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Journal articles on the topic 'Embedded model control'

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Author: Grafiati

Published: 14 March 2023

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1

Cetinkunt, Sabri, Shin-ichi Nakajima, Brad Nelson, and Salem Haggag. "Embedded-Model-Based Control." Journal of Control Science and Engineering 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/237897.

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2

Takács, Gergely, Gabriel Batista, Martin Gulan, and Boris Rohaľ-Ilkiv. "Embedded explicit model predictive vibration control." Mechatronics 36 (June 2016): 54–62. http://dx.doi.org/10.1016/j.mechatronics.2016.04.008.

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3

Johansen, Tor A. "Toward Dependable Embedded Model Predictive Control." IEEE Systems Journal 11, no. 2 (June 2017): 1208–19. http://dx.doi.org/10.1109/jsyst.2014.2368129.

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4

McLain, Richard B., and Michael A. Henson. "Nonlinear Model Reference Adaptive Control with Embedded Linear Models." Industrial & Engineering Chemistry Research 39, no. 8 (August 2000): 3007–17. http://dx.doi.org/10.1021/ie990088t.

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5

Lotufo, Mauricio Alejandro, Luigi Colangelo, and Carlo Novara. "Control Design for UAV Quadrotors via Embedded Model Control." IEEE Transactions on Control Systems Technology 28, no. 5 (September 2020): 1741–56. http://dx.doi.org/10.1109/tcst.2019.2918750.

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6

Canuto, Enrico, and Fabio Musso. "Embedded model control: Application to web winding." ISA Transactions 46, no. 3 (June 2007): 379–90. http://dx.doi.org/10.1016/j.isatra.2007.01.002.

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7

Canuto, Enrico. "Embedded Model Control: Outline of the theory." ISA Transactions 46, no. 3 (June 2007): 363–77. http://dx.doi.org/10.1016/j.isatra.2007.01.006.

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8

Canuto, Enrico, Carlo Novara, and Luigi Colangelo. "Embedded model control: Reconciling modern control theory and error-based control design." Control Theory and Technology 16, no. 4 (November 2018): 261–83. http://dx.doi.org/10.1007/s11768-018-8130-1.

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9

Ling, K. V., B. F. Wu, and J. M. Maciejowski. "Embedded Model Predictive Control (MPC) using a FPGA." IFAC Proceedings Volumes 41, no. 2 (2008): 15250–55. http://dx.doi.org/10.3182/20080706-5-kr-1001.02579.

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10

Currie, J., and D. I. Wilson. "Lightweight Model Predictive Control intended for embedded applications." IFAC Proceedings Volumes 43, no. 5 (2010): 278–83. http://dx.doi.org/10.3182/20100705-3-be-2011.00046.

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11

Acuña-Bravo, Wilber, Andrés Guillermo Molano-Jiménez, and Enrico Canuto. "Embedded model control, performance limits: A case study." DYNA 84, no. 201 (June 12, 2017): 267. http://dx.doi.org/10.15446/dyna.v84n201.59165.

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Este trabajo presenta el análisis e implementación de dos leyes de control aplicadas en un caso de estudio. La primera y el enfoque principal de este trabajo es el control a modelo embebido cuyas características fundamentales son la cancelación activa de disturbios y el manejo de incertidumbres. Aquí se prueban diferentes configuraciones para los parámetros del control logrando que los límites en el desempeño queden expuestos debido a las limitaciones del modelo. Por otro lado se encuentra la ley de control PID clásica basada en acciones sobre el error de trayectoria como un paradigma de control que no requiere de un modelo para su ejecución. La implementación y pruebas se hacen en una planta didáctica comercial, donde se verifican las propiedades de desempeño y rechazo a perturbaciones mostrando que aun en este simple proceso el desempeño puede verse degradado debido a la ausencia de un modelo de la planta embebido en la ley de control

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12

Kufoalor, D. K. M., L. Imsland, and T. A. Johansen. "Efficient implementation of step response models for embedded Model Predictive Control." Computers & Chemical Engineering 90 (July 2016): 121–35. http://dx.doi.org/10.1016/j.compchemeng.2016.04.002.

