Set-point Changes Research Articles

A comparison between grey-box models and neural networks for indoor air temperature prediction in buildings

Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air temperature prediction tests have been performed on simulated and measured data from buildings with different thermal insulation and thermal mass during both heating and cooling seasons. Results show that neural networks have, on average, a better prediction performance than grey-box models. Both modelling approaches are affected by the building characteristics and by the season considered. The grey-box models require less training data, although the latter seems to play a role only in the worse-performing tests. When user setpoint changes in the testing phase, the LSTM neural network shows a significant drop in the root mean square error. In conclusion, although LSTM outperforms grey-box models on average, the reduced training data and higher reliability under normal operating conditions, as well as their linearity, make RC models a strong alternative for predictive controllers.

IMPLEMENTATION OF AN ECONOMICAL IoT-BASED LIQUID TANK CONTROL SYSTEM TO SUPPORT ONLINE PROCESS CONTROL PRACTICUM

Due to the pandemic era, many courses in higher education were conducted online to minimize the risk of disease transmission. For several engineering courses that contain practical material, an appropriate method is required to deliver practicum learning materials online to maintain the quality of these courses. Therefore, in this paper, the implementation of an economical IoT-based (Internet of Things) liquid tank control system was introduced to support the online practicum class. The tank system design has a height of 120 cm and a diameter of 50 cm. The tank features a valve, flowmeter, and an 18 LPM water pump. The control systems, consisting of Arduino Uno, an ultrasonic sensor, and a stepper motor, was run on the MATLAB Simulink application. IoT systems can be deployed using the TeamViewer app. After running the simulation, it was found that the lower setpoint change did not make significant changes in the process and produced the same curve shape as the steady state. The curves at steady state and during processing are not much different for the disturbance change. The error can be calculated using IAE and ITAE. The performances of the control system with IAE and ITAE values are 1.460 and 915.122 for setpoint change, respectively. For disturbance change, the IAE and ITAE values are 0.877 and 490.446, respectively. This system can help students better understand the industry 4.0 concept and allow them do practical work effectively from home at a low cost.

The Sub-Metered HVAC Implemented for Demand Response Dataset

Abstract Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research.

Automatic Tuning of Level Controllers in a Flotation Bank using Bayesian Optimisation

A flotation bank consisting of 6 cells in series under Single-Input-Single-Output (SISO) Proportional Integral (PI) level control is automatically tuned using Bayesian Optimisation (BO). Open loop step tests from the valve position to the level are used to identify first-order plus time-delay (FOPTD) models for each flotation cell. The PI controller settings are tuned according to the Skogestad Internal Model Control (SIMC) tuning rules. Stability bounds derived from µ-analysis are defined using these SIMC settings. As the optimum achieved by the Bayesian optimiser is largely dependent on the parameter space provided to the tuning algorithm, this space is selected first to ensure stability and secondly for performance. The BO framework is able to tune each of the six SISO PI controllers to provide significantly improved level control over the original SIMC controllers with regards to different forms of the integrated error when the plant is subjected to step changes in the level setpoints and disturbances to the feed flow. This improvement comes at the cost of an increased number of tests to conduct.

Water Quality Trade-offs for Risk Management Interventions in a Green Building.

