Time-based Control Research Articles

Stabilization of Load Frequency Control System via Event-Triggered Intermittent Control

This brief studies the stabilization of load frequency control (LFC) system. The event-triggered intermittent control (ETIC) is designed for the multi-area LFC system. The stabilization of the LFC system is achieved under the designed ETIC. Moreover, the scheme of ETIC is improved via state-based control width (SCW) and an ETIC-SCW is proposed. Two performance indices: the rate of control and the cost of control, are defined for comparisons. Specific cases are studied to verify the effectiveness of the proposed ETIC schemes. From the numerical simulations, it is shown that the designed ETIC, especially ETIC-SCW, achieves clearly lower rate of control and cost of control than the time-based intermittent control and the event-triggered control for LFC system in the literature.

Finite-time Consensus of Multi-agent Systems via Time-based Event-triggered Control

Finite-time Consensus of Multi-agent Systems via Time-based Event-triggered Control

Control and consent regime dynamics within labour platforms

Much is known about how labour platforms use ‘algorithmic management’ to implement rules which govern labour by matching workers (or service providers) with clients (or users). But little is known about whether and how platform workers engage with these rules by manipulating them to their own advantage, and how this accounts for wider ‘regime dynamics’ across (and within) different types of platforms (for example, on-location and online). Based on a comparative analysis of two food delivery (Deliveroo and Takeaway) and two freelancing (Upwork and Jellow) platforms in Belgium, we discuss the rules platforms use to govern labour and examine what role workers have in shaping a ‘space’ of control over the conduct of their work. Drawing on labour process theory, we argue that this space is shaped by the way in which platforms shift risks onto workers by rules governing access to work through rewards, penalties as well as labour deployment reflecting various contractual statuses. Hence, we explain how workers also shape such spaces by organising consent around these rules, pointing to a ‘social space’ for food delivery workers and a ‘market space’ for self-employed freelancers. These spaces refer to different regime types, that is, ‘pay-based control’ and ‘time-based control’ for food delivery, and ‘customer-based control’ and ‘task-based control’ for online freelancers. These types are shaped by the control and consent dynamics within labour platforms, reflecting the platforms’ labour governance strategies and workers’ attempts to ensure control over these strategies within the distinctive political institutional realm.

Stabilization via Event-Triggered Impulsive Control With Constraints for Switched Stochastic Systems.

This article studies the event-triggered impulsive control (ETIC) with constraints for the stabilization of switched stochastic systems (SSSs). An ETIC scheme with constraints is proposed for SSS by designing two levels of events via three indices: 1) a threshold value; 2) a control-free index; and 3) a check period. It is also constrained via a constraint index. Based on the activation probabilities and transition probabilities of subsystems, the stabilizations in terms of the p th moment exponential stability and almost exponential stability are achieved, respectively, by the ETIC with constraints. Moreover, based on the scheme of ETIC with constraints, sampling-based ETIC and random ETIC are proposed, respectively. The stabilization conditions via sampling-based ETIC and random ETIC are also derived. It is shown that the ETIC with constraints is non-Zeno and robust with respect to time delays and can achieve lower impulse frequency than the classic time-based impulsive control and recent ETIC schemes. Finally, two examples are presented to demonstrate the effectiveness of the ETIC with constraints.

OC-002 EVALUATION OF GIANT INGUINAL HERNIA REPAIR RESULTS - A RETROSPECTIVE SINGLE INSTITUTION CASE-CONTROL PROPENSITY SCORE MATCHED STUDY

