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1
Yu, Jian Zhi, and Yong Sheng Chen. "Study on Control Strategy of Train Automatic Speed Control System." Advanced Materials Research 291-294 (July 2011): 2763–66. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2763.
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To improve the efficiency of the automatic control in the process of train operation, reduce state transition caused by the signal interference and guarantee the safe operation of automatic train operation system, a kind of train automatic speed control strategy based on the fuzzy-predictive control algorithm is put forward. According to the dual-redundant control model, the reliability and safety of the train automatic speed control system is analyzed. Using anti-interference design, a series of model simulations have been accomplished. The results show that the system has good reliability.
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Pacheco Quiñones, Daniel, Maria Paterna, and Carlo De Benedictis. "Automatic Electromechanical Perturbator for Postural Control Analysis Based on Model Predictive Control." Applied Sciences 11, no. 9 (April 29, 2021): 4090. http://dx.doi.org/10.3390/app11094090.
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Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain balance. Whereas most of the applications available in the literature and on the market involve shifting and tilting of the base of support, the system presented in this paper is based on the direct application of an impulsive (short-lasting) force by means of an electromechanical device (named automatic perturbator). The control of such stimulation is rather complex since it requires high dynamics and accuracy. Moreover, the occurrence of several non-linearities, mainly related to the human–machine interaction, signals the necessity for robust control in order to achieve the essential repeatability and reliability. A linear electric motor, in combination with Model Predictive Control, was used to develop an automatic perturbator prototype. A test bench, supported by model simulations, was developed to test the architecture of the perturbation device. The performance of the control logic has been optimized by iterative tuning of the controller parameters, and the resulting behavior of the automatic perturbator is presented.
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Hoffmann, Hartmut, Michael F. Zäh, Ingo Faass, Roland Mork, Matthias Golle, Bernd Griesbach, and Matthias Kerschner. "Automatic Process Control in Press Shops." Key Engineering Materials 344 (July 2007): 881–88. http://dx.doi.org/10.4028/www.scientific.net/kem.344.881.
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The manufacturing of automotive body components in press lines is a sensitive process. The quality characteristics of body components vary. These fluctuations are rooted in the fact that the factors influencing the component quality are varying, e.g., fluctuations of batches regarding material quality, abrasion or heating of the tool during the production cycle. If a certain quality characteristic exceeds a predefined range an intervention in the process is necessary. This intervention is based upon the subjective know-how of the machine operator. Objective information about the state of the process, like tool temperature or the material quality of the semi-finished product is not available. Therefore, a lack of knowledge emerges in the interrelations between the tuning parameters of the system press-tool and the component quality during different stages of the process (material quality, temperature…). In this paper a complete concept for an automatic process control in press shops is described. The concept will be realized in a pilot plant for mass production in the press shop of AUDI AG. The mechanisms of occurrence of quality defects are shown in the paper, as well as the essential factors influencing the quality during the mass production of body components in the automotive industry and their variation. A sensor-system for continuous measurement of influencing variables during the mass production is presented. The key element of the concept is the non-destructive identification of material-properties for every single blank. By associating the sensor-data with the respective quality, a knowledge-based process control can be realized. The purpose is to create a failure prediction algorithm and make optimal adjustments for each stroke of the moulding press, respectively. The potential of existing actuators in modern press lines as well as new, tool integrated proposals for actuators are highlighted.
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Baek, Sujeong. "System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems." International Journal of Advanced Manufacturing Technology 113, no. 3-4 (January 29, 2021): 955–66. http://dx.doi.org/10.1007/s00170-021-06652-z.
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AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.
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Zhang, Lihui, Helei Cui, Hongli Li, Feng Han, Yaqiu Zhang, and Wenfu Wu. "Parameters Online Detection and Model Predictive Control during the Grain Drying Process." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/924698.
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In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant) and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.
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Yin, Xiao Hong, Can Yang, and Han Zhao. "Sectionalized Motion Control for Automatic Guided Vehicle." Applied Mechanics and Materials 152-154 (January 2012): 1127–32. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1127.
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In this work, an approach called sectionalized motion control (SMC) was proposed in order to achieve control with high precision and low energy consumption for the entire AGV tracking process. In this method, according to the characteristics of the AGV’s early, middle and terminal motion phases, the neural dynamics-based tracking, energy-efficient tracking, and model predictive technologies were adopted. Furthermore, a simulation using Matlab software was performed in order to verify the proposed approach. The simulation results showed that SMC is capable of providing smooth, energy-efficient, robust and globally stable control for the AGV system.
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Wen, Shuhuan, Jingwei Yang, Ahmad B. Rad, Shengyong Chen, and Pengcheng Hao. "Weighted Multimodel Predictive Function Control for Automatic Train Operation System." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/520627.
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Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.
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Guo, Yan Ping, and Qi Cui. "PLC-based Automatic Control of Food Production Line." Advanced Materials Research 482-484 (February 2012): 2214–17. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2214.
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Standard programmable logic controller (PLC) based proportional-integral-derivative (PID) was originally employed as part of the food production line control strategy, but after observing the response of those process due to measured disturbances during normal operation, it was evident that PID control could not meet the desired cooking specifications. It was decided to pursue advanced process control strategies as a means to meet the food production specifications. The problem of this research study was to design and analyze the performance of a PLC-based model state feedback controller implementation for an industrial food production line, and to determine its viability in comparison to commercially available PC-based model predictive controller implementations applied to food production line.
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Yang, Da Peng, Ping Bo Sun, and Ke Qiang Hua. "Model Predictive Control of Flight Arrival Interval." Advanced Materials Research 503-504 (April 2012): 1375–80. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.1375.
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In order to achieve the automation of Air Traffic Control (ATC), use system to identify the controlled model of flights arrival process which has been already built, using Model Predictive Control (MPC) of the dynamic matrix contro1 (DMC) to control the ATC process. According to DMC algorithm and the features of ATC, the design parameters of this system can be determined by a lot of simulations. It proves that the system design and parameters selection make the system has the required performance and the robustness even if the parameters be changed in a wide range. The experiment on the ATC Simulation System proves that the MPC method is available, conclusion of the study provides a new idea and method for the engineering implementation of the automation of flights arrival process control and some improvement of airspace utilization.
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Lin, Chi Ying, and Yu Sheng Zeng. "Visual Servoing of Automatic Alignment System Using Model Predictive Control." Key Engineering Materials 625 (August 2014): 627–32. http://dx.doi.org/10.4028/www.scientific.net/kem.625.627.