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13

CHEN, De-sheng, Bin WANG, Jie XUE, and Li YU. "Graphical meta-model construction method of embedded control system." Journal of Computer Applications 32, no. 11 (May 26, 2013): 3085–88. http://dx.doi.org/10.3724/sp.j.1087.2012.03085.

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14

Danko, G. "EMBEDDED, DYNAMIC MODEL IDENTIFICATION FOR DIFFERENTIAL MACHINE MOTION CONTROL." IFAC Proceedings Volumes 40, no. 11 (2007): 133–45. http://dx.doi.org/10.3182/20070821-3-ca-2919.00020.

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15

Bezerra Correia Lima, Rafael, and Pericles Rezende Barros. "Embedded Subspace Predictive Control, a Model Based Design Approach." IEEE Latin America Transactions 19, no. 11 (November 2021): 1816–23. http://dx.doi.org/10.1109/tla.2021.9475614.

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16

Gulan, Martin, Gergely Takacs, Ngoc Anh Nguyen, Sorin Olaru, Pedro Rodriguez-Ayerbe, and Boris Rohal'-Ilkiv. "Efficient Embedded Model Predictive Vibration Control via Convex Lifting." IEEE Transactions on Control Systems Technology 27, no. 1 (January 2019): 48–62. http://dx.doi.org/10.1109/tcst.2017.2764019.

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17

Chakrabarty, Ankush, Elizabeth Healey, Dawei Shi, Stamatina Zavitsanou, Francis J. Doyle, and Eyal Dassau. "Embedded Model Predictive Control for a Wearable Artificial Pancreas." IEEE Transactions on Control Systems Technology 28, no. 6 (November 2020): 2600–2607. http://dx.doi.org/10.1109/tcst.2019.2939122.

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18

Acuña-Bravo, Wilber, Enrico Canuto, Marco Agostani, and Marco Bonadei. "Proportional electro-hydraulic valves: An Embedded Model Control solution." Control Engineering Practice 62 (May 2017): 22–35. http://dx.doi.org/10.1016/j.conengprac.2017.01.013.

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19

Canuto, Enrico, Wilber Acuna-Bravo, Andrés Molano-Jimenez, and Carlos Perez Montenegro. "Embedded model control calls for disturbance modeling and rejection." ISA Transactions 51, no. 5 (September 2012): 584–95. http://dx.doi.org/10.1016/j.isatra.2012.04.002.

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20

Lotufo, M. A., L. Colangelo, C. Perez-Montenegro, C. Novara, and E. Canuto. "Embedded Model Control for UAV Quadrotor via Feedback Linearization." IFAC-PapersOnLine 49, no. 17 (2016): 266–71. http://dx.doi.org/10.1016/j.ifacol.2016.09.046.

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21

Xian Zhang, Xian Zhang, Pengju Tang Xian Zhang, Dong Yin Pengju Tang, Taiguo Qu Dong Yin, and Yiwen Liu Taiguo Qu. "The Network Model of Internet Access Intranet Based on Embedded Platform." 網際網路技術學刊 23, no. 2 (March 2022): 201–8. http://dx.doi.org/10.53106/160792642022032302001.

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<p>To better manage and control the embedded devices located in the remote Intranet, the functional requirements of the Internet accessing the Intranet need to be effectively solved. This paper studies and implements a network model of external network accessing intranet network and realizes the network model’s function by programming. At the same time, an experimental platform is built to test the performance metrics of the network model, such as the maximum load and CPU utilization, device connection success rate, user access response time, and other performance parameters. The test results show that the network model can meet the network scale of at least 3000 intranet devices and has high practical value and application prospects.</p> <p> </p>

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22

Fisher, Francis (Pete). "Embedded ASR: Lessons Learned Integrating Speech Control into Embedded Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (July 2000): 758–61. http://dx.doi.org/10.1177/154193120004402268.

