Desired Set Point Research Articles

Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation

This study proposes a simulation-based methodology for estimating the energy saving achievable through the implementation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump). In this methodology, the operation of the heating supply unit each day is initiated at a different time, aiming at achieving the desired setpoint upon (and not before) the expected arrival of the occupants. It requires the estimation of the ramp-up duration (the time it takes the heating system to bring the indoor temperature to the desired setpoint), which can be provided by machine learning-based models. To justify the corresponding required deployment investment, an accurate estimation of the resulting achievable energy saving is needed. Accordingly, physics-based energy behavior simulations are first performed. Next, various ML algorithms are employed to estimate the ramp-up duration using the simulated time-series data of indoor temperature, setpoints, and weather conditions. It is shown that the proposed pipelines can estimate the ramp-up duration with a mean absolute error of about 3 min in all indoor spaces. To assess the resulting potential energy saving, a re-simulation is conducted using ML-based ramp-up estimations for each day, resulting in an energy savings of approximately 10%.

Optimizing Production Stability while Safeguarding Information

The goal of Finite Time Stabilization is to finish a particular thing in a fixed time. Regardless of the system's original state, finite-time stabilisation refers to the regulation of a system so that it reaches a desired equilibrium or setpoint in a finite length of time. Finite-time stabilisation is essential for providing quick and effective control over a variety of variables in industrial processes, such as temperature, pressure, flow rate, or composition. By layering materials based on a computer model, 3D printing, sometimes referred to as additive manufacturing, creates three-dimensional items. Even though 3D printing technology has advanced significantly in recent years, manufacturing them still presents a number of difficulties. Some of the typical difficulties include: Cost: Due to the intricate parts and high level of precision needed when manufacturing 3D printers, the cost might be high. Quality parts, such as motors, electronics, and extruders, can be expensive to source. As a result, manufacturers may find it difficult to strike a balance between price and performance. So, as part of our process, we analyse the data and forecast the pricing to make things simple for the client. To make this prediction, we employed logistic regression. More than that those client data is secured through fernet algorithm.

Prevention and characterization of thin film defects induced by contaminant aggregates in initiated chemical vapor deposition.

As initiated Chemical Vapor Deposition (iCVD) finds increasing application in precision industries like electronics and optics, defect prevention will become critical. While studies of non-ideal morphology exist in the iCVD literature, no studies investigate the role of defects. To address this knowledge gap, we show that the buildup of short-chain polymers or oligomers during normal operation of an iCVD reactor can lead to defects that compromise film integrity. We used atomic force microscopy to show that oligomer aggregates selectively prevented film growth, causing these hole-like defects. X-ray diffraction and optical microscopy demonstrated the crystallinity of the aggregates, pointing to a flat-on lamellar or mono-lamellar structure. To understand the origin of the aggregates, spectroscopic ellipsometry showed that samples exposed to the reactor consistently accrued low-volatility contaminants. X-ray photoelectron spectroscopy revealed material derived from polymerization in the contamination, while scanning electron microscopy showed the presence of defect-causing aggregates. We directly linked oligomeric/polymeric contamination with defect formation by showing an increased defect rate when a contaminant polymer was heated alongside the sample. Most importantly, we showed that starting a deposition at a high sample temperature (e.g., 50 °C) before reducing it to the desired setpoint (e.g., 9 °C) unilaterally prevented defects, providing a simple method to prevent defects with minimal impact on operations.

Artificial intelligence powered glucose monitoring and controlling system: Pumping module.