Abstract Background Giant inguinal hernia (GIH), defined as a hernia descending below the inner-thigh midpoint in upright position, is rare. Case reports describe techniques in which the abdominal cavity is prepared and care is taken to prevent abdominal compartment syndrome (ACS). Our aim was to analyze and review our experience with GIH repair. Methods Retrospective single-institution, case-control, study included adults who underwent GIH repair between 2014–2021 at an university referral center. Technique included endo-tracheal intubation, head-down positioning, maximal pre-incision reduction of hernia contents, and repair with mesh. Follow-up was conducted in outpatient clinic. Results were compared to a time-based control group. Results During the study period, 58 patients underwent an open GIH repair with mesh without abdominal preparation. Control group included 232 patients who underwent open non-giant inguinal hernia repair (control group). Bowel resection was not necessary in any case. The in-hospital and 30-day complication rates were 17.2% and 68.9% in the GIH group, and 8.2% and 27.2%, in the control group (p=0.04, OR=2.3; and p<0.001, OR=5.9, respectively). Ischemic orchitis was common in the GIH group (65.5%) compared to the control group (13.4%) (p<0.001, OR=12.3). ACS, major complications and mortality were not reported. 90-day complication rates were similar. 2 cases of recurrence (0.9%) were observed in the control group. Conclusions Our study suggests that GIH repair is safe and feasible, and there is no need for abdominal cavity preparation. Early complications, mainly ischemic orchitis, are more common than control group, but without long-term implications and with a very low recurrence rate.

Improved nitrogen removal efficiency by implementation of intermittent aeration.

The aim of the paper was to verify the possibility of using intermittent aeration to decrease outflow total nitrogen concentrations in municipal WWTPs of different capacities (15,780, 23,000 and 806,250 PE). A simple time-based intermittent aeration control was used in the experimental work. The results of WWTPs are compared with each other and with literature sources. The use of intermittent aeration led to a significant decrease of outflow total nitrogen concentration by up to 57% without the need of additional investment costs or the increase of operating costs. Nitrification kinetic tests and fluorescence in-situ hybridization observations proved that intermittent aeration has no negative impact on nitrifiers. The results proved that it is possible to utilize intermittent aeration for treatment plants initially designed with permanent aeration of nitrification sections if the current load is lower than the designed capacity. A necessary prerequisite for successful implementation of intermittent aeration is sufficient blower power. Although the time-based control is very simple and inexpensive to implement, the results are comparable to more complex systems such as systems based on rule-based systems for N-NH4+ and N-NO3- concentrations, on fuzzy logic or on mathematical models.

Efficient Monitoring and Adaptive Control of Indoor Air Quality Based on IoT Technology and Fuzzy Inference

In recent years, more and more occupants have suffered from respiratory illness due to poor indoor air quality (IAQ). In order to address this issue, this paper presents a method to achieve efficient monitoring and adaptive control of IAQ. Firstly, an indoor air quality monitoring and control system (IAQMCS) is developed using IoT technology. Then, based on fuzzy inference, a novel fuzzy air quality index (FAQI) model is proposed to effectively assess IAQ. Furthermore, a simple adaptive control mechanism, called SACM, is designed to automatically control the IAQMCS according to a real-time FAQI value. Finally, extensive experiments are performed by comparing with regular control (time-based control), which show that our proposed method effectively measures various air parameters (CO2, VOC, HCHO, PM2.5, PM10, etc.) and has good performance in terms of evaluation accuracy, average FAQI value, and overall IAQ.

Investigating Temporal Memory Strength and Time-Based Impulse Control Using the DRL Task

Abstract Differential reinforcement of low rate (DRL) responding is a schedule-controlled behavior sometimes used in timing research, but also received critics of not providing a pure measure of timing due to the influence of the subject’s motivation or inhibitory control. Nevertheless, we argue that the DRL task provides a unique approach to study how timing and emotion interact with each other. Here, we review evidence showing that male rats prenatally treated with choline supplementation had difficulty in acquiring longer criterion times in the DRL task. This was possibly due to the stronger memory strength of their previously learned shorter criterion times. Female rats, in contrast, performed better than male rats in the same task, but those receiving prenatal choline supplementation were the best performers in this task with longer criterion times because they required less training. Like all female rats, male rats treated with prenatal choline supplementation made very few burst responses, suggesting that the treatment improved male rats’ emotional regulation when facing ‘frustrating’ outcomes. The differential impulse control plus different memory strength of the rats trained in the DRL task revealed the potential interaction of sex hormones and prenatal choline supplementation, a rare combination in a single animal study on timing and time perception. In summary, although the DRL task is certainly not the best timing task, it may be useful in assisting us in better understanding how time perception participates in emotional regulation, especially relevant when the emotion is triggered by a failure in timing.

