Steel Science Portal

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.

ONLINE ISSN: 2185-811X

PRINT ISSN: 1342-5668

Publisher :

THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Current Issue
Vol. 35 (2022)
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 34 (2021)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 33 (2020)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 32 (2019)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 31 (2018)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 30 (2017)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 29 (2016)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 28 (2015)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 27 (2014)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 26 (2013)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 25 (2012)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 24 (2011)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 23 (2010)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 22 (2009)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 21 (2008)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 20 (2007)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 19 (2006)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 18 (2005)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 17 (2004)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 16 (2003)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 15 (2002)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 14 (2001)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 13 (2000)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 12 (1999)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 11 (1998)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 10 (1997)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 9 (1996)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 8 (1995)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 7 (1994)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 6 (1993)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 5 (1992)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 4 (1991)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 3 (1990)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 2 (1989)
- No. 12
- No. 11
- No. 10
- No. 9
- No. 8
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1
Vol. 1 (1988)
- No. 7
- No. 6
- No. 5
- No. 4
- No. 3
- No. 2
- No. 1

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

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

Daichi Nakano, Shin-ichi Maeda, Shin Ishii

pp. 327-332

Readers Likes, Comments, Shares
PDF (J-STAGE) Share
Share it with SNS
Proposal of New Reinforcement Learning with a State-independent Policy

TwitterTweet
You can tweet with article title and URL.

FacebookShare
You can share URL of article published on line.

MendeleySave
You can save articles in your library.

BibTeX
You can download bibliographic data as BibTeX format.

RIS
You can download bibliographic data as RIS format.
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

Abstract

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

Fatima Tahir, Toshiyuki Ohtsuka

pp. 333-342

Readers Likes, Comments, Shares
PDF (J-STAGE) Share
Share it with SNS
Tuning of Nonlinear Model Predictive Controller for Parameter-Dependent Systems and its Application to the Speed Control of Spark Ignition Engines

TwitterTweet
You can tweet with article title and URL.

FacebookShare
You can share URL of article published on line.

MendeleySave
You can save articles in your library.

BibTeX
You can download bibliographic data as BibTeX format.

RIS
You can download bibliographic data as RIS format.
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

Abstract

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

Kazuyuki Iwamoto, Noriyoshi Kumazawa, Hiroyuki Ichihara, Naoto Abe, Yasuyoshi Fukui

pp. 343-351

Readers Likes, Comments, Shares
PDF (J-STAGE) Share
Share it with SNS
The Observable Formed Delta Operator Model to Simplify the Identification

TwitterTweet
You can tweet with article title and URL.

FacebookShare
You can share URL of article published on line.

MendeleySave
You can save articles in your library.

BibTeX
You can download bibliographic data as BibTeX format.

RIS
You can download bibliographic data as RIS format.
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

Abstract

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

04 Oct. (Last 30 Days)

Interaction Coefficients of Cu and Sn with Mn in Molten Iron at 1873 K ISIJ International Advance Publication
Deformation of Non-metallic Inclusions in Steel during Rolling Process: A Review ISIJ International Advance Publication
Thickness classifier on steel in heavy melting scrap by deep-learning-based image analysis ISIJ International Advance Publication
Bandwidth Maximization of Disturbance Observer Based on Experimental Frequency Response Data SICE Journal of Control, Measurement, and System Integration Vol.13(2020), No.6
Soft sensors and Diagnostic Models Using Real Time Data of Blast Furnaces at Tata Steel ISIJ International Advance Publication
Effect of SiO₂content and mass ratio of CaO to Al₂O₃ on the viscosity and structure of CaO-Al₂O₃-B₂O₃-SiO₂ slags ISIJ International Advance Publication
Non-Cooperative Optimization Algorithm of Charging Scheduling for Electric Vehicle SICE Journal of Control, Measurement, and System Integration Vol.13(2020), No.6
Thermodynamic Conditions of MgO and MgO·Al₂O₃ Formation and Variation of Inclusions Formed in Fe-17 mass%Cr Steel at 1873 K Tetsu-to-Hagané Vol.108(2022), No.8
Comparison of Oxidation Behavior of Various Reactive Elements in Alloys during Electroslag Remelting (ESR) Process: An Overview ISIJ International Vol.62(2022), No.8
Effect of 2CaO·SiO₂ Addition on Reaction Behavior of Iron Ore Sinters ISIJ International Vol.62(2022), No.9

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

Share it with SNS

Tuning of Nonlinear Model Predictive Controller for Parameter-Dependent Systems and its Application to the Speed Control of Spark Ignition Engines

Share it with SNS

The Observable Formed Delta Operator Model to Simplify the Identification

Share it with SNS

Article Access Ranking

Search Phrase Ranking