Diabetes, a globally escalating health concern, necessitates innovative solutions for efficient detection and management. Blood glucose control is an essential aspect of managing diabetes and finding the most effective ways to control it. The latest findings suggest that a basal insulin administration rate and a single, high-concentration injection before a meal may not be sufficient to maintain healthy blood glucose levels. While the basal insulin rate treatment can stabilize blood glucose levels over the long term, it may not be enough to bring the levels below the post-meal limit after 60 min. The short-term impacts of meals can be greatly reduced by high-concentration injections, which can help stabilize blood glucose levels. Unfortunately, they cannot provide long-term stability to satisfy the post-meal or pre-meal restrictions. However, proportional-integral-derivative (PID) control with basal dose maintains the blood glucose levels within the range for a longer period. To develop a closed-loop electronic system to pump required insulin into the patient's body automatically in synchronization with glucose sensor readings. The proposed system integrates a glucose sensor, decision unit, and pumping module to specifically address the pumping of insulin and enhance system effectiveness. Serving as the intelligence hub, the decision unit analyzes data from the glucose sensor to determine the optimal insulin dosage, guided by a pre-existing glucose and insulin level table. The artificial intelligence detection block processes this information, providing decision instructions to the pumping module. Equipped with communication antennas, the glucose sensor and micropump operate in a feedback loop, creating a closed-loop system that eliminates the need for manual intervention. The incorporation of a PID controller to assess and regulate blood glucose and insulin levels in individuals with diabetes introduces a sophisticated and dynamic element to diabetes management. The simulation not only allows visualization of how the body responds to different inputs but also offers a valuable tool for predicting and testing the effects of various interventions over time. The PID controller's role in adjusting insulin dosage based on the discrepancy between desired setpoints and actual measurements showcases a proactive strategy for maintaining blood glucose levels within a healthy range. This dynamic feedback loop not only delays the onset of steady-state conditions but also effectively counteracts post-meal spikes in blood glucose. The WiFi-controlled voltage controller and the PID controller simulation collectively underscore the ongoing efforts to enhance efficiency, safety, and personalized care within the realm of diabetes management. These technological advancements not only contribute to the optimization of insulin delivery systems but also have the potential to reshape our understanding of glucose and insulin dynamics, fostering a new era of precision medicine in the treatment of diabetes.

Barrier-Function-Based Adaptive Fast-Terminal Sliding-Mode Control for a PMSM Speed-Regulation System

Barrier-function-based adaptive fast-terminal sliding-mode control approaches have been devised to enhance the precision of speed regulation of permanent magnet synchronous motors (PMSMs). Firstly, the speed loop utilizes fast-terminal sliding-mode control, which contributes to a faster convergence rate and enhances the robustness of the system. By adopting this control technique, the system can quickly reach the desired speed setpoint and effectively handle disturbances. Secondly, an adaptive law based on the barrier function is employed to adjust the control gain adaptively. The proposed adaptive law considers the magnitude of the disturbance and effectively mitigates chattering resulting from excessive switching gain. Unlike conventional control methods, the design of the adaptive fast-terminal sliding-mode control does not require attaining the upper limit of the lumped disturbances. Experimental results are presented to validate the proposed approach. These results demonstrate that the proposed method outperforms the conventional terminal sliding mode control technique in terms of handling both external and internal disturbances.

Can the Brain's Thermostatic Mechanism Generate Sleep-Wake and NREM-REM Sleep Cycles? A Nested Doll Model of Sleep-Regulating Processes.

Evidence is gradually accumulating in support of the hypothesis that a process of thermostatic brain cooling and warming underlies sleep cycles, i.e., the alternations between non-rapid-eye-movement and rapid-eye-movement sleep throughout the sleep phase of the sleep-wake cycle. A mathematical thermostat model predicts an exponential shape of fluctuations in temperature above and below the desired temperature setpoint. If the thermostatic process underlies sleep cycles, can this model explain the mechanisms governing the sleep cyclicities in humans? The proposed nested doll model incorporates Process s generating sleep cycles into Process S generating sleep-wake cycles of the two-process model of sleep-wake regulation. Process s produces ultradian fluctuations around the setpoint, while Process S turns this setpoint up and down in accord with the durations of the preceding wake phase and the following sleep phase of the sleep-wake cycle, respectively. Predictions of the model were obtained in an in silico study and confirmed by simulations of oscillations of spectral electroencephalographic indexes of sleep regulation obtained from night sleep and multiple napping attempts. Only simple-inverse exponential and exponential-functions from the thermostatic model were used for predictions and simulations of rather complex and varying shapes of sleep cycles during an all-night sleep episode. To further test the proposed model, experiments on mammal species with monophasic sleep are required. If supported, this model can provide a valuable framework for understanding the involvement of sleep-wake regulatory processes in the mechanism of thermostatic brain cooling/warming.