A Distributed Multi-Agent Reinforcement Learning With Graph Decomposition Approach for Large-Scale Adaptive Traffic Signal Control

With the emerging connected-vehicle technologies and smart roadways, the need for intelligent adaptive traffic signal controls (ATSC) is more than ever before. This paper first proposes an Accumulated Exponentially Weighted Waiting Time-based Adaptive Traffic Signal Control (AEWWT-ATSC) model to calculate priorities of roadways for signal scheduling. As the size of the traffic network grows, it adds great complexities and challenges to computational efficiencies. Considering this, we propose a novel Distributed Multi-agent Reinforcement Learning (DMARL) with a graph decomposition approach for large-scale ATSC problems. The decomposition clusters intersections by the level of connectivity (LoC), defined by the average residual capacities (ARC) between connected intersections, enabling us to train subgraphs instead of the entire network in a synchronized way. The problem is formulated as a Markov Decision Process (MDP), and the Double Dueling Deep Q Network with Prioritized Experience Replay is utilized to solve it. Under the optimal policy, the agents can select the optimal signal durations to minimize the waiting time and queue size. In evaluation, we show the superiority of the AEWWT-ATSC based RL methods in different densities and demonstrate the DMARL with a graph decomposition approach on a large graph in Manhattan, NYC. The approach is generic and can be extended to various types of use cases.

Trajectory Generation of Balloon Robot Using Time-based State Control Form

Trajectory Generation of Balloon Robot Using Time-based State Control Form

Deep learning-based image recognition method for on-demand defrosting control to save energy in commercial energy systems

Periodical defrosting is essential to restore the initial capability of heat exchangers and improve the operating efficiency of commercial energy systems. The defrosting control method applying image recognition technology is considered a low-cost and easy-operating approach to implement demand-based defrosting cycles. However, with regard to different operation environments, the establishment of high-accuracy image recognition model will consume plenty of labor and cost, which leads to the low extensibility between homotypic or heterotypic devices and severely limits its practical application in commercial energy systems. To this end, a novel deep learning-based image recognition method was presented for the extensible implementation of on-demand defrosting control. In order to improve the recognition accuracy, a convolutional neural network (CNN) model was proposed to extract in-depth and complicated features for frosty state detection. By integrating deep clustering and image augmentation, the time-consuming experiments and labor-intensive labeling workload were greatly reduced. The recognition accuracy of proposed CNN model was on average 5.50% higher than that of conventional CNN model, and the recognition accuracy was further increased to 97.57% through the hyperparameters optimization. Based on the trained CNN model, a defrosting control method was proposed for the on-demand defrosting control according to the real-time frosty state recognition. Compared with original time-based control method (defrosting per device after a fixed interval), the field experiment testified that the defrosting frequency, accumulated time and energy consumption were decreased by 31.68%, 65.83% and 42.92% respectively by adopting the proposed control method. The economic and environmental analysis indicated that the payback time was approximately half a year and annual reduction in CO2 emission is 28.44 t, which signified the great application potential of the proposed method. This study will shed light on the further application of image recognition technology in defrosting control and promote net-zero emissions in commercial energy systems.

Investigation of time-based pressure control for microfluidics chip design

The emergence of the microfluidic chip was a game-changer in microbiological analysis platforms. This technology, by combining physics, chemistry, biology, and computing, helps researchers to obtain precise results in a shorter time. However, it requires more advancements in order to lessen its limitations. This study presents the design, modelling, and microbiological analysis of a microelectromechanical system (MEMS) based microfluidic chip. Three different microfluidic chips have been developed during the design process. These chips have different inlet channels and one outlet channel. The modelling process was carried out with Multiphysics Software. Pressure and velocity data in micron-sized channels were checked for each system. The flow directions of the fluids in the inlet and outlet channels were observed according to the pressure change. As a result of the analysis, the highest velocity was found in the microfluidic chip with three inlet channels. In comparison, the highest pressure was measured in the microfluidic chip with four inlet channels. These values are 2.36 x10-17 m/s and 13.5 Pa, respectively. The pressure values of the 4 and 5-channel microfluidic chips were very close. The results showed that as the number of inlet channels increased, the pressure value in the microfluidic chip increased, but the velocity value decreased.

