Transactions of the Institute of Systems, Control and Information Engineers
New Arrival Alert : OFF

You can use this feature after you logged into the site.
Please click the button below.

Log in / Sign up
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668

Transactions of the Institute of Systems, Control and Information Engineers Vol. 27 (2014), No. 8

  • Proposal of New Reinforcement Learning with a State-independent Policy

    pp. 327-332

    Bookmark

    You can use this feature after you logged into the site.
    Please click the button below.

    Log in / Sign Up

    DOI:10.5687/iscie.27.327

    Usually, reinforcement learning (RL) algorithms have a difficulty to learn the optimal control policy as the dimensionality of the state (and action) becomes large, because of the explosive increase in the search space to optimize. To avoid such an unfavorable explosive increase, in this study, we propose BASLEM algorithm (Blind Action Sequence Learning with EM algorithm) which acquires a state-independent and time-dependent control policy starting from a certain fixed initial state. Numerical simulation to control a non-holonomic system shows that RL of state-independent and time-dependent policies attain great improvement in efficiency over the existing RL algorithm.
  • Tuning of Nonlinear Model Predictive Controller for Parameter-Dependent Systems and its Application to the Speed Control of Spark Ignition Engines

    pp. 333-342

    Bookmark

    You can use this feature after you logged into the site.
    Please click the button below.

    Log in / Sign Up

    DOI:10.5687/iscie.27.333

    In this paper, we propose a systematic method for the efficient tuning of the performance index in Nonlinear Model Predictive Control (NMPC) of parameter-dependent systems. The quadratic cost function in NMPC is tuned by applying the inverse optimality conditions on the linear quadratic regulator designed for the linearized model using the Inverse Linear Quadratic (ILQ) regulator design method. This approach provides some tuning parameters that give a trade-offbetween the speed of the system’s response and the magnitude of the control input. We propose two systematic methods for the selection of parameter-dependent tuning parameter. This approach is applied to the speed control of mean-value model of Spark Ignition (SI) engines. Effectiveness of the proposed methods is elaborated in simulation results.
  • The Observable Formed Delta Operator Model to Simplify the Identification

    pp. 343-351

    Bookmark

    You can use this feature after you logged into the site.
    Please click the button below.

    Log in / Sign Up

    DOI:10.5687/iscie.27.343

    A delta operator model is usually applied to identification problems with short sampling periods. To identify the system, we have to compute the difference of the input and output data in discrete time and design a noise filter. In this paper, we propose the delta operator model with the observable canonical form. By using this model, we can easily estimate the parameters of the delta operator model without the difference of the input and output data. We derive the Cramer Rao inequality, which evaluates the performance for estimating parameters in the proposed model. Finally, we show the results of the identification experiments for an inertia rotor pendulum and a nonminimum phase system.

Article Access Ranking

16 May. (Last 30 Days)

  1. Influence of Thermomechanical Treatment on Delayed Fracture Property of Mo-Bearing Medium-Carbon Steel ISIJ International Vol.62(2022), No.2
  2. Interaction Coefficients of Mo, B, Ni, Ti and Nb with Sn in Molten Fe–18mass%Cr Alloy ISIJ International Vol.62(2022), No.3
  3. Fundamentals of Silico-Ferrite of Calcium and Aluminium (SFCA) and SFCA-I Iron Ore Sinter Bonding Phase Formation: Effects of MgO Source on Phase Formation during Heating ISIJ International Vol.62(2022), No.4
  4. Dissolution Kinetics of Synthetic FeCr2O4 in CaO–MgO–Al2O3–SiO2 Slag ISIJ International Vol.62(2022), No.4
  5. Surface Quality Evaluation of Heavy and Medium Plate Using an Analytic Hierarchy Process Based on Defects Online Detection ISIJ International Advance Publication
  6. Influence of Stabilizing Elements on Ductile-Brittle Transition Temperature (DBTT) of 18Cr Ferritic Stainless Steels ISIJ International Vol.62(2022), No.4
  7. Exploration of the Relationship between the Electromagnetic Field and the Hydrodynamic Phenomenon in a Channel Type Induction Heating Tundish Using a Validated Model ISIJ International Vol.62(2022), No.4
  8. Assessment of Blast Furnace Operational Constraints in the Presence of Hydrogen Injection ISIJ International Advance Publication
  9. Phenomenological Understanding about Melting Temperature of Multi-Component Oxides Tetsu-to-Hagané Vol.108(2022), No.4
  10. Numerical Simulation of Charging Biochar Composite Briquette to Blast Furnace ISIJ International Vol.62(2022), No.4

