehicle attitude control (VAC) mode and maneuverability control (MC) mode. The switch of control mode is determined by vehicle states as well. According to the analysis of load transfer, the acceleration scheduling strategy based on the “g-g” ellipse is utilized to generate the desired current command. Three typical scenarios are conducted through co-simulation of Carsim and Simulink to verify the control strategy, including accelerating and hard braking test, double lane change test, and duration circuit test. Especially, the limit of actuators is taken into consideration. The simulation results show that the proposed integrated control strategy could reduce the lap time and increase the maneuverability as well as stability of race cars, compared with traditional direct yaw moment control. The ultimate acceleration of the vehicle is improved significantly.

Deadlock control and hybrid social spider scheduling algorithm for two-stage assembly permutation flowshop with limited buffers

This paper focuses on the deadlock control and scheduling problem of two-stage assembly permutation flowshop with limited buffers. In such a system, all parts are first processed in a permutation flowshop, then assembled into final products. The objective is to find a feasible schedule, including a manufacturing subschedule and an assembly subschedule, to minimize makespan. Owing to limited buffers, not only may the blocking phenomenon appear in the system operation, but also some manufacturing subschedules may lead to deadlock states, making them unavailable. To obtain a feasible schedule, this work first proposes a deadlock control or an availability detection and amendment (ADA) algorithm to determine whether a manufacturing subschedule is available, and convert unavailable ones to available ones. Then by embedding ADA into a social spider algorithm (SSA), a hybrid social spider algorithm (HSSA) is developed for computing feasible schedules. In HSSA, an improved heuristic search algorithm is proposed to generate a better initial population, and a simplified variable neighborhood search algorithm is designed and incorporated into HSSA to enhance its searching ability. Finally, HSSA is tested on instances, showing its superiority over SSA in performance. The effect of buffer size on the scheduling objective is discussed.

“Sometimes it’s Like Putting the Track in Front of the Rushing Train”: Having to Be ‘On Call’ for Work Limits the Temporal Flexibility of Crowdworkers

Research suggests that the temporal flexibility advertised to crowdworkers by crowdsourcing platforms is limited by both client-imposed constraints (e.g., strict completion times) and crowdworkers’ tooling practices (e.g., multitasking). In this paper, we explore an additional contributor to workers’ limited temporal flexibility: the design of crowdsourcing platforms, namely requiring crowdworkers to be ‘on call’ for work. We conducted two studies to investigate the impact of having to be ‘on call’ on workers’ schedule control and job control. We find that being ‘on call’ impacted: (1) participants’ ability to schedule their time and stick to planned work hours, and (2) the pace at which participants worked and took breaks. The results of the two studies suggest that the ‘on-demand’ nature of crowdsourcing platforms can limit workers’ temporal flexibility by reducing schedule control and job control. We conclude the paper by discussing the implications of the results for: (a) crowdworkers, (b) crowdsourcing platforms, and (c) the wider platform economy.

Empirical contrast model for high-contrast imaging A VLT/SPHERE case study

Context. The ability to accurately predict the contrast achieved with high-contrast imagers is important for efficient scheduling and quality control measures in modern observatories. Aims. We aim to consistently predict and measure the raw contrast achieved by SPHERE/IRDIS on a frame-by-frame basis in order to improve the efficiency of SPHERE at the Very Large Telescope (VLT) and maximise scientific yield. Methods. Contrast curves were calculated for over 5 yr of archival data obtained using the most common SPHERE/IRDIS corona-graphic mode in the H2/H3 dual-band filter. These data consist of approximately 80 000 individual frames, which were merged and interpolated with atmospheric data to create a large database of contrast curves with associated features. An empirical power-law model for contrast – motivated by physical considerations – was then trained and finally tested on an out-of-sample test dataset. Results. At an angular separation of 300 mas, the contrast model achieved a mean (out-of-sample) test error of 0.13 magnitude with the 5th and 95th percentiles of the residuals equal to −0.23 and 0.64 magnitude respectively. The models test-set root mean square error (RMSE) between 250 and 600 mas was between 0.31 and 0.40 magnitude, which is equivalent to that of other state-of-the-art contrast models presented in the literature. In general, the model performed best for targets of between 5 and 9 G-band magnitude, with degraded performance for targets outside this range. This model is currently being incorporated into the Paranal SCUBA software for first-level quality control and real-time scheduling support.