Energy efficiency of Heating, Ventilation and Air Conditioning systems in production environments through model-predictive control schemes: The case of battery production

In the German industry Heating, Ventilation and Air Conditioning (HVAC) systems account for 11 %–20 % of the final energy consumption. Usually HVAC systems are dimensioned based on static extreme values and thus during most of the operation time, HVAC systems tend to be overdimensioned. In addition, the extensive literature review of 40 papers shows that often topics on model-based energy efficiency improvement are commonly performed for office or residential buildings, but rather rarely for production environments. Furthermore, the performance of control schemes for energy efficiency improvement are rarely compared with each other or incompletely compared with each other. In this publication, we use the framework of a Cyber–Physical Production System (CPPS) to compare the performance of four main control schemes, ranging from simple time-based control to model-predictive control, which has not been done in the context of battery production before. Over a fixed observation period, the four control schemes are transparently compared with each other in a case study using the example of the Battery LabFactory Braunschweig (BLB). Compared to the initial state, a significant reduction in final energy demand can be achieved with all examined control approaches, which is even up to 37.29% in case of the model-predictive control. Surprisingly, even simple control approaches, without any predictions, have a good energetic saving potential of up to 20.22% compared to the initial state, which is why they are initially recommended for practical implementation. Moreover, we publish all source code of the control schemes, in order to encourage the implementation of energy efficiency measures in further applications not restricted to battery production.

Real-time optimal control of HVAC systems: Model accuracy and optimization reward

Real-time optimal control is considered as an efficient tool to improve the energy efficiency of heating, ventilation, and air-conditioning (HVAC) systems. It minimizes the energy consumption of HVAC systems by searching the optimal settings (normally set-points) for local control loops. Generally, in a model-based real-time optimal control, a reliable and accurate model is important for optimization performance but not easy to be obtained in practice. Thus, model errors exist universally and cannot be avoided in the application. The model error may have a negative impact on the performance of real-time optimal control. For example, in power minimization, some individual optimization actions may lead to power use increase (negative reward) instead of power use decrease (positive reward). This paper analyzes the impact of model accuracy on individual optimization actions. Using numerical analysis, different sizes of model accuracy were investigated and the possibility of positive/negative reward was quantified in percentage. This paper also revealed that event-based optimal control with controlled thresholds could significantly reduce the percentage of negative reward when compared with a time-based optimal control (e.g. optimization was carried out every 30 min).

Enabling efficient traceable and revocable time-based data sharing in smart city

With the assistance of emerging techniques, such as cloud computing, fog computing and Internet of Things (IoT), smart city is developing rapidly into a novel and well-accepted service pattern these days. The trend also facilitates numerous relevant applications, e.g., smart health care, smart office, smart campus, etc., and drives the urgent demand for data sharing. However, this brings many concerns on data security as there is more private and sensitive information contained in the data of smart city applications. It may incur disastrous consequences if the shared data are illegally accessed, which necessitates an efficient data access control scheme for data sharing in smart city applications with resource-poor user terminals. To this end, we proposes an efficient traceable and revocable time-based CP-ABE (TR-TABE) scheme which can achieve time-based and fine-grained data access control over large attribute universe for data sharing in large-scale smart city applications. To trace and punish the malicious users that intentionally leak their keys to pursue illicit profits, we design an efficient user tracing and revocation mechanism with forward and backward security. For efficiency improvement, we integrate outsourced decryption and verify the correctness of its result. The proposed scheme is proved secure with formal security proof and is demonstrated to be practical for data sharing in smart city applications with extensive performance evaluation.

Comparison of Reaction Time-based Collaborative Velocity Control and Intelligent Driver Model for Agent-based Simulation of Autonomous Car

Based on historical records, driving in hazardous weather conditions is one of the most serious causes that lead to fatal accidents on roads in general and in United Arab Emirates (UAE) highways in particular. One solution to improve road safety is to equip vehicles and infrastructure with connected and smart devices and convert them into autonomous vehicles. Before deploying a concrete solution to the field, it must be validated by simulation, and more specifically by agent-based simulation. In this paper, we propose to implement the Reaction Time-Based Collaborative Velocity Control (RT-CVC) model that was implemented in autonomous cars into an agent-based simulator. This model is compared to the Intelligent Driver Model (IDM), which is one of the standard longitudinal driving behaviors in simulation environments. The experimental results show that RT-CVC generates traffic flows with fewer vehicle collisions and shorter travel times. This positive analysis is balanced by the fact that RT-CVC is designed for autonomous cars, and IDM is designed to model human-drive decisions. Using RT-CVC for modeling a human driver may be counter productive in simulation experiments.

