Desired Set-point Value Research Articles

Distributed Feedback Control of Multichannel Linear Systems

In this paper it is established that any jointly controllable, jointly observable, multi-channel, discrete or continuous time linear system with a strongly connected neighbor communication graph can be exponentially stabilized with any pre-specified convergence rate using a time-invariant distributed linear control. As an illustration of how this finding can be used to deal with certain distributed tracking problems, a solution is given to a distributed set-point control problem for a continuous-time, multi-channel linear system in which each and every agent with access to the system is able to independently adjust its scalar-valued, controlled output to any desired set-point value. To better understand the constraints on controller design, the distributed control problem is recast as a classical decentralized control problem. Armed with the tools of decentralized control, including the notion of a “fixed spectrum”, it is possible to show quite surprisingly that if the only information each agent is allowed to share with its neighbors is its measured output, then distributed stabilization in some cases is impossible. Using well-known decentralized control concepts, lower bounds are derived on the dimensions of the shared sub-states of local controllers which, if satisfied, guarantee that there will be no fixed closed-loop system eigenvalues to contend with. The decentralized control perspective also enables one to assert definitively that without imposing a partitioning constraint, the closed-loop spectrum of any jointly controllable, jointly observable multi-channel linear system with a strongly connected neighbor graph, can be freely assigned with distributed feedback control. The paper then turns to the important and often overlooked design problem of dealing with the effects of transmission delays across a network. It is explained why in the face of finite delays, exponential stabilization at any prescribed convergence rate can still be achieved with distributed control, at least for discrete-time multi-channel linear systems.

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.

Development of a battery management system for Centralized Control of a Battery Cluster

The purpose of this paper is to establish a supervisory battery management system which collects measurements from the installed battery storage units and enables various battery management functions. In addition, an HMI user interface was designed to allow the user to monitor and control the batteries. The batteries communicate with the PLC via Modbus TCP/IP protocol. The communication enables the exchange of measured power and state of charge values between the PLC and batteries, as well as setting the desired setpoint values. The PLC and HMI communicate via PROFINET. The battery storage units are installed in Smart Grid Laboratory (SGLab) at the University of Zagreb Faculty of Electrical Engineering and Computing.

Achieving Optimal Paper Properties: A Layered Multiscale kMC and LSTM-ANN-Based Control Approach for Kraft Pulping

The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization (DP). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose DP while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-point values with the use of a computationally faster surrogate model with high accuracy and low offset.

Sliding-Mode Boundary Control of an In-Line Heating System Governed by Coupled PDE/ODE Dynamics

This present article addresses the feedback control design problem of in-line liquid flow heating units used to process silicon wafers in the semiconductor industry. A physics-based model is developed to describe the thermal behavior of the heater and the liquid circulating in it. Under sensible approximations, process dynamics are shown to be governed by a coupled partial differential equation (PDE)/ordinary differential equation (ODE) system. A boundary control law capable of steering the fluid temperature to the desired set-point value while rejecting matched disturbances is developed. The proposed boundary control is sliding-mode-based and makes use of boundary measurements only. The convergence properties of the closed-loop system are formally demonstrated by means of Lyapunov-based analysis. In addition, the input-to-state stability (ISS) properties of the closed-loop system with respect to nonmatching boundary disturbances are investigated. To corroborate the theoretical findings, this article additionally presents the experimental results with the proposed controller implemented in the closed loop on a real in-line heating unit.

Real-time metabolic heat-based specific growth rate soft sensor for monitoring and control of high molecular weight hyaluronic acid production by Streptococcus zooepidemicus.