Developing the risk-based model of scheduled sanitary-epidemiologic control of prepared food providers

The relevance of the study is determined by a substantial change in the structure of services offered by prepared food providers and a growth in outbreaks of communicable diseases due to violations of the mandatory sanitary-epidemiological requirements by prepared food providers and nutrition units in establishments. The aim of this study was to develop the methodical support for the risk-based model of the sanitary-epidemiological control over economic entities operating as prepared food providers. The study was accomplished following the enquiry of the RF Chief Sanitary Inspector. The general principles of risk calculation and identification of risk categories of an economic activity and production facilities remaining intact, a suggestion is to take a value of a temporary exposure criterion for prepared food providers as equal to 1.0 when determining numbers of exposed population. This is justified since a time of a contact with a potential infectious agent in food is of no significance. In addition to that, the model development entails mandatory consideration of population with risks of negative exposures due to consuming food offered by takeaways. The study results indicate that consideration of takeaway food and correct consideration of people’s contacts with a potentially hazardous infectious agent lead to a substantial increase in levels of health risks. Accordingly, there is a growth in the share of economic entities, which, according to potential risks of health harm for consumers, can be assigned into the categories of ‘extremely high risk’ and / or ‘high risk’ (from 1.3 to 8.2 % and from 1.7 to 32 % accordingly in the Perm oblast and Moscow oblast as two pilot regions).The resulting structure of risk categories economic entities are assigned into is more adequate to the current sanitary-epidemiological situation in the country and provides reliable protection for consumers’ health. The prospects of the model development involve accumulation and science-intense analysis of digital branch and interdepartmental data on the branch functioning, results of control and surveillance activities, and health impairments in population associated with risk factors typical for activities performed by prepared food providers.

The effect of vehicle-to-grid integration on power grid stability: A review

This article explores the promising potential of vehicle-to-grid (V2G) technology, which has attracted considerable interest due to its capacity to enhance power grid stability, decrease carbon emissions, and boost renewable energy utilization. The study begins by examining recent developments in V2G technology, then proceeds to discuss the challenges associated with integrating V2G into the power grid, and finally presents strategies to improve grid stability. Despite the potential of V2G technology, certain challenges must be addressed, including grid stress, complexities in load management, and grid imbalances. The article outlines various strategies to improve power grid stability, such as smart energy metering, time-of-use (TOU) schemes, rolling prediction decision frameworks, and an array of control approaches. Additionally, the research highlights the application of machine learning algorithms for efficient EV scheduling and power control units, voltage stabilization systems, and energy management units to ensure grid reliability. Future research should focus on developing scalable infrastructure solutions, establishing universal standards, designing optimization algorithms, and devising strategies to enhance battery life, to facilitate efficient V2G integration.

An integrated control strategy for simultaneous robot assignment, tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network

There has been a leap in the field of smart manufacturing with the advancement of automation systems, robotic technology, big data analytics, and state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Three very important aspects of smart manufacturing systems are system productivity, product quality, and maintenance of machines and equipment. These three issues are strongly interrelated and collectively determine the performance of a smart manufacturing system. Although there has been significant studies in production control, quality control and maintenance scheduling to address each of these aspects individually, there has been a lack of sufficient studies taking all of them into consideration in one control scheme. In this paper, a mobile multi-skilled robot operated Flexible Manufacturing System (FMS) is considered and a model that integrates robots, individual workstation processes and product quality is developed using a Heterogeneous Graph Structure. Heterogeneous Graph Neural Network (HGNN) is used to aggregate local information from different nodes of the graph model to create node embeddings that represent global information. A control problem is then formulated in the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework to simultaneously consider robot assignment, product quality and maintenance scheduling. The problem is solved using Multi-Agent Reinforcement Learning (MARL). A case study is presented to demonstrate the effectiveness of the HGNN-MARL control strategy by comparing it to three baselines and the naive MARL policy without HGNN.