Premise plumbing water quality degradation has led to negative health impacts from pathogen outbreaks (e.g., Legionella pneumophila and non-tuberculous mycobacteria), as well as chronic effects from exposure to heavy metals or disinfection by-products (DBP). Common water quality management interventions include flushing, heat shock (thermal disinfection), supplemental disinfection (shock or super chlorination), and water heater temperature setpoint change. In this study, a Legionella pneumophila- colonized Leadership in Energy and Environmental Design (LEED) certified building was monitored to study health-relevant water quality changes before and after three controlled management interventions: (1) flushing at several points throughout the building; (2) changing the water heater set point; and (3) a combination of interventions (1) and (2) by flushing during a period of elevated water heater set point (incompletely performed due to operational issues). Microbial (culturable L. pneumophila, the L. pneumophila mip gene, and cATP) and physico-chemical (pH, temperature, conductivity, disinfectant residual, disinfection by-products (DBPs; total trihalomethanes, TTHM), and heavy metals) water quality were monitored alongside building occupancy as approximated using Wi-Fi logins. Flushing alone resulted in a significant decrease in cATP and L. pneumophila concentrations (p = 0.018 and 0.019, respectively) and a significant increase in chlorine concentrations (p = 0.002) as well as iron and DBP levels (p = 0.002). Copper concentrations increased during the water heater temperature setpoint increase alone to 140°F during December 2022 (p = 0.01). During the flushing and elevated temperature in parts of the building in February 2023, there was a significant increase in chlorine concentrations (p = 0.002) and iron (p = 0.002) but no significant decrease in L. pneumophila concentrations in the drinking water samples (p = 0.27). This study demonstrated the potential impacts of short term or incompletely implemented interventions which in this case were not sufficient to holistically improve water quality. As implementing interventions is logistically- and time-intensive, more effective and holistic approaches are needed for informing preventative and corrective actions that are beneficial for multiple water quality and sustainability goals.

BLDC Motor Stability Management Using Adaptive PID (MRAC-PID)

BLDC motors have become popular in various industries such as automotive, consumer, healthcare, industrial automation, and instrumentation due to their optimal performance. To keep the BLDC motor in optimal condition, a control engineering system is required that serves as a controller. A single Proportional Integral Derivative (PID) control system is only suitable for linear conditions, so it cannot produce satisfactory output when there is a change in set point. To overcome this obstacle, an adaptive PID control system known as MRAC-PID control system is applied, which is able to control the stability of the BLDC motor as desired. Testing of this system is done under 4 different conditions using MATLAB software. After testing, the parameter values for the MRAC control system were obtained, namely ???????? = 0.4; ???????? = 50.75; ???????? = 0.0000867. Based on the test, the MRAC control system produces a system success rate of 81.2% to 98.9%.

Proposal of a control scheme for testing a centrifuge-based pellet injection system in DIPAK-PET

The envisaged Pellet Engineering Testbed (PET) in the framework of Direct Internal Recycling Integrated Development Platform Karlsruhe (DIPAK) will provide unique opportunities to advance pellet injection technology and its implementation in the fuel cycle as well as into plasma control of EU-DEMO. This work analyses the parameters for Pellet Launching System (PLS) control according to requirements from plasma control. Main parameter is the particle flux and the related accuracy with respect to amount of flux and arrival time of pellets. Others are addressed as well like dynamic response to setpoint changes and adaptivity of isotope composition of the hydrogen ice. The performance of the PLS is limited by the mass loss in the guiding tubes, both by erosion (applies to any pellet) and loss of an entire pellet, that may apply to only a few pellets, provided a suitable guiding tube geometry is selected depending on the required injection speed. DIPAK-PET has to provide data points in order to enable proper guiding tube design activities based on modelling. A short description of the proposed technical solution for the Pellet Launching System is provided and setup for the Programmable Logic Control (PLC) is suggested comprising a MasterPLC and a dedicated Human Machine Interface (operators’ desk). A non-exhaustive proposal of experiments to be performed in DIPAK-PET is listed in order to verify the estimated performance values for the relevant PLS parameters.

Robust predictive fault-tolerant switching control for discrete linear systems with actuator random failures

Under the condition that state variables are unavailable, a robust predictive fault-tolerant switching control method is proposed for discrete linear systems with actuator random failures. The randomness of failures is described in terms of conditional probabilities. Then, a transfer probability matrix is established to predict the probability of state change. A model incorporating the system state deviation and tracking error is further developed to enhance the regulation of the controller. By using related theories, sufficient conditions for the stability of the system are obtained. Furthermore, average dwell times for the system are obtained according to exponential stability analysis. These times and the matrix form conditions for the output feedback fault-tolerant switching controller to switch between conventional control and fault-tolerant control. Based on the previous work, the research on disturbances and set-point changes has been extended. The feasibility of the method is verified by a case study of the pressure-holding phase.