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Over the past few decades, vision based alignment has been accepted as an important technique to achieve higher economic benefits for precision manufacturing and measurement applications. Also referred to as visual servoing, this technique basically applies the vision feedback information and drives the moving parts to the desired target location using some appropriate control laws. Although recently rapid development of advanced image processing algorithms and hardware have made this alignment process an easier task, some fundamental issues including inevitable system constraints and singularities, still remain as a challenging research topic for further investigation. This paper aims to develop a visual servoing method for automatic alignment system using model predictive control (MPC). The reason for using this optimal control for visual servoing design is because of its capability of handling constraints such as motor and image constraints in precision alignment systems. In particular, a microassembly system for peg and hole alignment application is adopted to illustrate the design process. The goal is to perform visual tracking of two image feature points based on a XYθ motor-stage system. From the viewpoint of MPC, this is an optimization problem that minimizes feature errors under given constraints. Therefore, a dynamic model consisting of camera parameters and motion stage dynamics is first derived to build the prediction model and set up the cost function. At each sample step the control command is obtained by solving a quadratic programming optimization problem. Finally, simulation results with comparison to a conventional image based visual servoing method demonstrate the effectiveness and potential use of this method.
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Tambo, Norihito, Yoshihiko Matsui, Ken-ichi Kurotani, Masakazu Kubota, Hirohide Akiyama, Tokio Ohto, Yasushi Zaitsu, and Haruo Itoh. "Control of coagulation process by dual wavelength article analyzer." Water Science and Technology 36, no. 4 (August 1, 1997): 135–42. http://dx.doi.org/10.2166/wst.1997.0104.
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A coagulation process for water purification plants mainly uses feedforward control based on raw water quality and empirical data and requires operator's help. We developed a new floc sensor for measuring floc size in a flush mixer to be used for floc control. A control system using model predictive control was developed on the floc size data. A series of experiments was performed to confirm controllability of settled water quality by controlling flush mixer floc size. An automatic control with feedback from the coagulation process was evaluated as practical and reliable. Finally this new control method was applied for actual plant and evaluated as practical.
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Zhao, Xinxin, and Zhijun Li. "Data-Driven Predictive Control Applied to Gear Shifting for Heavy-Duty Vehicles." Energies 11, no. 8 (August 16, 2018): 2139. http://dx.doi.org/10.3390/en11082139.
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In this paper, the data-driven predictive control method is applied to the clutch speed tracking control for the inertial phase of the shift process. While the clutch speed difference changes according to the predetermined trajectory, the purpose of improving the shift quality is achieved. The data-driven predictive control is implemented by combining the subspace identification with the model predictive control. Firstly, the predictive factors are constructed from the input and output data of the shift process via subspace identification, and then the factors are applied to a prediction equation. Secondly, an optimization function is deduced by taking the tracking error and the increments of inputs into accounts. Finally, the optimal solutions are solved through quadratic programming algorithm in Matlab software, and the future inputs of the system are obtained. The control algorithm is applied to the upshift process of an automatic transmission, the simulation results show that the algorithm is in good performance and satisfies the practical requirements.
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Han, Feng, Chuncheng Zuo, Wenfu Wu, Junxing Li, and Zhe Liu. "Model Predictive Control of the Grain Drying Process." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/584376.
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Drying plays an important role in the postharvesting process of grain. To ensure the quality of the dried grain and improve the intelligent level in drying process, a digital simulation of corn drying machine system based on a virtual instrument was established for 5HSZ dryer, automatically control the air temperature, and predict the discharging speed of grain and so forth. Finally, an online measurement and automated control software of grain parameters were developed to provide the changes of moisture, temperature, humidity, and germination rate in the process of drying. The study carried out in the actual processing showed that it can meet the requirements of the actual drying operation, effectively control the stability of the grain moisture, and keep the dry food quality.
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Ye, Hao, Haobin Jiang, Shidian Ma, Bin Tang, and Lukuman Wahab. "Linear model predictive control of automatic parking path tracking with soft constraints." International Journal of Advanced Robotic Systems 16, no. 3 (May 1, 2019): 172988141985220. http://dx.doi.org/10.1177/1729881419852201.
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This study examines how to improve the accuracy of auto parking path tracking control; therefore, a linear model predictive control with softening constraints path tracking control strategy is proposed. Firstly, a linear time-varying predictive model of vehicle is established, and the future state of the vehicle can be predicted. The designed objective function fully considers the deviation between the predictor variable and the reference variable. Also, the relaxation factors are added to the optimization process, and the control increment of each cycle is calculated by the quadratic programming. Through rolling optimization and feedback correction, all kinds of deviations in the control process can be corrected in time. Then, the Simulink/CarSim simulation is carried out jointly. Furthermore, the path tracking simulation based on proportion–integration–differentiation control and no control is also carried out to compare with the model predictive control. Finally, a real vehicle test is carried out for model predictive control algorithm, and a comparative experiment based on proportion–integration–differentiation control and no control is carried out.
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Sharma, Sachin, Vineet Kumar, and K. P. S. Rana. "Automatic oscillations detection and quantification in process control loops using linear predictive coding." Engineering Science and Technology, an International Journal 23, no. 1 (February 2020): 123–43. http://dx.doi.org/10.1016/j.jestch.2019.04.006.
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Tiwari*, Vipul Kumar, Abhishek Choudhary, Umesh Kr Singh, Anil Kumar Kothari, and Manish Kr Singh. "Real-Time Lime Quality Control through Process Automation." International Journal of Emerging Science and Engineering 7, no. 2 (May 30, 2021): 1–10. http://dx.doi.org/10.35940/ijese.b2502.057221.
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In the steel industry - Tata steel, India, most of the lime produced in the lime plant is used in the steel-making process at LD shops. The quality of steel produced at LD shops depends on the quality of lime used. Moreover, the lime also helps in the crucial dephosphorization process during steel-making. The calcined lime produced in the lime plant goes to the laboratory for testing its final quality (CaO%), which is very difficult to control. To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random forest and extreme gradient boosting have been developed using different algorithms. Python has been used as a tool to integrate the algorithms in the models. Each model has been trained on the past 14 months’ data of process parameters, collected from level 1 sensor devices, to predict the future quality of lime. To boost the model’s prediction performance, hyper-parameter tuning has been performed using grid-search algorithm. A comparative study has been done among all the models to select a final model with the least root mean square error in predicting and control future lime quality. After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. In addition to this, a self-learning approach has also been incorporated into support vector machine model to enhance its performance further in real time. The result shows that the performance has been boosted from 85% strike-rate in +/-2 error range to 90% of strike-rate in +/-1 error range in real-time. Further, the above predictive model has been extended to build a control model which gives prescriptions as output to control the future quality of lime. For this purpose, a golden batch of good data has been fetched which has shown the best quality of lime (≥ 94% of CaO%). A good range of process parameters has been extracted in the form of upper control limit and lower control limit to tune the set-points and to give the prescriptions to the user. The integration of these two models (Predictive model and control model) helps in controlling the quality of lime 12 hours before its final production of lime in lime plant. Results show that both models (Predictive model and control model) have 90% of strike-rate within +/-1 of error in real-time. Finally, a human machine interface has been developed to facilitate the user to take action based on control model’s output. Eventually this work is deployed as a lime making process automation to predict and control the lime quality.