This present investigation addressing the metabolic bottleneck in synthesis of high MW HA by Streptococcus zooepidemicus and illustrates the application of calorimetric fed-batch control of µ at a narrower range. Feedforward (FF) and feedback (FB) control was devised to improve the molecular weight (MW) of HA production by S. zooepidemicus. Metabolic heat measurements (Fermentation calorimetry) were modeled to decipher real-time specific growth rate, [Formula: see text] was looped into the PID circuit, envisaged to control [Formula: see text] to their desired setpoint values 0.05 [Formula: see text], 0.1 [Formula: see text], and 0.15 [Formula: see text] respectively. Similarly, a predetermined exponential feed rate irrespective of real-time µ was carried out in FF strategy. The developed FB strategy established a robust control capable of maintaining the specific growth rate (µ) close to the [Formula: see text] value with a minimal tracking error. Exponential feed rate carried out with a lowest [Formula: see text] of 0.05 [Formula: see text] showed an improved MW of HA to 2.98 MDa and 2.94 MDa for the FF and FB-based control strategies respectively. An optimal HA titer of 4.73g/L was achieved in FF control strategy at [Formula: see text]. Superior control of µ at low [Formula: see text] value was observed to influence HA polymerization positively by yielding an improved MW and desired polydispersity index (PDI) of HA. PID control offers advantage over conventional fed-batch method to synthesize HA at an improved MW. Calorimetric signal-based µ control by PID negates adverse effects due to the secretion of other end products albeit maintaining regular metabolic activities. KEY POINTS: First report to compare HA productivities by feedforward and feedback control strategy. Inherent merits of regulating µ at narrower range were entailed. Relationship between operating µ and HA molecular weight was discussed.

Simulation and Implementation PID Controlling Buck Converter DC

Regulating the output voltage based on the desired set point is useful for many applications. However, getting the optimal value using fast computation with minimal error is still challenging. This paper aims to design, simulate, and implement a second-order Buck-Boost DC-DC converter circuit so that the voltage result according to the desired set point can be achieved. Initially, testing is conducted using Matlab Simulink. Then, Proteus is used to test the computation of the program on embedded systems in which the result is implemented in C. In low voltage power electronics applications, this approach has never been used to determine the output form. To determine the value of Kp, Ki, dan Kd, PID, Ziger Nichos (Guo, 2002). method is used. Meanwhile, tuning is done through Matlab. For simulation on Proteus, the output is tested by setting the setpoint values of 3.0, 2.5, and 1.7 volts. This aims to see the pattern of changes in the simulation. The simulation results with Proteus show that they have similar peak values but with different overshoot values. This is because the simulation must pass the reference voltage before it drops to the desired setpoint value. Proteus simulation can also help to prove embedded system programs are running correctly. On the other hand, the value of 1.7 volts is used as a setpoint in device implementation. This is due to the determination that the setpoint voltage in the implementation does not exceed the value of the source/power supply. The results show that for the rise time value of 378,770 ms, Overshoot and settling time are 11.798% and 0, respectively. This means the result produces an optimal value which is a return to the initial target. The optimal factor is assessed from the ability to minimize existing errors as well as having the shortest possible computational process.

Automatic control of the sand granulation process to improve homogeneity in the manufacture of ceramic tiles

The purpose of this article is to quantify to what extent the design of an automatic control system for the clay sand granulation process will improve the percentage of homogeneity in the manufacture of ceramic tiles, through the analysis of the percentage of dust moisture. For which the process under study is initially described, identifying its stages, the control strategy to be used and its main characteristics. After conducting the research, it was determined that from the proposed control strategy, in which the flow of clay sand is controlled through frequency variators and the sequential injection of water spray through electric nozzles, it was achieved improve the homogeneity percentage by 94.78%, achieving that the error between the desired value and the present value of the humidity percentage is 0.015%, the approximation to the desired value or setpoint value being significant. Thus, it was also possible to determine that through an analysis of the dispersion model, that the collected data respond to a constant; evidencing that there is no sudden variation in the percentage of homogeneity during the automated operation of the process, which validates the improvement of homogeneity in the manufacture of ceramic tiles.

Closed-loop control of microstructure and mechanical properties in additive manufacturing by directed energy deposition

A closed-loop directed energy deposition (CDED) system is developed to control the microstructure and mechanical properties of laser additive manufactured material in real-time. An austenitic stainless-steel grade of S316-L, which avoids eutectoid phase transformation, is examined to evaluate this unique control technique's performance. A set of walls are constructed in an open-loop manner by the laser additive manufacturing process to study the thermal dynamics and microstructure variations of a multi-layer structure under fixed processing conditions. The microstructure and subsequent mechanical properties of different thicknesses are affected mainly by the cooling rate change, resulting in a graded microstructure profile and mechanical properties in the deposition cross-section. The CDED system is implemented to eliminate cooling rate variations and locally control the deposition cooling rate to the desired setpoint value in similar five-layer depositions. This control is achieved by adaptively adjusting the traveling speed in real-time rather than keeping it constant. The closed-loop control of the cooling rate results in a uniform microstructure and hardness throughout the deposition cross-section and different layers. Although improved uniformity is observed in closed-loop samples, some microstructural and hardness deviations are still present, caused by post-solidification heating cycles of subsequent layers.