Two-stage robust scheduling and real-time load control of community microgrid with multiple uncertainties

With the increasing popularity of wind and solar generation, the stable and economic operation of the community microgrid system become more challenging. This paper is concerned with the robust scheduling and real-time control of demand response (DR) air conditioning (AC) loads for community microgrid. Firstly, a two-stage robust scheduling model coupled by the day-ahead dispatch and real-time dispatch is established by considering the uncertainties of wind/photovoltaic power generation and loads. Moreover, the incorporation of multiple interval uncertain sets is introduced to characterize the uncertainties related to wind power, photovoltaics, and load, thereby reducing the conservativeness of the model. Secondly, the real-time control of AC loads by adjusting the on/off actions and temperature setpoints with limited impact on user comfort so as to track the optimized electricity consumption curve. Finally, the simulation results show the effectiveness and robustness of the proposed optimization dispatch and real-time control algorithm.

Optimal battery scheduling in solar-plus-storage grid-connected microgrid for profit and cost efficiency: A use case on an Israeli microgrid

This paper presents an optimal energy management algorithm for solar-plus-storage grid-connected microgrid simulated on a real full-scale small town microgrid test-case, taking into account the daily solar energy generation as well as the electricity demand to ensure that the battery is charged and discharged at the optimal times to balance energy supply and demand. The optimization is performed using dynamic programming and finding the minimum-cost-path, to minimize the total cash-flow. The formulation is utilized with two variations such that two business models are considered, optimizing cost efficiency in both aspects of minimizing cost and maximizing profits, according to the scenario. The first model maximizes self-consumption, and the second model is the case of retail electricity providers. The results are compared to real data from a solar-plus-storage grid-connected microgrid located in Israel, that is currently managed by a rule-based scheduling procedure. The results show that by using the optimal control schedule, the revenue increases up to 8% during the spring and autumn when the weather conditions are typically moderate. In summer and winter, where the climate conditions are more extreme and the demand for energy is at its peak, the full revenue potential can be realized by using the suggested optimal control algorithm, offering an increased profit of up to 30%. Unlike prior studies which are restricted to small use-cases, this paper leverages a unique and private large-scale data-set from a small town, enabling a thorough exploration of the optimization’s real-world viability and scalability. Through rigorous simulations, the findings underscore the method’s exceptional potential in effectively integrating PV-plus-storage assets and optimizing energy use in power grids.

Temperature-Controlled Laser Cutting of an Electrical Steel Sheet Using a Novel Fuzzy Logic Controller

A novel PID-type fuzzy logic controller (FLC) with an online fuzzy tuner was created to maintain stable in situ control of the cutting front temperature, aiming to enhance the laser process for thin non-oriented electrical steel sheets. In the developed controller, the output scaling factors and the universe of discourse were initially optimized using a hybrid of the particle swarm optimization and grey wolf optimization methods. The optimal parameters obtained were utilized in experiments involving the laser cutting of thin non-oriented electrical steel sheets, compared to an open-loop control system maintaining a constant cutting speed. The PID-type FLC with an online fuzzy tuner demonstrated a superior cutting quality, generating a smaller roundness and a reduced heat-affected zone (HAZ) through the in situ tuning of control parameters. Particularly, the HAZ width was significantly smaller than that reported in a previous study which used fuzzy gain scheduling for temperature control. Moreover, the cutting time was diminished by optimally adjusting the cutting speed using PID-type FLC with an online fuzzy tuner. Therefore, the accumulated heat in the steel sheet, particularly under high laser pulse frequencies, was effectively reduced, making it suitable for industrial applications.