Search Phrase Ranking

16 May. (Last 30 Days)

  1. blast furnace
  2. blast furnace permeability
  3. blast furnace productivity
  4. jet impingement
  5. jet impingement + cooling + runout table
  6. refractory
  7. steel
  8. viscosity of slag fluorine
  9. 1. m. nainar, a. veawab: ind. eng. chem. res., 48(2009), 9299. https://doi.org/10.1021/ie801802a 2. c. h. yu, c. h. huang, c. s. tan: aerosol air qual. res., 12(2012), 745. https://doi.org/10.4209/aaqr.2012.05.0132 3. g. léonard, c. crosset, d. toye, g. heyen: comput. chem. eng., 83(2015), 121. https://doi.org/10.1016/j.compchemeng.2015.05.003 4. e. e. ünveren, b. ö. monkul, ş. sarıoğlan, n. karademir, e. alper: petroleum, 3(2017), 37. https://doi.org/10.1016/j.petlm.2016.11.001 5. m. b. yue, b. sun, y. cao, y. wang, j. wang: chem. eur. j., 14(2008), 3442. https://doi.org/10.1002/chem.200701467 6. w. choi, j. park, c. kim, m. choi: chem. eng. j., 408(2021), 127289. https://doi.org/10.1016/j.cej.2020.127289 7. c. chen, s. t. yang, w. s. ahn, r. ryoo: chem. commun., 24(2009), 3627. https://doi.org/10.1039/b905589d 8. p. zhao, g. zhang, y. xu, y. k. lv, z. yang, h. cheng: energy and fuels, 33(2019), 3357. https://doi.org/10.1021/acs.energyfuels.8b04278 9. k. dong, w. liu, r. zhu: high temp. mater. process, 34(2015), 539. https://doi.org/10.1515/htmp-2014-0076 10. s. wang, s. xu, s. gao, p. xiao, m. jiang, h. zhao, b. huang, l. liu, h. niu, j. wang, d. guo: sci. rep., 11(2021), 1. https://doi.org/10.1038/s41598-021-90532-9 11. q. t. vu, h. yamada, k. yogo: ind. eng. chem. res., 60(2021), 4942. https://doi.org/10.1021/acs.iecr.0c05694 12. m. wang, l. yao, j. wang, z. zhang, w. qiao, d. long, l. ling: appl. energy, 168(2016), 282. https://doi.org/10.1016/j.apenergy.2016.01.085 13. a. heydari-gorji, a. sayari: ind. eng. chem. res., 51(2012), 6887. https://doi.org/10.1021/ie3003446 14. s. a. didas, r. zhu, n. a. brunelli, d. s. sholl, c. w. jones: j. phys. chem. c., 118(2014), 12302. https://doi.org/10.1021/jp5025137 15. q. t. vu, h. yamada, k. yogo: ind. eng. chem. res., 57(2018), 2638. https://doi.org/10.1021/acs.iecr.7b04722 16. q. t. vu, h. yamada, k. yogo: energy & fuels, 33(2019), 3370. https://doi.org/10.1021/acs.energyfuels.8b04307 17. x. zhang, x. zheng, s. zhang, b. zhao, w. wu: ind. eng. chem. res., 51(2012), 15163. https://doi.org/10.1021/ie300180u 18. h. yamada, f. a. chowdhury, j. fujiki, k. yogo: acs sustain. chem. eng., 7(2019), 9574. https://doi.org/10.1021/acssuschemeng.9b01064 19. x. wang, q. guo, t. kong: chem. eng. j., 273(2015), 472. https://doi.org/10.1016/j.cej.2015.03.098 20. f. s. taheri, a. ghaemi, a. maleki: energy and fuels, 33(2019),11465. https://doi.org/10.1021/acs.energyfuels.9b02636 21. a. sayari, y. belmabkhout: j. am. chem. soc., 132(2010), 6312. https://doi.org/10.1021/ja1013773
  10. 10.1016/j.apenergy.2016.01.085