Novel twofold control for delayed industrial processes with integrating and inverse response characteristics

Non-self- regulating nature of integrating plant causes their regulation a great challenge to researchers. Presence of time delay and positive zero in such processes makes their control more demanding. A novel control technique is developed for such processes to cope with the difficult situations and achieve enhanced performance and robustness. Suggested method is twofold: first the Inner loop controller’s parameters are adjusted to stabilize the process using maximum sensitivity concept and then the outer loop control block is designed by specifying desired setpoint tracking response in the frame of direct synthesis approach involving single tunable parameter. The tunable parameter’s values are obtained so that the robust stability condition is satisfied along with the justification of robust performance constraint in the presence of unstructured uncertainties. The effectiveness of the proposed method is verified on different plants and its advantages over some existing methods are also highlighted. Suggested approach yields improved performance with less control effort and enhanced robustness.

Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints.

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.

Design of Automation and Monitoring Systems for Filling Mineral Water Tanks Based on Android Applications

The PURE “ARH” refillable drinking water filling station located on Arief Rahman Hakim Street, Klampis Ngasem, is still not very practical and efficient. This is because the machine is still manual, operated by pressing a manual button to switch it on and off, and requires visual observation until the water container is filled. As a result, this creates uncertainty in the volume of water being filled. Therefore, in this research, an automated prototype and monitoring system for filling mineral water tanks based on an Android application were developed. The use of an HCSR04 ultrasonic sensor for mineral water tank detection at approximately 20 cm and an FS400A water flow sensor to determine the volume of water filled is employed. Subsequently, the results of mineral water tank’s water filling can be monitored, displaying the number of fillings and the volume of water filled, which are then sent to the Android application. Three mineral water tank sizes are used as samples: 5 liters, 15 liters, and 19 liters. The filling process allows configuration of the filling volume through the Android application, allowing refillable drinking water station owners to set their desired set points based on the mineral water tank’s capacity. The validation results of the FS400A G1 flow sensor shows an average error of 1.35%, resulting in an accuracy level of 98.65%, while the HC-SR04 ultrasonic sensor has an average error of 1.31% with an accuracy level of 98.69%. As for the testing of the filling mineral water tanks control system using the on-off control method, in this research, it has been successfully achieved with an error rate of 0.9% for a 5-liter set point, 1.3% for a 15-liter set point, and 2.4% for a 19-liter set point. From these results, it can be concluded that the water-filling control system operates effectively, as the actuator functions properly with a logic state of one (1), and once the set point is reached, the actuator turns off with a logic state of null (0).

Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning

Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.

STUDY THE PERFORMANCE OF TWO-WHEELED BALANCING MOBILE ROBOT USING FUZZY PD CONTROLLER

Nowadays, the research on a two-wheeled self-balancing robot is an active area of research especially in terms of design as well as control to continue the innovation applications of robots in the future. Most of the two-wheeled self-balancing robots are designed based on an inverted pendulum system for stability and maneuverability. The aim of this paper is to propose the fuzzy PD controller to control and maintain its balance on the two wheels. A sensor of the Inertial Measurement Unit (IMU) was used as an input to evaluate and obtain the position and orientation of the robot. The control algorithms for the robot also are designed to keep the pendulum upright. Then, the fuzzy PD concept was applied to correct the error between the desired set point and the actual tilt angle position to adjust the speed of the motor accordingly. The results obtained from this controller were capable of maintaining the balancing of the robot by using an experimental method of PID tuning. The prototype of the two-wheeled self-balancing robot was implemented with Arduino Uno and a fuzzy PD controller. However, the limitation of the project is the longer size and heavier weight of the robot are less stable, then a better controller is needed to balance the robot.