Use of Hydraulically Operated PRVs for Pressure Regulation and Power Generation in Water Distribution Networks

In recent years, power generation has been coupled with pressure regulation to optimize management of water distribution networks, also showing attractive revenues and short payback periods. A recent study proposed a prototype that performs both electric and hydraulic regulation for pressure regulation and hydropower generation in a water distribution network, wherein laboratory experiments showed the effectiveness of the control algorithms and the prototype’s capability in regulating pressure at the desired set-point value while optimizing power production. However, the prototype uses needle valves for flow and pressure regulation, whereas hydraulically operated pressure-reducing valves (PRVs) can be more effective for pressure regulation. Moreover, such PRVs require much less power for operation, thus proving more reliable in case of electrical blackout. Consequently, a different prototype was proposed, which uses hydraulically operated PRVs instead of needle valves. This paper discusses the control algorithm of the system to achieve pressure regulation and optimal power generation. A proportional integral controller was used for PRV control, whereas the inverter control was performed by means of both an external optimization module and dimensionless variables. Laboratory experiments confirmed the effectiveness of the proposed approach and pointed out the effect of the gains on the process dynamics.

Feedback-feedforward fuzzy logic approach for temperature control in bioethanol vacuum distiller

<span>Energy conservation and diversification are becoming a major research issue. Awareness of the limited sources of energy from fossil fuels encourages research on renewable energy. Bioethanol is a promising fuel substitute for gasoline. Bioethanol processing includes sugar extraction, fermentation, distillation, and absorption. Temperature and pressure controls are essential in bioethanol processing. This paper presents a feedback-feedforward fuzzy logic approach for temperature control in a bioethanol vacuum distiller. In this study, vacuum pressure is employed as feedforward inputs for a fuzzy logic controller. The feedforward input directly modifies the main controller, i.e., fuzzy logic controller, through fuzzy rules. The controller is implemented using Arduino Mega 2560 microcontroller. The results show that the proposed feedback-feedforward fuzzy logic controller could successfully maintain the temperature at the desired setpoint value with small steady-state error (3.85%) and relatively shorter settling time compared to classical PID controller and fuzzy logic controller.</span>

Optimal gain scheduling of VSC‐HVDC system sliding mode control via artificial bee colony and mine blast algorithms

This study focuses on the optimal gain scheduling of the robust non-linear sliding mode control (SMC) for the voltage source converters high-voltage direct current (VSC-HVDC) systems, for ensuring the desired dynamic behaviour improvement and stability enhancement. First, the development of the system steady-state mathematical model of non-linear character is explored in order to control the active power, the reactive power, and the DC link voltage towards their desired set point values. Their measured signals, as the outputs, are controlled via the optimal SMC-based controllers, using their reference signals directly. Both artificial bee colony and mine blast algorithm are considered to optimally select the optimal gains of SMC controller that will minimise the steady-state error of the obtained dynamic behaviour. Therefore, the DC voltage, the active and reactive powers can be properly maintained at their reference values. Both optimal SMC-based techniques are compared in terms of the behavioural analysis and robustness. Using MATLAB™-Simulink, the results significantly verify that the designed controllers based on SMC can achieve favourable tracking performance and contribute efficiently towards improving the system dynamic behaviour.