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For the most basic embedded systems, an integrator must assume that the system has no keyboard support, no mouse, and no monitor. A programmable keypad and a limited alphanumeric display are the usual interface devices. Our research looks at the new generation of powerful embedded platforms and operating systems to investigate the potential integration of automatic speech recognition (ASR) to control high-end diagnostic systems. Our goal is to present a working design model and identify critical issues to aid future designers and system engineers in the process of incorporating speech control into embedded systems. Critical issues discussed include hardware platforms, use of off-the-shelf ASR software, real-time versus near-real-time operating systems, and user system navigation and location awareness.

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23

Ramelan, Agus, Arief Syaichu Rohman, and Allen Kelana. "Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control." MATEC Web of Conferences 198 (2018): 04007. http://dx.doi.org/10.1051/matecconf/201819804007.

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This paper presents an implementation embedded system for position control of Permanent Magnet Synchronous Motor (PMSM). The control system consists of raspberry pi 3 as a microcontroller, ASDA-A2 servo drive, and Delta Servo ECMA type. The software design includes simulation tool and Python included on Raspbian OS. Communication between Raspberry Pi 3 and ASDA-A2 drivers using the ASCII Modbus communication protocol. Raspberry Pi 3 processes the reference data and the actual reading result and calculates the resulting error. The control algorithm used in this research is Model Predictive Control (MPC). As a Linear Quadratic Regulator, MPC aims to design and generate an optimal control signal with the ability to anticipate saturation, receding horizon, future event and take control accordingly In the design of the MPC technique to adjust the speed of the PMSM to take action of reference tracking, performance index optimization is done by adjusting the value of weighting horizon N, Q and R. The simulation and implementation results showed that the PMSM can reach the stability point on each desired setpoint and result in a near-zero steady-state error.

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24

Meng, Ling Jun, Xiang Hu Wu, and Ming Cheng Qu. "Graphical Meta-Model Construction Method of Embedded Control System of Specific Areas." Applied Mechanics and Materials 644-650 (September 2014): 305–8. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.305.

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An embedded control system development methodology based on model-driven development method was emerged, the basic features and architecture of the embedded control system of specific areas were analyzed and summarized, and the key elements of this specific domain were identified and extracted. Afterwards, a visible element modeling technique was used to build a directly graphical model system, and this kind of embedded modeling technique based on model-driven was applied to realize a model of embedded Robot control system design. As a result, the Robot source model of this system was obtained.

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25

Huang, Shiuh Jer, Chiao Kuen Yu, and You Min Huang. "Robotic Impedance Control with Fuzzy Sliding Model Control Strategy." Applied Mechanics and Materials 598 (July 2014): 605–9. http://dx.doi.org/10.4028/www.scientific.net/amm.598.605.

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During robotic assembly and interactive applications, the robot end-effector must follow a motion trajectory and exert an appropriate force profile against the contacted environment to provide a specified dynamic working compliance. It is a difficult control problem. Here, an embedded robotic control structure is constructed and the related hybrid control software programs are developed. The model-free intelligent fuzzy sliding mode controller is introduced to design force and position controllers, respectively for hybrid impedance control purpose. The experimental results are provided to demonstrate the effectiveness of the proposed hybrid impedance control system.

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26

Greatwood, Colin, and Arthur G. Richards. "Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control." Autonomous Robots 43, no. 7 (January 12, 2019): 1681–93. http://dx.doi.org/10.1007/s10514-019-09829-4.

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27

Doncevic, Danimir T., Artur M. Schweidtmann, Yannic Vaupel, Pascal Schäfer, Adrian Caspari, and Alexander Mitsos. "Deterministic Global Nonlinear Model Predictive Control with Neural Networks Embedded." IFAC-PapersOnLine 53, no. 2 (2020): 5273–78. http://dx.doi.org/10.1016/j.ifacol.2020.12.1207.

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28

Khajanchi, Hussain I., Joseph N. Bruno, and Ambrose A. Adegbege. "An Embedded FPGA Architecture for Real-Time Model Predictive Control." IFAC-PapersOnLine 53, no. 2 (2020): 7833–38. http://dx.doi.org/10.1016/j.ifacol.2020.12.1886.

