Basic Control Structures Research Articles

Power instruction correction based frequency response strategy for grid forming inverter in islanded microgrids

Grid forming (GFM) inverter interfaced energy storage system can offer frequency support for islanded microgrids (IMGs), and the frequency response relies on the GFM inverter’s power output and power instruction interaction. However, droop control, a primary GFM method, faces challenges in reducing frequency deviations during significant disturbances due to the constant power instruction, potentially triggering the relay. To enhance the security of IMGs, this paper proposes a power instruction correction (PIC) based frequency response strategy for GFM inverter in IMGs. Firstly, the fundamental principle of the PIC strategy that originates the basic droop control is theoretically analyzed. When disturbances are insignificant, the PIC strategy maintains the basic droop control structure. In the event of a significant disturbance, the PIC strategy promptly corrects the active power instruction by estimating the disturbance approximately to reduce frequency deviations. Secondly, by analyzing specific operating modes and switching logic, the detailed implementation of the PIC strategy is presented. Finally, the superiority of the PIC strategy is verified through simulation and hardware-in-the-loop experiments.

Control of new energy‐intensified triple column extractive distillation using feed split technique

AbstractBACKGROUNDHere, we investigated the control performance of extractive distillation intensified with ‘feed split’ (FS) technique – a process intensification technique that has not been widely applied to extractive distillation. Today, only a handful of studies have been reported on this topic, with only one focusing on the control performance and reporting the significant trade‐off between the steady‐state and dynamic performances. Nonetheless, the previous control study was based on the complex double column FS configuration. Recently, a new triple column FS configuration was reported (Sep. Purif. Technol. 2022 (297) 121467), but its control performance was not investigated. Hypothetically, the triple column FS configuration is less complex and does not involve any column integration and thus, may demonstrate a better dynamic performance. This study analyzed the control performances of the triple column FS configuration and the conventional configuration for the binary azeotropic separation of tetrahydrofuran and ethanol.RESULTSTwo types of control structures were examined for both configurations, i.e., the basic temperature control structure and the different feed forward temperature control schemes. The best control structure from each configuration is compared and findings showed that the conventional configuration provides better controllability performance.CONCLUSIONOur results suggested that the conventional configuration can bring the product qualities closer to their setpoint, while providing a lower transient deviation. © 2023 Society of Chemical Industry (SCI).

Computationally Efficient Stability-Based Nonlinear Model Predictive Control Design for Quadrotor Aerial Vehicles

This article presents the design of a stability-guaranteed nonlinear model predictive controller for quadrotor-type microaerial vehicles to operate robustly on fast trajectories. The basic controller structure operates without having to use terminal conditions in the optimization problem. As a result, the controller is computationally less demanding and provides more stable closed-loop performance than traditional nonlinear predictive control schemes. This article presents a detailed stability analysis without terminal costs or terminal constraints and proves the asymptotic stability and necessary conditions for the recursive feasibility of the system. This article derives the growth-bound sequence that enables obtaining the shortest possible prediction horizon for stability. The proposed analysis provides the necessary conditions to implement the controller while using the shortest stabilizing prediction horizon compared to major traditional predictive control schemes reported in the literature. This particular feature enables the proposed controller to perform fast optimization and, hence, the capability to implement fast trajectories using feedback regularization. In order to demonstrate the validity of this new proposed control scheme, first, several MATLAB simulations are conducted to demonstrate the improved performance of the controller especially when the quadrotor vehicle follows fast trajectories. Real-time lab experiments are also conducted to validate the performance of the proposed scheme for point stabilization (hovering) and trajectory tracking problems. The results show that the proposed scheme can stabilize the system with a relatively short prediction horizon, a fast convergence rate, and a small tracking error.