A Multi-Objective Optimization Approach for Elevator Group Control Systems Based on Particle Swarm Algorithm

Currently, elevator group control systems containing multiple elevators are used in high-rise buildings, where elevator groups are centrally dispatched according to a set dispatching plan to provide vertical transportation services for the passengers. However, if these elevators work independently, once passengers send out a call signal, multiple elevators will respond to the same request at the same time, and the elevator group will repeatedly go back and forth, which seriously affects the system operation efficiency and also increases energy consumption, so it is a key issue to solve the dispatching problem in the elevator group control system. Elevator group control scheduling is an NP-hard problem with explosive combination characteristics. In this paper, a multi-objective model is formulated based on the criteria of average passenger travel time, average waiting time and system energy consumption for the scheduling problem. Then the particle swarm optimization algorithm is proposed to solve the scheduling problem. Finally, the performance of the proposed algorithm is compared with the genetic algorithm-based elevator group control scheduling, the simulation results show that the proposed algorithm with fast convergence while the waiting time of passengers is significantly decreased.

Fuzzy Gain Scheduling of the Fractional-Order PID Controller for a Continuous Stirred-Tank Reactor Process

In most chemical industries, continuous stirred-tank reactors (CSTRs) are essential for the production of chemicals. The CSTR process is highly nonlinear and has both stable and unstable equilibrium points. Control system engineers face a difficult issue when designing a single controller for temperature control in both stable and unstable areas. Due to these problems, implementing the conventional proportional-integral-derivative (PID) controller may lead to instability in controlled variables. Therefore, in this paper, the entire operating region is divided into three regions. The fractional-order PID controller as a local controller is designed in each operating region to control the temperature, and its parameters are tuned with a genetic algorithm by minimizing the integral of absolute error and control input change with a weighting factor. Then, a fuzzy gain scheduling scheme based on the Tagaki-Sugno fuzzy model is used to properly interpolate the outputs of the local controllers. A set of simulations are carried out to demonstrate the effectiveness of the proposed controller, and its performance is compared with that of an adaptive nonlinear controller and fuzzy gain scheduling of the PID controller.

Towards a blockchain and machine learning-based framework for decentralised energy management

In most domestic buildings, gas and electricity are supplied by energy and utility companies through centralised energy systems. This often results in a high burden on central management systems and has adverse effects on energy prices. Blockchain-based peer-to-peer energy trading platforms can deliver strategic operation of decentralised multi-energy network among multiple domestic buildings to reduce global greenhouse gas emissions and address global climate change issues. However, prevailing blockchain-based energy trading platforms focused on system implementation for peer-to-peer electricity trading while lacking predictive control and energy scheduling optimisation. Therefore, this paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy (i.e., heat and electricity) allocation and transmission, among multiple domestic buildings. Machine learning is harnessed to predict day-ahead energy generation and consumption patterns of prosumers and consumers within the multi-energy network. The proposed blockchain and machine learning-based decentralised energy management framework will establish optimal and automated energy allocation among multiple energy users through peer-to-peer energy transactions. This approach focuses on energy-matching from both the supply and demand sides while encouraging direct energy trading between prosumers and consumers. The security and fairness of energy trading can also be enhanced by using smart contracts to strictly execute the energy trading and bill payment rules. A case study of 4 real-life domestic buildings is introduced to determine the economic and technical potential of the proposed framework. In comparison to prevailing approaches, a key benefit from the proposed approach is an improved computational load/failure of a single point, energy trading strategy, workload, and capital cost energy. Findings suggest that energy costs reduced between 7.60% and 25.41% for prosumer buildings and a fall of 5.40%-17.63% for consumer buildings. In practical applications, the proposed approach can involve a larger number of prosumer and consumer buildings within the community to decentralise multiple energy trading, thus significantly contributing to the reduction of greenhouse gas emissions and enhancing environmental sustainability.