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29

Ku, Dae-Kwan, Jun-Keun Ji, and Guee-Soo Cha. "Embedded Control System of Segway Robot using Model Based Design." Journal of the Korea Academia-Industrial cooperation Society 11, no. 8 (August 31, 2010): 2975–82. http://dx.doi.org/10.5762/kais.2010.11.8.2975.

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30

Wang, S., and K. G. Shin. "Task construction for model-based design of embedded control software." IEEE Transactions on Software Engineering 32, no. 4 (April 2006): 254–64. http://dx.doi.org/10.1109/tse.2006.39.

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31

Chakrabarty, Ankush, Stamatina Zavitsanou, Francis J. Doyle, and Eyal Dassau. "Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems." IEEE Transactions on Biomedical Engineering 65, no. 3 (March 2018): 575–86. http://dx.doi.org/10.1109/tbme.2017.2707344.

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32

Jerez, Juan L., Paul J. Goulart, Stefan Richter, George A. Constantinides, Eric C. Kerrigan, and Manfred Morari. "Embedded Online Optimization for Model Predictive Control at Megahertz Rates." IEEE Transactions on Automatic Control 59, no. 12 (December 2014): 3238–51. http://dx.doi.org/10.1109/tac.2014.2351991.

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33

Bleris, Leonidas G., Mayuresh V. Kothare, Jesus Garcia, and Mark G. Arnold. "Embedded Model Predictive Control for System-on-a-Chip Applications." IFAC Proceedings Volumes 37, no. 9 (July 2004): 889–94. http://dx.doi.org/10.1016/s1474-6670(17)31922-5.

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34

Giselsson, Pontus. "Improved Fast Dual Gradient Methods for Embedded Model Predictive Control." IFAC Proceedings Volumes 47, no. 3 (2014): 2303–9. http://dx.doi.org/10.3182/20140824-6-za-1003.00295.

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35

Canuto, Enrico, Fabio Musso, and JosÉ Ospina. "Embedded model control: Submicroradian horizontality of the nanobalance thrust-stand." IEEE Transactions on Industrial Electronics 55, no. 9 (September 2008): 3435–46. http://dx.doi.org/10.1109/tie.2008.925332.

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36

Colangelo, Luigi, Luca Massotti, Enrico Canuto, and Carlo Novara. "Embedded model control GNC for the Next Generation Gravity Mission." Acta Astronautica 140 (November 2017): 497–508. http://dx.doi.org/10.1016/j.actaastro.2017.09.016.

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37

Sargsyan, Khachik, Xun Huan, and Habib N. Najm. "EMBEDDED MODEL ERROR REPRESENTATION FOR BAYESIAN MODEL CALIBRATION." International Journal for Uncertainty Quantification 9, no. 4 (2019): 365–94. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2019027384.

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38

Kufoalor, D. K. M., V. Aaker, T. A. Johansen, L. Imsland, and G. O. Eikrem. "Automatically generated embedded model predictive control: Moving an industrial PC-based MPC to an embedded platform." Optimal Control Applications and Methods 36, no. 5 (June 17, 2015): 705–27. http://dx.doi.org/10.1002/oca.2182.

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39

Slavov, Tsonyo, Alexander Mitov, and Jordan Kralev. "Advanced Embedded Control of Electrohydraulic Power Steering System." Cybernetics and Information Technologies 20, no. 2 (June 1, 2020): 105–21. http://dx.doi.org/10.2478/cait-2020-0020.

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AbstractThe article presents a developed embedded system for control of electrohydraulic power steering based on multivariable uncertain plant model and advanced control techniques. The plant model is obtained by identification procedure via “black box” system identification and takes into account the deviations of the parameters that characterize the way that the control signal acts on the state of the model. Three types of controller are designed: Linear-Quadratic Gaussian (LQG) controller, H∞ controller and μ-controller. The main result is a performed comparative analysis of time and frequency domain properties of control systems. The results show the better performance of systems based on µ-controllers. Also the robust stability and robust performance are investigated. All three systems achieved robust stability which guarantees their workability, but only the system with µ-controller has robust performance against prescribed uncertainties. The control algorithms are implemented in specialized 32-bit microcontroller. A number of real world experiments have been executed, which confirm the quality of the electrohydraulic power steering control system.