Thermoelectric-Based Radiant Cooling Systems: An Experimental and Numerical Investigation of Thermal Comfort

Researching novel cooling and heating technologies as alternatives to conventional vapor-compression refrigeration cycles has received growing attention in recent years. Thermoelectric (TE) systems rank among promising emerging technologies within this category. This paper presents a comprehensive investigation, utilizing numerical modeling and analysis via COMSOL Multiphysics along with experimental validation, to evaluate the performance of a radiant cooling ceiling panel working on thermoelectric principles. Performance metrics are based on thermal comfort levels within the designed test chamber. The system comprises a rectangular test chamber (~1.2 m × 1.2 m × 1.5 m) with a centrally positioned ceiling panel (dimensions: 0.6 m × 0.6 m × 0.002 m). Four TE modules are attached on top of the ceiling panel, facilitating effective cooling to regulate the ceiling temperature to the desired setpoint. The resultant lower ceiling temperature enables heat exchange within the chamber environment via radiation and convection mechanisms. This study examines the time-dependent variations in mean radiant temperature and operative temperature under natural convection conditions, with comfort level assessment carried out using the PMV method according to ASHRAE Standard 55. An experimental chamber is built to validate the numerical model by performing experiments at various ceiling temperatures. Design challenges are discussed in detail. The results of this investigation offer valuable insights into the anticipated thermal comfort achievable through TE-based radiant cooling systems across various operating conditions.

Machine learning-based predictive control using on-line model linearization: Application to an experimental electrochemical reactor

The electrochemical reaction-based process, a new type of chemical process that can generate valuable products using renewable electricity, is a sustainable alternative to the traditional chemical manufacturing processes. One promising research area of electrochemical reaction processing is to reduce carbon dioxide (CO2) into carbon-based products, which can contribute to closing the carbon cycle if CO2 is directly captured from the atmosphere. In this work, we demonstrate a model predictive control (MPC) scheme that uses a neural network (NN) model as the process model to implement real-time multi-input-multi-output (MIMO) control in an electrochemical reactor for CO2 reduction. Specifically, a long short-term memory network (LSTM) model is developed using historical experimental data of the electrochemical reactor to capture the nonlinear input-output relationship as an alternative to the complex, first principles-based model. Furthermore, the Koopman operator method is used to linearize the LSTM model to reduce the nonlinear optimization step in the MPC to a well-understood and easy-to-solve quadratic programming (QP) problem. The performance of the LSTM model, Koopman-based optimization, and MPC using the linearization of the LSTM model are evaluated with various simulations as well as open-loop and closed-loop experiments. As the results, the proposed MPC scheme can drive the two output states, that are concentrations of the products (C2H4 and CO), to their desired setpoints by computing optimal input variables (surface potential and electrode rotation speed) in real-time in closed-loop experiments. Furthermore, a transfer learning-based method is utilized to update the NN model to handle process variability.

An intelligent monitoring model for greenhouse microclimate based on RBF Neural Network for optimal setpoint detection

The greenhouse climate is a complex dynamic system that requires careful optimization to achieve maximum plant growth with minimum energy consumption. The energy used by greenhouse (GH) actuators has an indirect but detrimental effect on profits. In multi-input/multi-output systems, conventional proportional–integral–derivative (PID) controllers are unable to determine the desired setpoints for the greenhouse microclimate. In this study, an innovative approach to monitoring the greenhouse microclimate is proposed, the Guidance Control Unit (GCU), which optimizes the setpoints of PID controllers for energy efficiency. The proposed system goes through two stages. The first uses Multi-Objective Optimization (MOO) to minimize greenhouse energy consumption based on optimal setpoints generated using the non-dominating sorting genetic algorithm (NSGA-II). The second stage replaces the MOO with a machine-learning approach — the Radial Basis Function Neural Network (RBFNN). After training and testing the RBFNN offline on the generated data, it was used to detect the optimal setpoints for the PID controllers in any weather conditions. The resulting setpoints represent two critical climatic variables, indoor temperature and relative humidity, and are provided to the PID controllers with values that take account of external disturbances. The proposed system showed good detection of the optimal setpoints and maintained the greenhouse system’s energy efficiency at over 25.88%, the overshoot at less 10.92% and rise time showed 11.27 % with an average RMSE of 0.069 for the RBFNN. There was only a minor (0.0017%) difference in prediction error between the results using RBFNN and NSGA-II. The proposed system fulfilled the study’s objectives by detection the optimal setpoints of PID controllers while saving energy and can also be applied to other complex systems, such as industrial buildings, that require more than one optimization step.