Effect of Suspension Roll Angle Sensors on the Performance of H∞ Active Antiroll Bar Control System of Trucks

The rollover phenomenon is quite familiar with trucks and often has severe consequences worldwide. This article focuses on developing a new control structure for the active antiroll bar system using the robust control method. The main objective is to improve the vehicle’s roll stability by using additional suspension roll angle sensors. First, a truck model equipped with an active antiroll bar system is introduced. Then, the basic robust controller structure diagram is described in detail. Based on this basic robust controller, the authors develop a new robust controller considering the suspension roll angle sensors. Finally, the response of a truck using an active antiroll bar system with two robust (H∞) controllers and a passive antiroll bar system is compared and evaluated in the time domain and in the frequency domain. The results showed that the use of two sensors of the suspension roll angle sensors increased the efficiency by about 20% compared to the basic robust controller. This has proven the effectiveness of a new approach in improving the roll stability of trucks.

Dynamic controllability strategy of reactive-extractive dividing wall column for the separation of water-containing ternary azeotropic mixture

The reactive-extractive dividing wall column (REDWC), which combines conventional reactive distillation, extractive distillation, and dividing wall column, is a highly integrated process with a compact physical space and strong interrelations between variables. It is challenging to design robust control strategies for the REDWC process. However, there are no investigations reported on the control of REDWC. In this study, through analyzing open-loop controllability indicators of the steady-state process, the basic control structure (CS1) is constructed. Subsequently, four proportional-integral control schemes are further established on the CS1: the proportional control structure (CS2), the internal composition control structure (CS3), the feedforward control structure (CS4), and the three-point temperature control structure (CS5) with feedforward loops. The dynamic performance tests are conducted with ± 10 % feed flow rate and ± 10 % feed composition disturbances. The qualitative research demonstrates that the CS5 offers the best dynamic performance in terms of settling time, steady-state deviations, and overshoots without the use of expensive composition controllers. With the integral absolute error (IAE) as an evaluation criterion, it is quantitatively demonstrated that the CS5 has the best robustness and should be the preferred control scheme. Another key finding shows that the vapor split ratio and the stoichiometric ratio of the reactants are two crucial degrees of freedom to guarantee product quality in the REDWC. The major contributions of this work are to provide a meaningful reference for the control of the REDWC and even more complicated distillation.

A cognitive and neural network approach for software defect prediction

Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments.

Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance effort. Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code. Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems. In this paper, an effort has been made to categorize the Control Flow Graphs (CFGs) nodes according to their node features. In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs). We then incorporated CCMs’ results as a node feature value in CFGs and generated the same based on the node connectivity for a graph. In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN). We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects. The Python programming language is used, along with Keras and TensorFlow. Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes. Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs. Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyper-parameter tuning procedures are used to evaluate the approaches. The experimental results show that the proposed models outperformed state-of-the-art methods such as Nave Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) in all evaluation criteria.

Extractive Dividing-Wall Column Distillation with a Novel Control Structure Integrating Pressure Swing and Pressure Compensation

In this work, an energy-efficient extractive dividing-wall column process with heat integration (E-DWC-HI) is proposed for the first time to separate the dichloromethane (DCM)–methanol (MeOH) azeotrope, which is a widely existing waste effluent in the pharmaceutical industry. The result of economic evaluation shows that the E-DWC-HI process is a preferred choice for the separation of DCM–MeOH mixture. Designing a robust control strategy is essential for the E-DWC-HI process. Thus, four control structures are proposed and tested under the disturbances of ±20% feed flow rate and ±5% feed composition. The basic control structure (CS1) and the double temperature difference control structure (CS2) both have a fixed vapor split. The dynamic response shows that the required purity cannot be met due to the loss of an important control degree of freedom. In CS3, variable vapor split is carried out by adjusting the pressures on the two sides of the dividing wall, and all product purities are held close to their set points except for MeOH product purity with an obvious offset. The dynamic performance of CS4 has been significantly improved by integrating pressure swing and pressure compensation. The offset of MeOH product purity is significantly reduced from 2.05 to 0.03%.