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Flor Unda, Omar. "Adaptive control systems for solar collectors." Athenea 2, no. 4 (June 15, 2021): 19–25. http://dx.doi.org/10.47460/athenea.v2i4.18.
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En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX.
Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel.
References
[1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlinear neural model-based predictive control of a solar plant. In Proc. European Control Conf. ECC'97. Brussels, Belgium, Volumen TH-E I2, p. paper 264.
[2]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998a. Comparison of RBF algorithms for output temperature prediction of a solar plant.. In Proc. CONTROLO'98, 9-11 September.
[3]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998b. Neural identification applied to predictive control of solar plant. Control Engineering Practice, Volumen 6, pp. pp. 333-344.
[4]Aström, K. J. & Wittenmark, B., 1989. Adaptative Control. Aström, K. J. & Wittermark, B., 1984. Computed controlles Systems, Theory and Design. Englewood Cliffs, NJ: Prentice Hall.
[5]Barão, M., 2000. Dynamic and no-linear control of a solar collector field. Thesis (in Portuguese). Universidade Técnica de Lisboa, Instituto Superior Técnico.
[6]Barão, M., Lemos, J. M. & Silva, R. N., 2002. Reduced complexity adaptative nonlinear control of a distribuited collector solar field. J. of Process Control, Volumen 12(1), pp. pp. 131-141.
[7]Berenguel, M., Arahal, M. R. & Camacho, E. F., 1998. Modeling free responses of a solar plant for predictive control. Control Engineering Practice, Volumen 6, pp. pp. 1257-1266.
[8]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1994. Simulation software package for the Acurex field.. Departamento de Ingeniería y Automática.
[9]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1997. Advanced Control of Solar Plants. Londres: Springer-Verlag.
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Wang, Jin Hui, Jian Yi Kong, Jin Tang Yang, Gong Fa Li, and Yao Li. "Multi-Variable Model Predictive Control of Automatic Variable-Load in Air Separation Unit Production Process." Applied Mechanics and Materials 29-32 (August 2010): 2356–59. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2356.
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The process of air separation unit has the characteristics of many types of equipment, long flow, and strong relativity and complicates structures. A multi-variable model predictive control strategy to achieve the goal of automatic variable-load for the control limits in the variable-load process of air separation unit was proposed. Regard model predictive controller as the core, the problems of coupling in the various technological parameters were solved. Moreover, the non-linear control request for global variable-loading process was satisfied, and the perfect operation character and rate were ensured. Production characteristics, structure, scheme and application processes for the control strategy were described in detail. The system succeeds in applying to the air separation unit in an iron and steel enterprise, the multi-variable control system shows its effectiveness and efficiency in the automatic variable-loading.
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Подвальный, С. Л., А. П. Попов, С. Г. Тихомиров, and О. Г. Неизвестный. "CONTROL SYSTEM SYNTHESIS FOR THE ETHYLBENZENE DEHYDROGENATION PROCESS BASED ON THE PREDICTIVE FUNCTIONAL CONTROL METHOD." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 6 (January 10, 2021): 7–14. http://dx.doi.org/10.36622/vstu.2020.16.6.001.
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Описывается применение метода предиктивного управления при синтезе автоматизированной системы программного управления производством стирола. На основе результатов системного анализа объекта управления обоснован способ управления технологическим процессом, проведены обзор и оценка возможности использования различных видов регулирующих устройств. В рамках концепции модельно-ориентированного проектирования синтезирована автоматизированная система, реализующая предсказательное функциональное управление (Predictive functional control (PFC)) концентрацией стирола на выходе из первой ступени реакторного блока. Выполнен структурный синтез и разработан алгоритм функционирования управляющей системы. В качестве структурного элемента, выполняющего построение прогнозной траектории изменения управляющего и управляемого параметров, использован комплекс динамических моделей, описывающих теплообменные процессы и физико-химические превращения, протекающие в исследуемой системе. Расчет оптимального значения управляющего воздействия производился с учетом инерционности технологического оборудования, параметров состояния каталитического слоя реактора, энергетических потоков и компонентов реакционной среды в соответствии со сформированным комплексным интегральным критерием энергоэффективности процесса при одновременном обеспечении максимальной производительности. Приведен сравнительный анализ результатов расчета переходных процессов по основному каналу системы управления, полученных при использовании регулятора ПИД-структуры и управляющего устройства, реализующего метод PFC-управления The article describes the application of the predictive control method in the synthesis of an automated system for software control of styrene production. Based on the results of the object system analysis, the method of process control is justified, the review and evaluation of the possibility of using various types of control devices is carried out. Within the framework of the model-oriented design concept, an automated system is synthesized that implements predictive functional control (PFC) of the concentration of styrene at the outlet of the reactor unit first stage. A structural synthesis was performed, and the control system operation algorithm was developed. A set of dynamic models describing heat exchange processes and physical and chemical transformations occurring in the system under study is used as a structural element that performs the construction of a predictive trajectory of changes in the controlling and controlled parameters. The calculation of the optimal value of the control action was carried out taking into account the inertia of the process equipment, the parameters of the state of the catalytic layer of the reactor, energy flows and components of the reaction medium in accordance with the formed complex integral criterion of the energy efficiency of the process while ensuring maximum productivity. A comparative analysis of the results of calculating transient processes for the main channel of the control system, obtained using a PID structure controller and a control device that implements the PFC control method
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TOLU, SILVIA, MAURICIO VANEGAS, JESÚS A. GARRIDO, NICETO R. LUQUE, and EDUARDO ROS. "ADAPTIVE AND PREDICTIVE CONTROL OF A SIMULATED ROBOT ARM." International Journal of Neural Systems 23, no. 03 (April 29, 2013): 1350010. http://dx.doi.org/10.1142/s012906571350010x.
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In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).