CFD modeling and control of a steam methane reforming reactor

This work initially focuses on developing a computational fluid dynamics (CFD) model of an industrial-scale steam methane reforming reactor (reforming tube) used to produce hydrogen. Subsequently, we design and evaluate three different feedback control schemes to drive the area-weighted average hydrogen mole fraction measured at the reforming tube outlet (x¯H2outlet) to a desired set-point value (x¯H2set) under the influence of a tube-side feed disturbance. Specifically, a CFD model of an industrial-scale reforming tube is developed in ANSYS Fluent with realistic geometry characteristics to simulate the transport and chemical reaction phenomena with approximate representation of the catalyst packing. Then, to realize the real-time regulation of the hydrogen production, the manipulated input and controlled output are chosen to be the outer reforming tube wall temperature profile and x¯H2outlet respectively. On the problem of feedback control, a proportional (P) control scheme, a proportional-integral (PI) control scheme and a control scheme combining dynamic optimization and integral feedback control to generate the outer reforming tube wall temperature profile based on x¯H2set are designed and integrated into real-time CFD simulation of the reforming tube to track x¯H2set. The CFD simulation results demonstrated that feedback control schemes can drive the value of x¯H2outlet toward x¯H2set in the presence of a tube-side feed disturbance and can significantly improve the process dynamics compared to the dynamics under open-loop control.

Detection and Isolation of Batch-to-Batch Parametric Drift in Crystallization Using In-Batch and Post-Batch Measurements

In this work, we focus on the development of a parametric drift detection and isolation (PDDI) method for the handling of batch-to-batch parametric drifts in a batch crystallization process used to produce hen egg-white (HEW) lysozyme crystals. We consider that the batch crystallization process is controlled by an in-batch model predictive control (MPC) system and is subject to batch-to-batch parametric drifts in the solubility, growth rates, continuous-phase mass and energy balance parameters, and nucleation rate. The proposed PDDI scheme consists of two parts: preparatory stage before batch-to-batch operation and post-batch stage during batch-to-batch operation. The goal of the preparatory stage is to compute the threshold values and signatures for each parametric drift using simulations and batch process common cause variance described by noise. During batch-to-batch operation, the proposed PDDI system monitors closed-loop process residuals, which are computed by taking the difference between the time profiles of the states obtained through in-batch and postbatch measurements from the time profiles of the states obtained from the drift-free simulation with noise. While the measurements of the protein solute concentration and the temperature in the crystallizer are available in real-time, post-batch measurements are usually available for the quality of the crystal products (e.g., number of crystals, average crystal size and shape) and this key characteristic is taken into account in the PDDI method. We then compare the residuals with signatures obtained in the preparatory stage for each parametric drift for isolation of a parametric drift. The PDDI system estimates the magnitude of the parametric drift and updates the parameters of the batch process model used in the in-batch MPC system to compute a set of jacket temperatures for the production of crystals with a desired shape distribution in the next batch. The performance of the MPC with the proposed PDDI scheme is demonstrated by applying it to a multiscale simulation of a batch crystallization process with parametric drifts in the solubility and crystal growth rates. The closed-loop system simulations demonstrate that crystals with a crystal shape distribution that is closer to a desired set-point value are produced under a parametric-drift handling scheme that integrates the in-batch MPC with the proposed PDDI system compared to those under the MPC with the nominal process model.

A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization

In this work, we develop a run-to-run (R2R) model parameter estimation scheme based on moving horizon estimation (MHE) concepts for the modeling of batch-to-batch process model parameter variation using a polynomial regression scheme in a moving horizon fashion. Subsequently, the batch process model parameters computed via the proposed R2R model parameter estimation scheme are used in a model predictive controller (MPC) within each batch to compute a set of optimal jacket temperatures for the production of crystals with a desired shape distribution in a batch crystallization process. The ability of the proposed method to suppress the inherent variation in the solubility caused by batch-to-batch parametric drift and handle the noise in post-batch measurements is demonstrated by applying the proposed parameter estimation and control method to a kinetic Monte Carlo (kMC) simulation model of a batch crystallization process used to produce hen-egg-white (HEW) lysozyme crystals. Furthermore, the performance of the proposed R2R model parameter estimation scheme is evaluated with respect to different orders of polynomials and different moving horizon lengths in order to calculate the best parameter estimates. The average crystal shape distribution of crystals produced from the closed-loop simulation of the batch crystallizer under the MPC with the proposed R2R model parameter estimation scheme is much closer to a desired set-point value compared to those of the double exponentially weighted moving average-based MPC (dEWMA-based MPC) and that of MPC based on the nominal process model.