An Automated Accident Causal Scenario Identification Method for Fully Automatic Operation System Based on STPA

Accident causal scenario can describe the process logic of the accident clearly and concretely from the perspective of the control mechanism. Only by improving the quality of the causal scenario can the effective control measures be taken. Combining the technical characteristics of the fully automatic operation (FAO) system, the paper proposes an automated accident causal scenario identification method for FAO system based on the System-Theoretic Process Analysis (STPA) method. Aiming at the problem that there are too many layers in the hierarchical control structure diagram of STPA method, which makes it impossible to effectively trace the cause and the problem that the basic control structure model only contains the control structural information and lacks the cause information, a new basic control structure model is defined to model multiple control processes in time sequence, and then the paper extends it from four aspects: control action, input variables, external disturbance, and synchronous timing to add more system cause information. For the lack of a unified standard description problem for the causal scenario, a four-stage causal scenario description method is defined, this paper has developed the first timing, non-first timing, synchronous timing, and external disturbance causal scenario search rules to ensure the automatic identification of the causal scenarios. Applying the automated safety analysis method to the case study of the operational scenarios of parking in a station of Beijing Yanfang Line, the automated identification of related causal scenarios is successfully completed through the Auto-STPA platform, and corresponding safety requirements are added. The feasibility of the method and the applicability to the analysis of operational scenarios are verified.

Measuring Scalable Business Process Model Complexity Based on Basic Control Structure

The business process model is a representation of business activities illustrated through diagram notations. This model is composed of repeated specific patterns called basic control structure. Each basic control structure has a level of complexity. The metrics for formulating existing complexity are very diverse, but can only define complexity partially and are less sensitive to small changes in the structure of the business process model. In this paper, we propose a formula of complexity metric that can indicate small changes in structure, type of branching logic, number of branches, loops, and depth. We call it the Yaqin complexity formula. To get the Yaqin complexity formula, we carried out several activities. These activities are identifying the metrics involved using the Goal-Question-Metric (GQM) method, then formulating the metric complexity, the next activity is testing the formula with several business process models and analyzing the test results, and then validating the Yaqin complexity formula using the Weyuker's properties framework. The Yaqin complexity formula, which involves seven parameters, is proven to be more comprehensive than other complexity formulas that involve less than seven parameters. The Yaqin complexity formula also proved to be more sensitive to other complexity formulas, where 7 out of 8 cases affected the Yaqin complexity metric. The validation results state that the Yaqin complexity formula meets 8 of 9 Weyuker's properties. Thus we have succeeded in formulating the Yaqin complexity, which is more comprehensive in involving parameters and more sensitive in detecting small changes in the structure of the business process model.

Comparison of dynamic performances for heat integrated reactive distillation considering safety

The heat integrated reactive distillation is beneficial on energy saving and economy compared with conventional reactive distillation. However, the dynamic controllability is unfavorable due to the complexities of system. In this paper, five different control structures for the isopropyl acetate synthesis are presented as considering both product purities and safety. The integral of absolute error (IAE) and process stream index (PSI) are used to quantify the dynamic performances. The result shows that four enhanced control schemes can achieve better dynamic performances compared with the basic control structure. Based on the four improved control structures, the control structure with temperature difference controller shows the best performance, as involving the overshoot, recovery time, IAE, and PSI, since it improves the tuning stability and adjusts the temperature difference of exchange heat process between the high-pressure column and the low-pressure column.

Dynamic control analysis of interconnected pressure-swing distillation process with and without heat integration for separating azeotrope

Abstract Dynamic controls of pressure-swing distillation with an intermediate connection (PSDIC) process of ethyl acetate and ethanol separation were investigated. The double temperature/composition cascade control structure can perfectly implement effective control when ± 20% feed disturbances were introduced. This control structure did not require the control of the flowrate of the side stream. The dynamic controllability of PSDIC with partial heat integration (PHIPSDIC) was also explored. The improved control structure can effectively control ± 20% feed disturbances. However, in industrial production, simple controller, sensitive and easy to operate, is the optimal target. To avoid the use of component controllers or complex control structure, the original product purities could be maintained using the basic control structure for the PSDIC process if the product purities in steady state were properly increased, albeit by incurring a slight rise in the total annual cost (TAC). This alternative method without a composition controller combined with the energy-saving PSDIC process provides a simple and effective control scheme in industrial production.