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Barbasova, T. A. "A MULTILEVEL RESOURCE-SAVING BLAST FURNACE PROCESS CONTROL." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 21, no. 1 (February 2021): 136–46. http://dx.doi.org/10.14529/ctcr210112.
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A multilevel resource-saving blast furnace process control is considered. The resource-saving control is provided for operating, adaptation, technical and economic control in the automated systems of blast-furnace processes. It is proposed to form optimal operation modes of blast furnace heating, metal charge structures, natural gas and oxygen consumption. Decisions are made using Kohonen neural networks taking into account current and planned parameters of coke quality, iron ore, raw materials and blast. At the level of operating control, the work suggests a model predictive control to improve the resource conservation indicators. The method is based on decomposition of the general problem of the process dynamics identification on particular problems: dynamic synchronization and identification of process transfer functions. At the level of adaptive control, optimal operating modes of blast furnaces are expedient to be developed with respect to blast furnace heating, structure of metal charge, natural gas and oxygen rate considering the current and planned parameters of coke, blasting. The blast furnace operating modes are suggested to be determined based on Kohonen neural networks. In evaluating the efficiency of introducing the model predictive control, the existing actual statistics of scatter of BF mode parameters should be based upon. The fact is that the introduction of model predictive control assumes no radical change of the BF melt technology. Like in all the control systems, the BF process is considered as the set control object with all its characteristics. Changing process settings, raw material content does not introduce any cardinal variation in the scatter of process characteristics. However, in this case a transient process occurs which is necessary for the control system to identify the changing conditions. The transient process is inherent to all the control systems and the blast furnace process is not an exclusion. As a result of transient process, the control system is set to the optimal mode.
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Durand, Helen. "Responsive Economic Model Predictive Control for Next-Generation Manufacturing." Mathematics 8, no. 2 (February 16, 2020): 259. http://dx.doi.org/10.3390/math8020259.
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There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types would advance the move toward artificial intelligence properties for this controller beyond those which it has today and would provide new thoughts on interpretability and verification for the controller. This work provides theoretical studies which relate nonlinear systems considerations for EMPC to these higher-level concepts using two ideas for EMPC formulations motivated by specific situations related to self-modification of a control design after human perceptions of the process response are received and to controller handling of anomalies.
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Yang, Yingze, Zheng Xu, Weirong Liu, Heng Li, Rui Zhang, and Zhiwu Huang. "Optimal Operation of High-Speed Trains Using Hybrid Model Predictive Control." Journal of Advanced Transportation 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/7308058.
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The high-speed train operation process is highly nonlinear and has multiple constraints and objectives, which lead to a requirement for the automatic train operation (ATO) system. In this paper, a hybrid model predictive control (MPC) framework is proposed for the controller design of the ATO system. Firstly, a piecewise linear system with state and input constraints is constructed through piecewise linearization of the high-speed train’s nonlinear dynamics. Secondly, the piecewise linear system is transformed into a mixed logical dynamical (MLD) system by introducing the auxiliary binary variables. For the transformed MLD system, a hybrid MPC controller is designed to realize the precise control under hard constraints. To reduce the online computation complexity, the explicit control law is computed offline by employing the mixed-integer linear programming (MILP) technique. Simulation results validate the effectiveness of the proposed method.
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Xu, Jun, and Wei Li. "The Nonlinear Time-Varying Response of Dynamic Thermal Tensioning for Welding-Induced Distortion Control." Journal of Manufacturing Science and Engineering 129, no. 2 (October 6, 2006): 333–41. http://dx.doi.org/10.1115/1.2540708.
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This paper presents a dynamic thermal tensioning method to control the welding induced distortion under production variation. The new method determines the optimal thermal tensioning parameters based on real-time distortion measurements. The paper is focused on a systematic study on the structural response and the development of an automatic control algorithm for the dynamic thermal tensioning process. A thermomechanical finite element model was used to study preheating effects in a gas metal arc welding process. A model predictive strategy was adopted for automatic distortion control. It has been found that the response of the dynamic thermal tensioning process is nonlinear and time varying. However, the response model can be linearized based on the superposition principle. A threshold-based control algorithm is developed and demonstrated using both simulation and experimental results.
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Ouyang, Mingsan, and Yunlong Wang. "Optimization and Realization of the Continuous Reactor with Improved Automatic Disturbance Rejection Control." Complexity 2020 (November 22, 2020): 1–14. http://dx.doi.org/10.1155/2020/4519428.
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In the chemical production process, the temperature of the continuous reactor has nonlinear characteristics such as large inertia. An improved autodisturbance control method is proposed. By improving the tracking differentiator with adjustable parameters, the expanded state observer and the control structure obtained an improved automatic disturbance rejection control model and realized the optimal control of the nonlinear and large-delay systems. On the process control training system, the experiment of the continuous system process flow is compared with the anti-interference of the Smith predictive compensation system, which uses an optimal set of data. The reactor temperature fluctuates, but the self-resistance is improved. The optimal overshoot of the disturbance is close to 0. Compared with the original antidisturbance controller, the antidisturbance time is not affected by the number of iterations, which reduces the high requirements on the accuracy of the parameters. Therefore, the improved automatic disturbance rejection control improves the operation and enhances the ability to resist external disturbance that changes the controlled object.
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Wenzelburger, Philipp, and Frank Allgöwer. "Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0." Applied Sciences 11, no. 17 (September 2, 2021): 8145. http://dx.doi.org/10.3390/app11178145.
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In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem.
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Sieberg, Philipp Maximilian, and Dieter Schramm. "Central Non-Linear Model-Based Predictive Vehicle Dynamics Control." Applied Sciences 11, no. 10 (May 20, 2021): 4687. http://dx.doi.org/10.3390/app11104687.
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Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953×10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way.
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Ma, Wen Bin, Kai Zhang, Jian Hua Liu, and Yan Nian Rui. "Based on Nash Optimization Decoupling Control System for Special Shape Pipe Automatic Welding Machine." Advanced Materials Research 383-390 (November 2011): 2832–37. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2832.
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A new decoupling control structure is presented for complex special shape pipe welding process, which consists of model construction by reorganization method. Using this structure, an decoupling control system is established which realizes an adaptive decoupling control strategy for the welding processes with integrated complexities by using structure of decoupling control system and based on multivariable predictive function and nash optimization to making sure the right coefficient. Such system can be easily implemented on the welding peocess and simulation show this approach is effective.