Regulation response of pilot-operated diaphragm type pressure-reducing valves: laboratory testing and impact on the performance of pressure control modes in water distribution systems

Pressure control is recognized as an efficient measure for reducing leaks from water distribution systems. We have evaluated the effectiveness of various pressure control modes by means of pilot-operated diaphragm type pressure-reducing valves (PRVs), taking into account the sensitivity of the valves to various settings. First, the response of a PRV to consecutive pressure settings variations was experimentally evaluated in the hydraulic simulation laboratory of National Institute for Scientific Research. These experiments revealed that the studied valve only reacts when variation in the pressure setting corresponds to at least a 1/6 turn of the pilot valve. Second, an actual case study from Quebec City, Canada, was simulated to evaluate the impact of the PRV response on three pressure control modes: fixed control, time-based control, and real-time control (RTC). The results show that RTC of pressure leads to reductions in leakage rate for the studied network but that the PRV operational constraints limit the expected performance of RTC.

Modelling the startup of machine tools for energy efficient multi-sleep control policies

Energy efficient control strategies have been of recent interest in literature as promising measures to reduce the energy consumed by production equipment during interruptions of part flow. A general time-based control policy is analyzed. Machine idle times are assumed stochastic and the expected value of the energy consumed per produced part is reduced while assuring a certain target of expected production rate. The startup time required to resume the service depends on the time period the machine stays in a low power consumption state. Since the startup is often related to one or more machine components, the optimal policy might be to partially switch the machine. Therefore, a policy acting at component level is also analyzed where the duration of the startup depends on the set of machine components that are switched off. The policy is compared to state-of-the-art policies and discussed for a set of simulated numerical cases representing several production environments.

A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating

Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented in this paper outlines the development of a prediction model that controls electricity consumption in a home, adapting to external environmental conditions and occupation. A backup geyser element in a solar geyser solution is identified as a metric for more efficient control than a time-based controller. The system is able to record multiple remote sensor readings from Internet of Things devices, built and based on an ESP8266 microcontroller, to a central SQL database that includes the hot water usage and heating patterns. Official weather predictions replace physical sensors, to provide the data for the environmental conditions. Fuzzification categorises the warm water usage from the multiple sensor recordings into four linguistic terms (None, Low, Medium and High). Partitioning clustering determines the relationship patterns between weather predictions and solar heating efficiency. Next, a hidden Markov model predicts solar heating efficiency, with the Viterbi algorithm calculating the geyser heating predictions, and the Baum–Welch algorithm for training the system. Warm water usage and solar heating efficiency predictions are used to calculate the optimal time periods to heat the water through electrical energy. Simulations with historical data are used for the evaluation and validation of the approach, by comparing the algorithm efficiency against time-based heating. In a simulation, the intelligent controller is 19.9% more efficient than a time-based controller, with higher warm water temperatures during the day. Furthermore, it is demonstrated that a controller, with knowledge of external conditions, can be switched on 728 times less than a time-based controller.

Ideal Time-Based Voltage Control using Evolutionary Algorithm in Distributed Generator Centered Networks

The distributed generators (DGs) consists of radial rural distribution networks that makes use of off-voltage tap changing transformers. Ideal tap changer positions for these transformers can be determined using the novel estimation technique proposed in this paper. A branchy low-voltage network is brought down to its equivalent line along with the utilization of spatial network decomposition in this technique. Evolutionary algorithm is used for determining the PV nodes ideal voltage module values in ideal seasonal control plan. A PQ node with 3 DGs incorporated in a radial 40-node network and a PQ node with 10 DGs are incorporated in a radial 33-node network are the distribution networks used for modelling the proposed system.