Control of a process with no reflux rate in heterogeneous extractive distillation (HED)

In this work, a process with no reflux rate (R) of distillate product in heterogeneous extractive distillation (HED) for the separation of close boiling compounds is proposed. The effects of pressure in HED and flowrate of solvent on controllability and economic performance are investigated. Close loop sensitivity tests show that an increase in flowrate of solvent can enhance the controllability but increases the annual total cost (TAC). Simulation shows that higher pressure in HED leads to lower flowrate ratio of solvent (EG) to feed. A lower pressure is preferred for economic index, but has worse controllability. There is trade-off between controllability and economic performance.The interaction between units is analyzed theoretically based on mass balance principle. The analysis can help further design of control structure. A basic control structure (CS1) based on modified alternative process with increased solvent flowrate is proposed first. Then an improved control structure (CS2) based on CS1 is proposed to reject the flowrate disturbance and address the interaction between units. Dual temperatures are applied in the recovery column (C2). The temperatures of two stages are controlled by the ratio of reflux rate to feed (R/F) and reboiler duty in C2, respectively. Pressure-compensated temperature control is applied to compensate temperature in the presence of flowrate disturbance. Dynamic responses show the improved control structure can handle ± 10% feed flowrate and ± 20% composition disturbances.

Flash/distillation for separating 2-pentanone/4-heptanone/water azeotropic mixture based equilibrium data and process design

4-heptanone and 2-Pentanone are promising organic solvents due to their high thermal stability. Due to the lack of binary interaction parameters between 4-heptanone and 2-Pentanone, the vapour-liquid equilibrium experiment is measured for 4-heptanone and 2-Pentanone in this work. The binary interaction parameters are obtained by UNIQUAC model, Wilson model and NRTL model regression. Based on the heterogeneous characteristics of the system, two kinds of steady-state processes are designed. Based on the minimum annual cost, TAC, carbon emissions and thermodynamic efficiency of the corresponding steady-state process were calculated, and for different feeding ways were compared. In addition, the internal cycle dynamic control (basic control structure and dual temperature control structure) are explored. The anti-disturbance performance of the system is detected by introducing 5% the feed disturbance. The result shows that the dual temperature control structure has a good anti-disturbance ability in the face of feed and composition disturbance. The interaction parameters obtained by experimental regression provide a theoretical basis for the design of steady-state process. Flash/distillation technology provides guidance for industrial purification of 2-pentanone/4-heptanone.

Improved Distributed Predictive Functional Control With Basic Function and PID Control Structure

It is known that constraints and disturbances in practice may affect the performance of distributed predictive functional control (DPFC) system greatly, thus it is necessary to develop a method to enhance the control performance further. Based on such background, a modified predictive functional control with basic function and PID control structure (BFPID-DPFC) is proposed for large-scale strong coupling system in this paper. In this approach, the performance index is reconstructed by utilizing the Proportional integral derivative (PID) factor and the weighting coefficient of basis functions firstly, then the ensemble control performance of the strong coupling system can be improved by adjusting the corresponding factors. Further, the coupling effect between subsystems can be eliminated by employing Nash game theory, then the improved DPFC approach is obtained. The comparisons between conventional DPFC method and the BFPID-DPFC approach are done in the simulation part, and the results show that the ensemble control performance of the proposed DPFC algorithm is better.

Controlled microparticle separation using whispering gallery mode forces

Abstract There is a wide variety of applications that require sorting and separation of micro-particles from a large cluster of similar objects. Existing methods can distinguish micro-particles by their bulk properties, such as their size, density, and electric polarizability. These methods, however, are not selective with respect to the individual geometry of the particles. In this work, we focus on the use of a resonance effect between a microparticle and an evanescent light field known as the Whispering Gallery Mode (WGM) force. The WGM force is highly sensitive to the radius of the particle and is both controllable and tunable. In this paper, we explore through simulation the design of a WGM-based device for micro-particle separation. In this device, particles flow in through an inlet and are carried over two actuation regions given by waveguides carrying laser light to generate the evanescent field. Particles are observed by a camera, allowing for feedback control on the power of the lasers. While the basic control structure is simple, there are several challenges, including unknown disturbances to the fluid flow, limited laser power, and uni-directional control over each actuation region. We combine Expectation Maximization with Kalman filtering to both estimate the unknown disturbance and filter the measurements into a position estimate. We then develop simple hybrid controllers and compare them to the ideal setting (without any constraints) based on a Linear-Quadratic-Gaussian (LQG) control approach.