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Pawar, Sushant N., Rajan H. Chile, and Balasaheb M. Patre. "Predictive extended state observer-based robust control for uncertain linear systems with experimental validation." Transactions of the Institute of Measurement and Control 43, no. 12 (April 6, 2021): 2615–27. http://dx.doi.org/10.1177/01423312211002599.
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This paper describes a predictive extended state observer-based robust control for uncertain process control applications. The technique discussed in the article uses the extended state observer (ESO) that can estimate the dynamics of the system as well as total disturbance encountered in the system. The disturbances, parametric uncertainties associated with the processes are treated as an extended state variable to be estimated in real-time using ESO. With the implementation of a predictive algorithm with an ESO, the proposed control structure extends its applicability to time-delayed higher-order processes. The proposed control technique utilizes the simple first-order modified predictive ESO even in the case of higher-order processes. The novel predictive ESO is able to obtain a delay less estimation of total disturbance as compared with existing normal ESO. Also, novel predictive ESO maintains its stability margin in presence of time delay as well provides better response as compared with normal ESO. Numerical simulations show that the proposed scheme provides a significant improvement in transient response as compared with internal model control-based proportional-integral-derivative (IMC-PID) control. The proposed scheme requires less knowledge of the process as compared with the IMC-PID structure. The implementation of the proposed control is tested on a real-life single tank level control system. Because of its merit, the suggested technique can be used as automatic for online tuning, as it is less reliant on the process model.
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Yu, Ji Lai, Ting Quan Gu, and Jian Guo Wang. "An Automatic Control Strategy of Strip Width in Cold Rolling." Advanced Materials Research 1004-1005 (August 2014): 1221–25. http://dx.doi.org/10.4028/www.scientific.net/amr.1004-1005.1221.
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This paper proposes an automatic control strategy of the strip width in cold rolling process. The prediction model for the exit strip width of each stand is established. In addition to the strip width variation of the incoming hot-rolled material between the measured and the pre-set values, the variations of the rolling force, tension, bending force and the roll shift amount of each stand are utilized to model and forecast the stand exit width deformation. Then, the model can be used to determine the compensation values of the different profile adjustment mechanism and the exit strip width can be controlled in high accuracy. In this way, the effect of the strip width variation of the incoming hot-rolled material and the fluctuation of the stand rolling process parameters on the exit strip width can be eliminated. Moreover, the feed-forward control of the strip width can reduce the incidence of abnormal situation, such as the strip breakage and off-tracking ,and improve the stability of the cold rolling process.
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Lukina, Anna. "Adaptive Optimization Framework for Control of Multi-Agent Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9991–92. http://dx.doi.org/10.1609/aaai.v33i01.33019991.
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The main focus of this work is an optimization-based framework for control of multi-agent systems that synthesizes actions steering a given system towards a specified state. The primary motivation for the research presented is a fascination with birds, which save energy on long-distance flights via forming a V-shape. We ask the following question: Are V-formations a result of solving an optimization problem and can this concept be utilized in multi-agent systems, particularly in drones swarms, to increase their safety and resilience? We demonstrate that our framework can be applied to any system modeled as a controllable Markov decision process with a cost (reward) function. A key feature of the procedure we propose is its automatic adaptation to the performance of optimization towards a given global objective. Combining model-predictive control and ideas from sequential Monte-Carlo methods, we introduce a performance-based adaptive horizon and implicitly build a Lyapunov function that guarantees convergence. We use statistical model-checking to verify the algorithm and assess its reliability.
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Cariapa, V., R. J. Stango, L. Chen, and R. Hermann. "Aspects of Process Model for Automatic Control of Edge-Deburring with Filamentary Brush." Journal of Engineering for Industry 114, no. 3 (August 1, 1992): 294–300. http://dx.doi.org/10.1115/1.2899795.
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In this paper, process models are reported for a dynamic system consisting of a polymer/mineral abrasive filamentary brush during an orthogonal deburring operation. Dynamic process models are generated by employing both a viscoelastic representation and Dynamic Data Systems approach for predicting brush response under random penetration depth operating conditions. A process model for material removal is obtained by the use of a multiple regression method in conjunction with experimental results for burr material removal under various brushing conditions. Time constants for both the viscoelastic dynamic system and material removal characteristics of brushes are reported and discussed. An examination of system transfer functions indicates that the system is stable. In addition, generally good agreement is obtained when comparison is made between the process model for burr removal and actual measurement of machined burrs.
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García Plaza, E., P. Núñez López, and E. Beamud González. "Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems." Sensors 18, no. 12 (December 11, 2018): 4381. http://dx.doi.org/10.3390/s18124381.
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Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.
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Wang, Zhen Chao. "The Design Process of Defect Detection Method for Large Industrial Automatic Control Software." Applied Mechanics and Materials 556-562 (May 2014): 2882–85. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2882.
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The current difference trend of automatic control software structure is more obvious, software integration makes linking correlation of overall structure appeared to play down. No significant linking characteristics exist between structural ports. The traditional detection methods of software defect are utilized to locate software defect under the differentiation structural framework, due to the lack of clear articulation feature to indicate the location region, resulting in unidentified area which software defect signal belong to and position deviation. In this paper, software defect detection method was proposed on the basis of difference structure fusion algorithm. The signal fusion technology was applied to mix the signal of software defect detection effectively, and chaos particle swarm algorithm optimization was employed to support vector machine parameters, establish the optimal models of software defect prediction, thus completing the software defect detection. Experimental results show that the algorithm for software defect detection, can greatly improve the accuracy of detection.
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Rathore, Anurag S., Somesh Mishra, Saxena Nikita, and Priyanka Priyanka. "Bioprocess Control: Current Progress and Future Perspectives." Life 11, no. 6 (June 13, 2021): 557. http://dx.doi.org/10.3390/life11060557.
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Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.
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Stankov, Stanko. "Modern control of mineral wool production process." Chemical Industry 67, no. 2 (2013): 375–84. http://dx.doi.org/10.2298/hemind120428069s.