Potential for Electricity Reduction in Demand Response Using Building Thermal Storage

Demand response (DR) contributes to filling the gap between electricity supply and demand by controlling energy resources on the consumer side and promoting the installation and use of renewable energy. This study is performed to identify the potential of building thermal storage for power reduction in DR. In addition, the optimal conditions are identified, in particular the on/off status of air-conditioning and changes in room temperature setpoints. A simulation model for an office building equipped with packaged air-conditioners is developed, and dynamic programming is applied to calculate the on/off and load ratios to minimize the cumulative electricity during the DR period. Based on the results, two types of operations are suggested: one is to maintain the temperature at the upper allowable limit (in cooling) during the DR period, and the other is to repeatedly turn the air-conditioner on and off. In addition, the following factors affect power reduction: the allowable room temperature range, the time between the request for reduction and the start of the DR period, and the partial load characteristics of the air-conditioning equipment.

CFD-based bioreactor model with proportional-integral-derivative controller functionality for dissolved oxygen and pH.

A physics-based model for predicting cell culture fluid properties inside a stirred tank bioreactor with embedded PID controller logic is presented. The model evokes a time-accurate solution to the fluid velocity field and overall volumetric mass transfer coefficient, as well as the ongoing effects of interfacial mass transfer, species mixing, and aqueous chemical reactions. The modeled system also includes a direct coupling between process variables and system control variables via embedded controller logic. Satisfactory agreement is realized between the model prediction and measured bioreactor data in terms of the steady-state operating conditions and the response to setpoint changes. Simulation runtimes are suitable for industrial research and design timescales.

Cascade Control Applied to a Single-Component Single-Stage Vaporizer—Modeling and Simulation

This comprehensive research addresses a gap in the literature by providing an extensive examination of a single-component single-stage vaporizer process. The research involves sophisticated analyses such as dynamic modeling, comprehensive control system design and performance evaluation. The study systematically derives a linear state-space model from complex nonlinear dynamic models, laying the basis for the development of two highly effective control systems specifically designed for vaporizer level and temperature control. The simulations of these control structures demonstrate notable properties, including fast disturbance rejection, minimal overshoot, and virtually no steady-state error, emphasizing their robustness and precision. This research focuses on the stability and response of the system and provides insight into its transient behavior during disturbances and setpoint changes. The study's broad implications extend beyond the results and provide a path for future improvements. The results indicate ways to refine the start-up stage, minimize initial overshoot during system initialization, and further improve control strategies. This work has the potential to make a difference in advancing the field of vaporization processes, providing engineers and researchers with the tools and insights needed to improve system reliability and performance in industrial applications.

Performance monitoring of heat exchanger networks using excess thermal and hydraulic loads

Fouling in heat exchanger networks (HENs) affects thermal and hydraulic efficiencies resulting in economic penalties to the process industries. Conventionally, overall heat transfer rates of the heat exchangers in HENs are monitored to identify fouling. However, apart from fouling, these rates also vary with inlet conditions. Hence, estimating the degree of fouling accurately based only on heat transfer rates can fail. Previously, we had proposed the use of excess thermal and hydraulic loads as the basis for monitoring fouling in standalone exchangers (Patil et al., 2022). These are quantified as thermal and hydraulic performance indicators that can be easily tracked through suitably constructed charts. In this paper, we extend the approach to HENs wherein deviations in temperature drop across a heat exchanger vis-à-vis the clean condition also depends upon upstream heat exchanger(s). The network performance monitoring charts rely on normalized performance indicators and temperatures across heat exchangers and also have explicitly denoted normal, alert, and alarm regions. The proposed charts are applicable to HENs with or without temperature controllers. Two case studies comprising multiple scenarios of flow rate and setpoint changes are studied. The results show that the method can effectively identify the degree of fouling.