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40

Szita, István, and András Lőrincz. "Kalman Filter Control Embedded into the Reinforcement Learning Framework." Neural Computation 16, no. 3 (March 1, 2004): 491–99. http://dx.doi.org/10.1162/089976604772744884.

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There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

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41

Hu, Sheng Hai, Bao Ping Zhang, Tian Shi Sun, and Wei Yu. "Model-Based Design and it's Application in Power Window." Advanced Materials Research 889-890 (February 2014): 850–55. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.850.

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Along with the continuous development of embedded systems, embedded products take higher requirements on the scale and performance of system software, and contributing to the complexity of control algorithm. So it would take more time and expense to realize the control algorithm. Therefore, traditional design is out of date and cant cope with the modern development rhythm well. While the Model-Based Design, is an efficient design method to deal with complex embedded control systems. It provides a common development and test platform for the engineers. In this paper, we take the power windows control system as example; establish the algorithm model with the Simulink/Stateflow. Then we introduce these process of the control system design which is based on the Model-Based Design method, according to the result of the final experiment, we prove the efficiency of this method, providing a reference for the design of other complex control systems.

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42

Aljarbouh, Ayman, Muhammad Fayaz, Muhammad Shuaib Qureshi, and Younes Boujoudar. "Hybrid Sliding Mode Control of Full-Car Semi-Active Suspension Systems." Symmetry 13, no. 12 (December 17, 2021): 2442. http://dx.doi.org/10.3390/sym13122442.

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With the advance in technology in driving vehicles, there is currently more emphasis on developing advanced control systems for better road handling stability and ride comfort. However, one of the challenging problems in the design and implementation of intelligent suspension systems is that there is currently no solution supporting the export of generic suspension models and control components for integration into embedded Electronic Control Units (ECUs). This significantly limits the usage of embedded suspension components in automotive production code software as it requires very high efforts in implementation, manual testing, and fulfilling coding requirements. This paper introduces a new dynamic model of full-car suspension system with semi-active suspension behavior and provides a hybrid sliding mode approach for control of full-car suspension dynamics such that the road handling stability and ride comfort characteristics are ensured. The semi-active suspension model and the hybrid sliding mode controller are implemented as Functional Mock-Up Units (FMUs) conforming to the Functional Mock-Up Interface for embedded systems (eFMI) and are calibrated with a set experimental tests using a 1/5 Soben-car test bench. The methods and prototype implementation proposed in this paper allow both model and controller re-usability and provide a generic way of integrating models and control software into embedded ECUs.

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43

Zheng, Fang, Yang Jie, Tao Shifei, Wu Zhiwei, and Chai Tianyou. "A Real-Time Embedded Control System for Electro-Fused Magnesia Furnace." Journal of Control Science and Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/719683.

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Since smelting process of electro-fused magnesia furnace is a complicated process which has characteristics like complex operation conditions, strong nonlinearities, and strong couplings, traditional linear controller cannot control it very well. Advanced intelligent control strategy is a good solution to this kind of industrial process. However, advanced intelligent control strategy always involves huge programming task and hard debugging and maintaining problems. In this paper, a real-time embedded control system is proposed for the process control of electro-fused magnesia furnace based on intelligent control strategy and model-based design technology. As for hardware, an embedded controller based on an industrial Single Board Computer (SBC) is developed to meet industrial field environment demands. As for software, a Linux based on Real-Time Application Interface (RTAI) is used as the real-time kernel of the controller to improve its real-time performance. The embedded software platform is also modified to support generating embedded code automatically from Simulink/Stateflow models. Based on the proposed embedded control system, the intelligent embedded control software of electro-fused magnesium furnace can be directly generated from Simulink/Stateflow models. To validate the effectiveness of the proposed embedded control system, hardware-in-the-loop (HIL) and industrial field experiments are both implemented. Experiments results show that the embedded control system works very well in both laboratory and industry environments.