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In this paper, the control of the plant for mineral wool production consisting of a number of the technological units of different sizes and complexity is considered. The application of modern equipment based on PLC (Programmable Logic Controller) and SCADA (Supervisory Control And Data Acquisition) configuration provides optimal control of technological process. Described supervisory and control system is consisting of a number of units doing decentralized distributed control of technological entities where all possible situation are considered during work of machines and devices, which are installed in electric drive and are protected from technological and electrical accident. Transformer station and diesel engine, raw materials transport and dosage, processes in dome oven, centrifuges, polycondensation (PC) chamber, burners, compressor station, binder preparation and dosage, wool cutting, completed panel packing and their transport to storehouse are controlled. Process variables and parameters like as level, flow, velocity, temperature, pressure, etc. are controlled. Control system is doing identification of process states changes, diagnostic and prediction of errors and provides prediction of behavior of control objects when input flows of materials and generates optimal values of control variables due to decreasing downtime and technic - economical requires connected to wool quality to be achieved. Supervisory and control system either eliminates unwanted changes in the production line or restricts them within the allowable limits according to the technology. In this way, the optimization of energy and raw materials consumption and appropriate products quality is achieved, where requirements are satisfied in accordance with process safety and environmental standards. SCADA provides a visual representation of controlled and uncontrolled parts of the technological process, processing alarms and events, monitoring of the changes of relevant variables, data archiving, choice of either automatic or manual control. Supervison of the production process by intuitive screens on monitors, and display terminal is allowed to the operator. Certain objects of plant are represented by the appropriate symbols whose color is changed depending on status (working, inactive status, stand by, failure, etc.). Symbols are associated with characteristic parameters and the variables whose current values are monitored by graphics and/or tables. Special attention is paid to alarm conditions occurring in the case of failure or exceeding the set limits of certain values. Alarm signal is manifested by sound and signal light, with a corresponding text message on the screen, with the date and time of occurrence and the request to the operator to confirm the alarm in some way. Alarms and other important data are stored in the database, including information about the time of removing the causes of the alarm emergence. Beside data acqusition and processing data of process variables and installed equipment state, the system also provides data archiving and generating and printing of daily, weekly and monthly reports. Based on these reports, analysis and appropriate corrections are being done to increase productivity. Specific data of drive functionality, raw materials and amounts of final products are available to management at any moment via Internet. Information about status of embedded equipment, devices and protective elements provide planning periodical reviews and remont some lines or whole plant. Then replacement of certain parts significantly contributes to efficienty of current, preventive and investment maintenance. In this way, reliability and safety of production plant are increased. With complete automatization of production process that is complex and multidisciplinary, demages and energy consumption are considerably decreasing, workforce is optimized, good quality of final product is achieved and plant work with full capacity is provided. Some technical solutions (diesel engine and transformer station supervisory, compressor station supervisory, electric drive regulation, dedusting system control, HVAC (heating, ventilation, and air conditioning) system control, transport and dosage system control) applied at realization of supervisory and control system for mineral wool production process, can be also applied to regulation large number of industrial processes.
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Nasimi, Elnara, and Hossam A. Gabbar. "Analysis of Liquid Zone Control Valve Oscillation Problem in CANDU Reactors." Journal of Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/450161.
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This paper looks at the existing challenges with steady-state Liquid Zone control at some CANDU (CANada Deuterium Uranium) stations, where—contrary to expectations for equilibrium flow—Liquid Zone Control Valve oscillations have proven to be a chronic, unanticipated challenge. Currently, the exact causes of this behaviour are not fully understood, although it is confirmed that the Control Valve oscillations are not due to automatic power adjustment requests or zone level changes due to process leaks. This phenomenon was analysed based on a case study of one domestic nuclear power station to determine whether it could be attributed to inherent controller properties. Next, a proposal is made in an attempt to improve current performance with minimal changes to the existing system hardware and logic using conventional technologies. Finally, a proposal was made to consider Model Predictive Control-based technology to minimize the undesirable Control Valve oscillations at steady state based on the obtained simulation results and discussion of other available alternatives.
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Wright, Marc-Oliver, Eli N. Perencevich, Christopher Novak, Joan N. Hebden, Harold C. Standiford, and Anthony D. Harris. "Preliminary Assessment of an Automated Surveillance System for Infection Control." Infection Control & Hospital Epidemiology 25, no. 4 (April 2004): 325–32. http://dx.doi.org/10.1086/502400.
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AbstractBackground and Objective:Rapid identification and investigation of potential outbreaks is key to limiting transmission in the healthcare setting. Manual review of laboratory results remains a cumbersome, time-consuming task for infection control practitioners (ICPs). Computer-automated techniques have shown promise for improving the efficiency and accuracy of surveillance. We examined the use of automated control charts, provided by an automated surveillance system, for detection of potential outbreaks.Setting:A 656-bed academic medical center.Methods:We retrospectively reviewed 13 months (November 2001 through November 2002) of laboratory-patient data, comparing an automated surveillance application with standard infection control practices. We evaluated positive predictive value, sensitivity, and time required to investigate the alerts. An ICP created 75 control charts. A standardized case investigation form was developed to evaluate each alert for the likelihood of nosocomial transmission based on temporal and spatial overlap and culture results.Results:The 75 control charts were created in 75 minutes and 18 alerts fired above the 3-sigma level. These were independently reviewed by an ICP and associate hospital epidemiologist. The review process required an average of 20 minutes per alert and the kappa score between the reviewers was 0.82. Eleven of the 18 alerts were determined to be potential outbreaks, yielding a positive predictive value of 0.61. Routine surveillance identified 5 of these 11 alerts during this time period.Conclusion:Automated surveillance with user-definable control charts for cluster identification was more sensitive than routine methods and is capable of operating with high specificity and positive predictive value in a time-efficient manner.
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Pauli, Victoria, Peter Kleinebudde, and Markus Krumme. "Predictive Model-Based Process Start-Up in Pharmaceutical Continuous Granulation and Drying." Pharmaceutics 12, no. 1 (January 15, 2020): 67. http://dx.doi.org/10.3390/pharmaceutics12010067.
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Continuous manufacturing (CM) is a promising strategy to achieve various benefits in the context of quality, flexibility, safety and cost in pharmaceutical production. One of the main technical challenges of CM is that the process needs to handle transient conditions such as the start-up phase before state of control operation is reached, which can potentially cause out-of-specification (OOS) material. In this context, the presented paper aims to demonstrate that suitable process control strategies during start-up of a continuous granulation and drying operation can limit or even avoid OOS material production and hence can ensure that the provided benefits of CM are not compromised by poor production yields. In detail, heat-up of the drying chamber prior the start of production can lead to thermal energy being stored inside of the stainless-steel housing, acting as an energy buffer that is known to cause over-dried granules in the first few minutes of the drying process. To compensate this issue, an automatic ramping procedure of dryer rotation speed (and hence drying time) was introduced into the plant’s process control system, which counteracts the excessive drying capacity during start-up. As a result, dry granules exiting the dryer complied with the targeted intermediate critical quality attribute loss-on-drying (LOD) from the very beginning of production.