The process control of the triple-column pressure-swing extractive distillation with partial heat integration

Recently, increasing researches have focused on the intensified heat-integrated triple-column pressure-swing extractive distillation (HITPED) owing to its superiority in economic and environmental benefits than the conventional extractive distillation. However, the dynamic controllability investigation for HITPED was lacking, resulting in the difficulties for industrial application. Therefore, separating the ternary azeotropic mixture tetrahydrofuran (THF)-methanol-water by HITPED is taken as an example to fully investigate the dynamic controllability. On the basis of the open-loop analysis a basic control structure CS1 is firstly proposed. To deal with the 20% disturbance in feed composition more effectively, the CS2 with a low selector and composition controllers is then developed. Nevertheless, composition controllers are less applied in chemical industry than temperature controllers owing to the long delay and high cost. As such, two different control structures (CS3 and CS4) without any composition controllers are then put forward. Integral absolute error (IAE) is applied to compare the dynamic performance. Under the 20% disturbances of the feed flowrate and composition, the robust control strategy CS4 with temperature controllers and a high selector exhibits the gratifying dynamic performance. The application of the selector further enlarges the dynamic researches in the distillation process.

Research on the Unit’s Control Strategy of On-line Primary Frequency Modulation

This paper introduces the basic principle and control structure of primary frequency modulation on the thermal power units, at the same time gives the forming method of primary frequency modulation compensation instruction. Combined with the actual power production and aiming at the actual problems of primary frequency modulation, this paper presents the flexible limiting control method of primary frequency modulation instruction and on-line automatic correction method, which can effectively improve the primary frequency modulation performance on the thermal power units and ensure the safe operation of power grid. This scheme provides reference for other units to improve the primary frequency modulation optimization.

Feedforward control method for single‐phase inverters with non‐linear load

Non-linear rectifier loads usually cause heavy distortion in the output voltage of single-phase inverters due to pulsating charging current of the rectifier direct current link capacitor. In this article, a predictive load current-based digital feedforward (PCFF) compensation method is proposed to decrease the inverter voltage distortion associated with the pulsating load current. PCFF control scheme works along with the basic inverter control structure that consists of a repetitive controller and a synchronous reference frame PI controller. The PCFF method further improves the single-phase inverter performance by enhancing its compensation dynamics. The simulation and experimental results show that the proposed control strategy can effectively reduce the total harmonics distortion of the inverter voltage and improve the output voltage quality.

Comparative optimal design and control of two alternative approaches for separating heterogeneous mixtures isopropyl alcohol-isopropyl acetate-water with four azeotropes

The separation of ternary mixtures with homogeneous or heterogeneous multi-azeotropes has received increasing attention. In this work, a systematic design, optimization and control procedure is proposed for extractive distillation of heterogeneous mixtures with four azeotropes. Herein, the ternary system isopropyl alcohol/isopropyl acetate/water is taken as a case study. The suitable entrainer is first obtained through a proposed approach combining the vapor–liquid equilibrium (VLE) curves and infinite dilution activity coefficient ratios. Conceptual design of two alternative separation sequences involving double-column extractive distillation with a pre-concentrator (DEDP) and triple-column extractive distillation (TED) are then investigated based on thermodynamic insights. With the method of sequential iterative optimization and taking total annual cost (TAC) as the objective function, the two processes are optimized. The optimal results indicate that the DEDP process with dimethyl sulfoxide as entrainer can save 6.43% TAC than that of TED. Finally, a new control structure combining reflux flowrate-to-feed (R/F) and adjustable reboiler duty-to-feed (QR/F) with “HiLoSelect” strategy is proposed to better handle three product purities than that of the basic control structure while feed flowrate and composition disturbances are introduced in the studied DEDP process.