著者
北森 俊行
出版者
公益社団法人 計測自動制御学会
雑誌
計測と制御 (ISSN:04534662)
巻号頁・発行日
vol.19, no.4, pp.382-391, 1980-04-10 (Released:2009-11-26)
参考文献数
7
被引用文献数
5
著者
藤田 真太郎 澤田 賢治 新 誠一 細川 嵩
出版者
公益社団法人 計測自動制御学会
雑誌
計測自動制御学会論文集 (ISSN:04534654)
巻号頁・発行日
vol.57, no.8, pp.367-377, 2021 (Released:2021-08-20)
参考文献数
21

Since the damage from cyber-attacks increases, there is an urgent need to research and develop security countermeasures for control systems. In the control system, the controller is an important device. This paper then considers a whitelisting system that models the normal operation sequence of a PLC (Programmable Logic Controller) and detects deviations from the model as abnormal. We propose three methods to auto-generate the whitelisting system by LD (Ladder Diagram): Petri net model generation, constraint condition derivation, and LD conversion. The first method generates Petri net models form SFCs (Sequential Function Charts) that are compatible with LDs. The second method derives whitelist conditions from Petri net models to check whether PLC performs the correct operation sequence. The third method implements the whitelist conditions into LD. The auto-generated whitelisting system enables us to monitor the state transitions of the PLC programs. Further, this paper carries out an experimental validation of the methods using a testbed system.
著者
東 俊一 田淵 絢子 杉江 俊治
出版者
公益社団法人 計測自動制御学会
雑誌
計測自動制御学会論文集 (ISSN:04534654)
巻号頁・発行日
vol.48, no.12, pp.882-888, 2012 (Released:2013-01-23)
参考文献数
21
被引用文献数
2 5

This paper develops a mathematical model of a sheepdog control strategy, in order to clarify a principle for controlling autonomously (and randomly) moving multiple agents. First, by observing real behaviors of a sheepfold and a sheepdog, we extract their essential properties as a multi-agent system. Based on this, difference equations are constructed for the sheep and sheepdog. It is verified by numerical simulations that the proposed model captures their qualitative behaviors.
著者
吉田 倫幸
出版者
公益社団法人 計測自動制御学会
雑誌
計測と制御 (ISSN:04534662)
巻号頁・発行日
vol.41, no.10, pp.696-701, 2002-10-10 (Released:2009-11-26)
参考文献数
22
被引用文献数
2
著者
藤岡 巧 岡島 寛 松永 信智
出版者
公益社団法人 計測自動制御学会
雑誌
計測自動制御学会論文集 (ISSN:04534654)
巻号頁・発行日
vol.50, no.12, pp.861-868, 2014 (Released:2014-12-23)
参考文献数
13
被引用文献数
3 7

Robust control considering model uncertainty have been widely studied. The benchmark problem using three-inertia system was designed by SICE committee in order to correctly evaluate robust control methods. Traditional robust controller is designed to achieve desired performance for the worst case in a model set. Therefore, it is difficult to achieve high control performance by the traditional robust controller. To overcome this problem, we have proposed a robust control system for this benchmark problem using the model error compensator (MEC) and the frequency shaped final-state control (FFSC). MEC can minimize the gap between real plant and its nominal model. An input signal designed by FFSC can settle desired state variables in terminal period and achieve vibration suppression control. Since MEC is familiar with many other control systems, it is easy to combine FFSC and MEC. Simulation results for the benchmark problem are shown to confirm the effectiveness of proposed method.
著者
船橋 賢一
出版者
公益社団法人 計測自動制御学会
雑誌
計測と制御 (ISSN:04534662)
巻号頁・発行日
vol.30, no.4, pp.280-284, 1991-04-10 (Released:2009-11-26)
参考文献数
20
被引用文献数
1
著者
井床 利之
出版者
公益社団法人 計測自動制御学会
雑誌
計測と制御 (ISSN:04534662)
巻号頁・発行日
vol.37, no.11, pp.771-773, 1998-11-10 (Released:2009-11-26)
参考文献数
4
著者
植田 聡史 伊藤 琢博 坂井 真一郎
出版者
公益社団法人 計測自動制御学会
雑誌
計測自動制御学会論文集 (ISSN:04534654)
巻号頁・発行日
vol.58, no.3, pp.194-201, 2022 (Released:2022-04-07)
参考文献数
12

Space missions require “resilience” to flexibly complete the mission in response to changes in the environment and system characteristics. The present study proposes a method for autonomously planning a corrective control law for lunar landing trajectory control to cope with off-nominal conditions and reflecting it in resilience improvement measures by utilizing reinforcement learning. The proposed method employs a reinforcement learning problem in which an agent is additionally placed in the control loop and the corrective control input as an action output by the agent is added to the original closed-loop control input. The results and insights are summarized for the resultant agent's characteristics which autonomously detect off-nominal conditions and proactively implement recovery measures, while verifying the capability and effectiveness of the proposed design framework enabled by a reinforcement learning architecture in a realistic and specific lunar landing sequence.