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Rzucidło, Paweł, and Peter Chudy. "Analysis of Interactions between Pilot-Operator and Advanced Flight Control System." Solid State Phenomena 180 (November 2011): 101–8. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.101.
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This work discusses the application of techniques serving the purpose of prediction and detection of unfavorable man-machine interactions. Prediction criteria of Pilot Induced Oscillations (PIO) were applied during design process of experimental multi-modal fly-by-wire control system for small light aircraft. Estimated PIO susceptibility was further verified during real-time simulations and flight tests. The verification process included subjective pilot/operator expert opinions and the results obtained from the automatic detectors, used for the PIO identification in long sets of recorded data. The general idea of the detection algorithms is based on the Fast Fourier Transform (FFT). These are being presented in this paper in a form of detection applications along with the results of the flight experiments.
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Cho, B. C., S. L. Liaw, C. N. Chang, R. F. Yu, S. J. Yang, and B. R. Chiou. "Development of a real-time control strategy with artificial neural network for automatic control of a continuous-flow sequencing batch reactor." Water Science and Technology 44, no. 1 (July 1, 2001): 95–104. http://dx.doi.org/10.2166/wst.2001.0023.
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The purpose of this study is to develop a reliable and effective real-time control strategy by integrating artificial neural network (ANN) process models to perform automatic operation of a dynamic continuous-flow SBR system. The ANN process models, including ORP/pH simulation models and water quality ([NH4+-N] and [NOx--N]) prediction models, can assist in real-time searching the ORP and pH control points and evaluating the operation performances of aerobic nitrification and anoxic denitrification operation phases. Since the major biological nitrogen removal mechanisms were controlled at nitritification (NH4+-N→NO2--N) and denitritification (NO2--N→N2) stages, as well as the phosphorus uptake and release could be completely controlled during aerobic and anoxic operation phases, the system operation performances under this ANN real-time control system revealed that both the aeration time and overall hydraulic retention time could be shortened to about 1.9-2.5 and 4.8-6.2 hrs/cycle respectively. The removal efficiencies of COD, ammonia nitrogen, total nitrogen, and phosphate were 98%, 98%, 97%, and 84% respectively, which were more effective and efficient than under conventional fixed-time control approach.
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Ramamurthi, K., and C. L. Hough. "Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations." Journal of Engineering for Industry 115, no. 3 (August 1, 1993): 268–77. http://dx.doi.org/10.1115/1.2901660.
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Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.
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Zhou, Ming, Xian Yi Meng, Yue Jin Wang, Zhen Li Gao, Yun Ping Dou, Bo Yan Tang, and Mei Xia Yuan. "Structural Analysis and Building an EDM Process Model." Advanced Materials Research 97-101 (March 2010): 4154–59. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.4154.
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The insufficient information about the mechanism of EDM process affects decision-making related to operation, parameter adjustment, and automatic control. Without a thorough understanding of the process dynamical features, it is difficult to determine a suitable model to describe such processes. This research employs the chaotic analytical techniques to determine the level of complexity of an EDM process; the correlation dimension method. With the understanding of the process complexity and its composition by correlation dimension analysis, a well-defined model is proposed. Finally, by using this model structure and size, an online time-varied predictive model was developed and verified by another experiment.
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Zhou, Yong Hua, Yang Peng Wang, Pin Wu, and Peng Wang. "Real-Time Optimal Speed Coordination and Scheduling for High-Speed Trains Based on Model Predictive Control." Advanced Materials Research 433-440 (January 2012): 6043–48. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6043.
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In the high-speed train control system, the command information such as allowable running distance, time and speed can be sent by the global system for mobile communications for railways (GSM-R). This paper will propose the framework of real-time train scheduling and control based on model predictive control for the optimal speed set-points of high-speed trains. The rolling optimization process combines the genetic algorithm with the simulation of train operation to evaluate the performance of speed set-points, which can be easily implemented in the parallel computing environment for real-time processing. The conflict resolution at the crossing stations is modeled by and embedded in the combination of various speed set-points which are formed from virtual to simulation speed. The final actual speed of train is engendered based on the movement authority and running time through the system of automatic train protection (ATP). The simulation results demonstrate the favorable performance of proposed method.
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Liu, Ming, Bo Leng, Lu Xiong, Yize Yu, and Xing Yang. "Segment Drift Control with a Supervision Mechanism for Autonomous Vehicles." Actuators 10, no. 9 (September 1, 2021): 219. http://dx.doi.org/10.3390/act10090219.
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Stable maneuverability is extremely important for the overall safety and robustness of autonomous vehicles under extreme conditions, and automated drift is able to ensure the widest possible range of maneuverability. However, due to the strong nonlinearity and fast vehicle dynamics occurring during the drift process, drift control is challenging. In view of the drift parking scenario, this paper proposes a segmented drift parking method to improve the handling ability of vehicles under extreme conditions. The whole process is divided into two parts: the location approach part and the drift part. The model predictive control (MPC) method was used in the approach to achieve consistency between the actual state and the expected state. For drift, the open-loop control law was designed on the basis of drift trajectories obtained by professional drivers. The drift monitoring strategy aims to monitor the whole drift process and improve the success rate of the drift. A simulation and an actual vehicle test platform were built, and the test results show that the proposed algorithm can be used to achieve accurate vehicle drift to the parking position.
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Mrugalska, Beata, and Ralf Stetter. "Health-Aware Model-Predictive Control of a Cooperative AGV-Based Production System." Sensors 19, no. 3 (January 27, 2019): 532. http://dx.doi.org/10.3390/s19030532.
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In the paper, a new scheduling strategy for assembly systems consisting of cooperating Automated Guided Vehicles (AGVs) based on their remaining operational time is developed. The operational time is associated with state of charge and state of health of the AGV battery. While the latter is defined as a possible number of repetitions of a set of given tasks, both are impossible to measure on-line directly with conventional sensors. Therefore, a novel state-of-charge estimator is proposed, which uses battery current and voltage sensor readings. In contrast to the approaches presented in the literature, a comprehensive analysis of its convergence is provided. Subsequently, a state-of-health predictor is developed. With the above measures, a new control strategy for cooperative AGVs is proposed. It is achieved by the allocation of alternative tasks among two cooperating robots referring to the state of the accomplished tasks from the previous stage of the assembly process. The proposed method allows a predictive control of assembly processes with several constraints, e.g., productivity of each assembly station, speed of the communication, or operation capability of the robots involved in the assembling process. The final part of the paper shows an experimental study exhibiting the performance of the proposed approach.