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44

MacKinnon, Lloyd, Hao Li, and Christopher L. E. Swartz. "Robust model predictive control with embedded multi-scenario closed-loop prediction." Computers & Chemical Engineering 149 (June 2021): 107283. http://dx.doi.org/10.1016/j.compchemeng.2021.107283.

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45

Xiao, Chunyu, Enrico Canuto, Wei Hong, Zebing Zhou, and Jun Luo. "Drag-free design based on Embedded Model Control for TianQin project." Acta Astronautica 198 (September 2022): 482–94. http://dx.doi.org/10.1016/j.actaastro.2022.06.031.

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46

Acuña-Bravo, Wilber, Andrés Guillermo Molano-Jiménez, and Enrico Canuto. "Embedded model control for underactuated systems: An application to Furuta pendulum." Control Engineering Practice 113 (August 2021): 104854. http://dx.doi.org/10.1016/j.conengprac.2021.104854.

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47

Bleris, Leonidas G., Jesus Garcia, Mayuresh V. Kothare, and Mark G. Arnold. "Towards embedded model predictive control for System-on-a-Chip applications." Journal of Process Control 16, no. 3 (March 2006): 255–64. http://dx.doi.org/10.1016/j.jprocont.2005.06.006.

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48

Phung, Duc-Kien, Bruno Hérissé, Julien Marzat, and Sylvain Bertrand. "Model Predictive Control for Autonomous Navigation Using Embedded Graphics Processing Unit." IFAC-PapersOnLine 50, no. 1 (July 2017): 11883–88. http://dx.doi.org/10.1016/j.ifacol.2017.08.1415.

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49

Kull, Tobias, Bernd Zeilmann, and Gerhard Fischerauer. "Modular Model Composition for Rapid Implementations of Embedded Economic Model Predictive Control in Microgrids." Applied Sciences 11, no. 22 (November 11, 2021): 10602. http://dx.doi.org/10.3390/app112210602.

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Economic model predictive control in microgrids combined with dynamic pricing of grid electricity is a promising technique to make the power system more flexible. However, to date, each individual microgrid requires major efforts for the mathematical modelling, the implementation on embedded devices, and the qualification of the control. In this work, a field-suitable generalised linear microgrid model is presented. This scalable model is instantiated on field-typical hardware and in a modular way, so that a class of various microgrids can be easily controlled. This significantly reduces the modelling effort during commissioning, decreases the necessary qualification of commissioning staff, and allows for the easy integration of additional microgrid devices during operation. An exemplary model, derived from an existing production facility microgrid, is instantiated, and the characteristics of the results are analysed.

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50

Xiao, Ping, Qidong Wang, Limin Niu, and Hong Gao. "Research on suspension system with embedded-permanent-magnet magnetorheological damper based on V-model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 10 (April 22, 2015): 1602–14. http://dx.doi.org/10.1177/0954406215583691.

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In order to enhance performance of automotive suspension with embedded-permanent-magnet magnetorheological (MR) damper, V-model developing flow of electronic control unit was adopted to study control system of the suspension. First of all, rapid control prototyping (RCP) system of suspension with embedded-permanent-magnet MR damper was built up based on real-time simulation platform D2P; control algorithm of fruit fly optimization algorithm– proportional-integral-derivative was designed and experiments of RCP simulation were carried out. Moreover, model of suspension with embedded-permanent-magnet MR damper was built with the instruction of magnetic field composition theory. On these bases, hardware in the loop simulation (HILS) platform was built up and HILS experiments were carried out as well. Finally, bench tests were carried out, whose results, together with simulation results, indicated that the controlling system of suspension with embedded-permanent-magnets MR damper in the given research could enhance automobiles’ ride performance and the designed control strategy and algorithm were good.

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