Design and implementation of PID based flow rate control using PLC

Industrial systems require efficient techniques to observe the stable operation of various industrial processes and to achieve optimal control. Considering the importance of industrial processes, Proportional Integral and Derivative (PID), Adaptive PID, and fuzzy logic are the most utilized control systems. Programmable Logic Controller (PLC) is a low-cost solution for the industrial processes requiring control and having the flexibility of graphical user interface. In this paper, an experimental study on flow rate control system for water flowing in a vessel is realized and implemented using Proportional (P), Proportional Derivative (PD), Proportional Integral (PI), and PID controllers with Programming Logic Controller (PLC). For optimal control, the constants for the PID controller are calculated based on Zeigler-Nichols (ZN) rules. ZN tuning rules can be used to find controller constants where the plant dynamics are not available. The experimental analysis is performed to validate the theoretical concepts. The achieved results and analysis demonstrate that the process variable, which is water inflow rate 4.92L/minute, is equal to the set point without any overshoot and remains controllable at every set point change.

Voltage-dependent bundle motion in mammalian auditory hair cells

Auditory hair cells have bundles of protrusions on their surface held together by proteinaceous links. Sound-induced vibrations deflect these bundles, transferring force through the links to open nearby mechanically sensitive ion channels and providing our sensation of sound. An increase in bundle compliance caused by channel gating can also be detected as motion. The channels open during depolarization and the bundle moves rapidly toward shorter stereocilia, followed by a larger but slower movement toward the taller stereocilia. To determine how voltage changes affect mammalian inner hair cell (IHC) bundles, we used whole cell patch clamp and high-speed imaging to apply electrical stimulation and measure bundle motion. We found two components in the motions of freestanding IHC bundles in response to depolarization, a fast offset away from the tallest row and a slower movement back toward the tallest row. Like in turtles, the slower motion was sensitive to channel block and suggested ion flux, particularly calcium, played a role. Our findings show IHCs can rapidly respond to changes in the bundle setpoint without ion influx. This work improves our understanding of the mechano-electrical transduction process and helps address the controversial role of calcium in regulating the channel current response.

Assessment of Linear Quadratic Regulator Based Optimal Stabilization of Weak-Grid-Following Inverter Interface

An optimal state- feedback based decentralized stabilizing control scheme for weak-grid connected inverter interfaces is proposed here. Linear Quadratic Regulator (LQR) based feedback control options viz. small-signal perturbations in the reference signals, and in the control gains are explored for the same. It is shown that, with full-state feedback of local control variables, the stability enhancement is not much effective with gain perturbations. Rather, the reference set-point change is shown to stabilize the inverter to weak-grid interconnection. Eliminating the need of communication assisted auxiliary inputs or the need for their estimation, the proposed approach is effective in achieving stability margin enhancement for wider range of X/R ratios and grid impedances. The cost of achieving stabilization using totally device-level information is the compromise made in set-point adjustment. However, this can be adopted for emergency weak-grid scenarios in a generic power system. The proposed control augmentation is analysed for different operating scenarios in which one or the other control loops are enabled, and corresponding modifications in the controller are discussed. The proposed claims are validated using small-signal models, simulation studies in MATLAB/Simulink as well as with a power-hardware-in-the-loop (PHIL) experimental setup.

Cooperative Reference Velocity Regulation Control for Virtual Coupling Systems of Heavy-Haul Trains

Virtual coupling systems are important for improving the safety and efficiency of heavy-haul trains by coordinating the velocities of heavy-haul trains. However, the set point change of reference velocity leads to unsafe train operation. To address this issue, in this paper, a cooperative reference velocity regulation control method is proposed for virtual coupling systems of heavy-haul trains, which can smoothly adjust the train velocity during the operation process. Firstly, the heavy-haul trains are characterized by a cyber-physical model, where the physical layer models the longitudinal dynamics of the trains, while the cyber layer represents the communication topology of the trains using graph theory. Secondly, a cooperative control law is designed to achieve velocity consensus and maintain a safe distance between adjacent trains. The set point modulation method is utilized to smoothly adjust the reference velocity of heavy-haul trains, suppressing velocity fluctuations of heavy-haul trains. The proposed approach is validated through extensive simulations under acceleration and deceleration conditions. Simulation results verify that the proposed method can significantly reduce the velocity fluctuations during the operation of virtual coupling systems when compared with the classical cooperative control method.