Data Acquisition System with Different Control Application Strategies Using LabVIEW

For real- time data acquisition LabVIEW is suitable as it is easy to learn and use to design prototype model and deployment along with the ability to solve complex algorithm in real time.This paper focuses on the basic concept for the development environment of LabVIEW and examples of different control actions with simulation results. Simple creation of computer communication is controlled by LabVIEW application. Simulation study is done for different control action and the effect can be shown in LabVIEW graphical environment. The software is very user- friendly due to its graphical icon source code. Thefront panel of LabVIEW helps with various measurement results as it uses GUI and the desired type set point can be controlled by selecting or setting the set point.

Distributed Adaptive Control Framework for Enhanced Voltage and Frequency Regulation in Inverter Interfaced Autonomous Distribution Network

This article presents a unique design method for an adaptive neural network based backstepping-like control (ANNBC) scheme. The technique is employed for synthesizing the primary controller for inverter interfaced distributed generators (IIDGs) integrated to an autonomous distribution network. Further, an optimal distributed secondary control framework is developed for a multiple IIDGs-based autonomous distribution network. The secondary controller facilitates optimal gain selection to regulate the frequency of the system and voltage of the critical bus to their desired set points. The framework also achieves accurate real and reactive power sharing among the IIDGs according to their power ratings. The novel design procedure of the proposed control framework takes into account the entire system dynamics of the IIDG including the uncertain terms (viz., load current and network dynamics) and is completely independent of the system parameters information. Suitable update laws are designed for estimating the unknown weights of the neural network and the uncertain system parameters. Lyapunov analysis is used to show that the tracking errors and parameter estimation errors are uniformly ultimately bounded. Finally, case studies are conducted on a typical autonomous distribution network having a single and multiple IIDGs modeled in MATLAB/Simulink platform.

Performance analysis and optimisation of the chiller-air handling units system with a wide range of ambient temperature

The integrated optimisation modelling for the chiller-air handling units system is developed for increasing the efficiency and energy utilisation of system. A building management system in the chillers network controls the cooling load to ensure the specified desired set temperature of the cooling air within the building can be satisfied. Unfortunately, the desired set point temperature of the cooling air is a fixed value and does not vary with the dynamic change of cooling demand with different ambient temperatures. Therefore, the power consumption of the chillers and the building cooling requirement with a wide range of different ambient temperatures is properly modelled by optimising the performance of chillers, air handling units, cooling towers, and water pumps. The linear programming model for the system is established to model a real representation of the chiller-air handling unit system. The result shows that the optimal coefficient of performance is greater by about 7%–10% than the current chiller system. The optimal power consumption of the chiller system reduces to 3%. Overall, the optimal decision solutions could be used as the potential improvement strategy to control the desired set point values in the building management system for efficient chiller-AHU system.

Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones

In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors’ and patients’ desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control.

MONITORING DAN KENDALI KECEPATAN MOTOR UNIVERSAL MENGGUNKAN HUMAN MACHINE INTERFACE (HMI)

Along with the development of increasingly advanced technology, many tools are created that can facilitate human work. For example, in the industrial world, tools are needed that can increase the efficiency of the production process. Electrical equipment with universal motor propulsion is generally equipped with a speed regulation that is limited to several choices of certain levels. The purpose of the Human Machine Interface (HMI) is to increase the interaction between the machine and the operator through a computer screen display so that the user's need for the system information provided makes physical work easier. The motor speed value (rpm) is read by the microcontroller and then instructs the mosfet driver to change the pwm (duty cycle) value that is read and responds to the universal motor. The encoder sensor reads the universal motor speed and then displays the speed value (rpm) with a graph on the Labview monitor. HMI in industry has a very important role in the monitoring and control system of a production system so that with this system it can save time and labor for observing and controlling each production work station. For the final stage in testing the monitoring and speed control tools the system works well, and has also obtained the gain determination of Kp = 0.25, Ki = 0.2 and Kd = 0 to get the desired set point and response. Then the last experiment was carried out by operating a universal motor from a speed of 1500 rpm, a speed of 2000 rpm and a speed of 2500 rpm. After that the rpm is returned to the point of 2000 rpm.

A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.