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Kolodin, Aleksey A., and Viktor V. Elshin. "Research and development of the controller based on the model predictive control." Vestnik of Samara State Technical University. Technical Sciences Series 29, no. 1 (April 23, 2021): 36–45. http://dx.doi.org/10.14498//tech.2021.1.3.
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Modern automated process control systems that use programmable logic controllers use software controllers based on the proportional integral-differential control law, the PID controller. In most cases, this regulator is sufficient for conducting the technological process. It has high performance with configurable and sufficient quality of regulation. However, using a PID controller for non-linear, poorly defined, multi-connected objects with a long delay time can lead to unstable control quality indicators, accumulation of errors, and ultimately to a deterioration in product quality. One of the most promising methods of control is Model Predictive Control - MPC. The method base on predictive models of control objects. The quality of the controller's control depends on how well the system dynamics described by the model used to design the controller. In most cases, MPC-based control use to handle optimal control problems on the Manufacturing Execution System-MES. However, thanks to the development of microprocessors and increased CPU performance, it becomes possible to apply the principles of control with a predictive model at a lower level, and perform real-time operational control in optimal modes. The work presents the algorithm of MPC controller. The control object is a SISO object with a nonlinear characteristic and a long transition process. Studies of the developed MPC regulator showed that the quality of regulation, compared to the PID regulator, increased by more than 20%, the time to get to set point decreased, and there was almost no over-regulation. The most effective application of the MPC controller is seen in processes with long transitions and with a significant delay time.
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Stankov, Stanko. "A supervisory control system of a biodiesel production plant." Chemical Industry 72, no. 6 (2018): 315–28. http://dx.doi.org/10.2298/hemind180726024s.
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This paper describes a system for control and monitoring of plants for production of ecologically pure fuel biodiesel, which is obtained by transforming vegetable oils into fatty acid methyl esters by the reaction of transesterification. The automation provides synchronized operation of installed devices and equipment, protection and blockage of devices in the cases of irregularities, optimum energy consumption, elimination of subjective factor influences, increased efficiency of the plant and achievement of required product quality. It also enables prediction and diagnosis of faults, prevention of the occurrence of anomalies and ensures quality system maintenance and continual production. The supervisory control system is based on PLC and SCADA configuration with decentralized and distributed control of the component parts of the plant. All technical units are monitored (transformer station, diesel generator, storages with decanters of crude vegetable oil, methanol, glycerol, acids, additives and the final products - biodiesel, energy supply unit with a nitrogen station and a compressor plant, production process, as well as the fire protection system). Control units (local PLCs in feedback with operator panels), which are located in cabinets along the drives, collect local digital and analog values of process variables and forward control signals to the actuators. Certain deviations of the regulated values from the setup values cause the alarm set off in the form of light and sound warnings on local panels and the monitor at the dispatch center, with the message relating to the cause, location and time of the disturbance and requiring from the operator to confirm the notice of the alarm. Visual interpretation of the drive components, tabular and graphical representation of characteristic values on dynamic screens enables the operator to monitor the entire production process. Monitoring of the status of each component is also enabled at SCADA in the dispatch center. Control and supervision provide: measurement of process parameters and variables; receiving, processing and displaying the process measurements in the form of tables and trend graphs; visualization of drive status; chronology of events and analysis; monitoring and processing of warning and alarm signals; the possibility of local, remote automatic and remote manual control of individual objects, as well as generating and presenting daily and periodical reports.
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Kychkin, Aleksey V., Alexandr I. Deryabin, Olga L. Vikentyeva, and Lidiia V. Shestakova. "Compensation predictive automation of the smart house climate control systems." Vestnik MGSU, no. 6 (June 2019): 734–47. http://dx.doi.org/10.22227/1997-0935.2019.6.734-747.
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Introduction. The gained experience in the field of building automation and IoT technologies yields a new approach to the management of engineering subsystems that provides stated parameters of operation quality throughout the entire building lifecycle. This paper explores compensatory and predictive algorithms in the scope of the aforementioned approach to manifest control over building climate parameters utilizing IoT controllers. This research aims to improve the management efficiency of smart house engineering subsystems through the implementation of a control system (CS) capable to compensate disturbances and predict their variations using an IoT controller and an analytical server. Materials and methods. In order to improve the quality of control, various algorithms based on analysis of data collected from controllers can be employed. The collected data about the object accumulated over the entire period of operation can be used to build a model for the purposes of predictive control. The predictive control allows to forecast the parameters having an effect on the object and compensate it beforehand under the inertia conditions. The continuous adaptation and adjustment of the CS model to operating conditions allows permanent optimizing the settings of the control algorithm ensuring the efficient operation of local control loops. Results. The CS is based on an IoT controller and able to predict and compensate potential disturbances. The compensation algorithm is updated depending on the behavior of the object properties, quality of control and availability of data most suitable for identification. Conclusions. The capabilities of the control system based on the IoT controller and generation of a compensatory and predictive control signal with the algorithm hosted at a cloud server are demonstrated on the indoor temperature control model. The following simulation models of the indoor temperature variation process are considered: model without CS, model with proportional plus integral controller with disturbance compensation and model with IoT controller-based CS with disturbance compensation. Structural and parametric identification of the models are accomplished by means of active experiment.
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Alvarellos, Alberto, Andrea Lopez Chao, Juan Ramón Rabuñal, María D. García-Vidaurrázaga, and Alejandro Pazos. "Development of an Automatic Low-Cost Air Quality Control System: A Radon Application." Applied Sciences 11, no. 5 (March 1, 2021): 2169. http://dx.doi.org/10.3390/app11052169.
Full textAbstract:
Air pollution is the fourth-largest overall risk factor for human health worldwide. Ambient air pollution (outdoors) and household air pollution (indoors) cause about 6.5 million premature deaths. The World Health Organization has established that between 3% and 14% of lung cancer cases are due to radon gas, making it the most important cause of lung cancer after smoking. This work presents a fully automated, low-cost indoor air quality control system that can monitor temperature, pressure, humidity, total volatile organic compounds (TOVC), and radon concentration. Using the radon concentration as an air quality measure, we created a prediction algorithm. The system uses those predictions to control a ventilation system automatically. We tested the algorithm for different prediction windows and compared the results with those without the ventilation system in a radon research room. In this room, the radon concentration is high 100% of the time, reaching a level eleven times higher than the recommended limit. The results show that the system can achieve an 86% reduction of the radon concentration, maintaining it low 90% of the time while having the ventilation system on during only 34% of the time. This work demonstrates that we can control air quality using low-cost resources, keeping a household or workplace safe but comfortable.