Improved performance of Hexacopter's roll balance control system

<span lang="EN-US">One of the obstacles in determining the constant of the proportional integral derivative (PID) control system for the stability of the Hexacopter is due to the dynamic response of the system. Changing the speed and direction of the aircraft's motion through the throttle is translated into a control system concept into a set-point change. If you have used PID control, which is constant, cannot adjust to changes in set-points or external influences, the stability and reliability of the aircraft cannot be guaranteed. This study proposes PID control, with adaptable constants, using a fuzzy logic controller (FLC). The influence of internal changes and factors outside the aircraft control system, in principle, will accumulate on the size of the error and delta-error. Thus, FLC performs tuning for the PID constant according to the error and delta-error. The design of fuzzifications and defuzzification is based on the maximum limit value of error and delta-error, and sets the value of the constant obtained by the Ziegler-Nichols method as the default value. After the real-plant test, the system performance is obtained as follows: settling time = 34mS; peak time 21mS; rise time = 9.2 mS; delay time = 5.7mS; percent overshoot and steady state error = 1%.</span>

Robust model agnostic predictive control algorithm for randomly excited dynamical systems

We propose a novel robust model agnostic predictive control (RoMAn-MPC) algorithm and illustrate its application in randomly excited dynamical systems. Unlike conventional model predictive control algorithms, the proposed RoMAn-MPC requires no information about the underlying physics of the system; instead, the governing physics is identified by using the recently proposed stochastic equation discovery framework. One key advantage of the proposed approach resides in its capability to generalize; this eliminates the repeated retraining phase — a major bottleneck with other machine learning-based model agnostic control algorithms. Overall, the proposed RoMAn-MPC (a) is robust against measurement noise, (b) works with sparse measurements, (c) can tackle set-point changes, (d) works with multiple control variables, and (e) can incorporate dead time. We have obtained state-of-the-art results on several benchmark examples. Finally, we use the proposed approach for vibration mitigation of a 76-storey building under seismic loading.

Improving the air supply and hydrogen dilution control of a fuel cell electric vehicle with explicit model predictive control

This study demonstrates the applicability of an explicit model predictive controller (ExMPC) to the air mass flow control in a fuel cell electric vehicle. The two control tasks comprise the supply of air to the fuel cell stack for the electrochemical reaction as well as the dilution of hydrogen in the outlet gas flow of the cathode subsystem. With the aim of achieving zero control deviation for steady states, the linear model in the state observer is extended with two perturbations. During the experimental validation of the proposed controller, fast dynamics are achieved with the ExMPC, which generally exploits the limits of the control variables. After the calibration of the ExMPC, the final comparison to a set of reference PI controllers reveals a significant improvement in the closed-loop behavior of the cathode subsystem during step changes in the controller setpoints.

O Splines-Based Fixed-Frequency Integral Sliding-Mode Controller for PFC Rectifier

This paper proposes a fast-response sliding mode controller (SMC) for a semi-bridgeless boost converter under large and quick load fluctuations to ensure tight output voltage regulation and unity power factor correction (PFC) at the line side. In this sense, a novel approach for estimating the reference current profile is presented focusing on the real-time phasor estimation via the O-splines of the discrete-time Taylor-Fourier transform (DTTFT). This method allows for improving the computational efficiency and dynamic performance of the estimations of amplitude, frequency and phase of the network voltage used for the generation of the reference current profile. Several aspects of the controller design are discussed, including the choice of the sliding surface, the existence and stability conditions, and the implementation of an adaptive hysteresis band to fix the switching frequency and reduce zero-crossing distortion. Experimental results of a GaN-based prototype validate the theoretical predictions, exhibiting a PF close to 1 and a total harmonic distortion lower than 3.2% in presence of load changes of up to 50% and changes in the output voltage set point. Several comprehensive experimental comparisons between the proposed framework and the most widely used methods recently reported in the literature are accomplished in terms of transitory and steady-state responses. The robustness of the proposed control approach is experimentally demonstrated under sag conditions and a wide operating range of the input voltage.