Handling Parametric Drift in Batch Crystallization Using Predictive Control with R2R Model Parameter Estimation

In this work, we develop a run-to-run (R2R) model parameter estimation scheme based on moving horizon estimation (MHE) concepts for the modeling of batch-to-batch process model parameter variation using a polynomial regression scheme in a moving horizon fashion. Then, a model predictive controller (MPC) with the proposed parameter estimation scheme is applied to a kinetic Monte Carlo (kMC) simulation model of a batch crystallization process used to produce hen-egg-white (HEW) lysozyme crystals. The average crystal shape distribution of crystals produced from the closed-loop simulation of the batch crystallizer under the MPC with the proposed R2R model parameter estimation scheme is much closer to a desired set-point value compared to that of MPC based on the nominal process model.

A new exergy-based model predictive control methodology for energy assessment and control

The paper proposes a new methodology based on integration of an exergy-auditing analysis with a model predictive control (MPC) scheme. The primary advantage of the methodology is that exergy analysis provides rich information to recognize processes that consume the major portion of energy. The proposed exergy-based MPC method thus is capable of incorporating the desired set-point values in conjunction with the corresponding operational constraints, being dictated by the exergy analysis, in design procedure of MPC. The proposed control scheme guides the system toward the desired set-points in a multi-input, multi-output (MIMO) configuration enhancement of the energy efficiency and prevention of loss of energy due to possible disturbances. The proposed method is comparatively implemented on an air separation unit as the benchmark case study in which a compressor – which is very common in gas industry – is identified as the main part with the major wasting energy. Variety of experiments was conducted to check the compressor under different disturbances. By 22 percent energy saving in the steady-state condition, the results clearly demonstrated superiority of the proposed methodology compared to the other energy-based MPC approaches.

Modeling and Control of Coal Mill

The paper presents development and validation of coal mill model (including the action of classifier) to be used for improved coal mill control. The model is developed by using the mass and heat balance equations of the coal mill. Genetic Algorithm is used to estimate the unknown parameters that are used in the model validation. The advantage is that the raw data used in modeling can be obtained without any extensive mill tests. The simulation results show a satisfactory agreement between the model response and measured value. Apart from the conventional PID controller, inorder to ensure tight control with less overshoot and to handle constraints Model Predictive Controller is designed to maintain outlet temperature and pulverized coal flow at desired set point value.

Modeling and control of shape distribution of protein crystal aggregates

We focus on the modeling and control of protein crystal aggregates in a large-scale batch crystallizer used to produce tetragonal hen-egg-white (HEW) lysozyme crystals. We initially present a kinetic Monte Carlo (kMC) simulation for the modeling of the crystal nucleation, growth, and shear-induced aggregation in an effort to control the evolution of the crystal shape distribution. Through experimental data, the crystal growth rate is calibrated and an empirical expression for the nucleation rate is also developed. Then, the method of moments is applied to a comprehensive population balance model to derive a reduced-order moment model that describes the dynamic evolution of the three leading moments of the crystal volume distribution. Along with mass and energy balances for the continuous phase, the moment model is used to design a model predictive control (MPC) strategy which drives the crystal shape distribution to a desired set-point value through the manipulation of the crystallizer jacket temperature. Compared to conventional operating strategies used in industry, it is demonstrated that the proposed MPC strategy is able to produce crystal aggregates with a desired shape distribution and a low polydispersity effectively dealing with the undesired effects of biased nucleation, depletion in the solute concentration, and changes in the average crystal shape due to the aggregation process.

A new algorithm for real-time pressure control in water distribution networks

This paper presents a new logic algorithm for real-time control of regulation valves in water distribution networks. This method entails identifying in real time the appropriate closure setting of regulation valves in order to reach and keep the desired piezometric height at the control node(s), by making use of measurements concerning both the piezometric height at the control node(s) and the water discharge in the pipes fitted with regulation valves. In the numerical application herein described this control algorithm is implemented within a hydraulic simulation model and is tested in the case study of a real distribution network, in which there is only one control valve, installed in the pipe linking the serving tank to the network. Results showed excellent performance in terms of pressure regulation (with very small deviations from the desired set-point value) and leakage reduction under various operation conditions.