Pub Date : 2022-06-16DOI: 10.1080/18824889.2022.2087413
Viet Duc Bui, T. Shirakawa, Hiroshi Sato
In recent years, owing to the advance in image processing using deep learning, autonomous unmanned aerial vehicle (UAV) navigation based on image recognition has become possible. However, several image-based deep learning methods focus primarily on single-task autonomous UAV systems, which cannot perform other required tasks. Meanwhile, deep learning methods based on multi-task learning, which are suitable for multi-tasking autonomous UAV systems, have not been sufficiently researched. Therefore, in this study, we propose a UAV flight control method that can enable correction of a UAV's self-position, self-direction, and recognition/selection of multiple movement directions using multi-task learning for exploring an unknown indoor environment, which is based only on information from monocular camera images.
{"title":"Autonomous unmanned aerial vehicle flight control using multi-task deep neural network for exploring indoor environments","authors":"Viet Duc Bui, T. Shirakawa, Hiroshi Sato","doi":"10.1080/18824889.2022.2087413","DOIUrl":"https://doi.org/10.1080/18824889.2022.2087413","url":null,"abstract":"In recent years, owing to the advance in image processing using deep learning, autonomous unmanned aerial vehicle (UAV) navigation based on image recognition has become possible. However, several image-based deep learning methods focus primarily on single-task autonomous UAV systems, which cannot perform other required tasks. Meanwhile, deep learning methods based on multi-task learning, which are suitable for multi-tasking autonomous UAV systems, have not been sufficiently researched. Therefore, in this study, we propose a UAV flight control method that can enable correction of a UAV's self-position, self-direction, and recognition/selection of multiple movement directions using multi-task learning for exploring an unknown indoor environment, which is based only on information from monocular camera images.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134619137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/18824889.2022.2155454
Junya Yamauchi, Makoto Saito, Marco Omainska, Takeshi Hatanaka, M. Fujita
This paper considers vision-based cooperative control for robotic networks pursuing a target object based on distributed Gaussian processes. We consider a situation where networked multiple robots are learning unknown motion of the target as a Gaussian process from different datasets. In this scene, some robots may lose sight of the target due to the limited field of view. To address the issue, we introduce a notion of time varying visibility set. Then, we propose a control law based on a distributed Gaussian process model, which is constructed from the Gaussian process model of each robot. By applying the proposed law to the error system, it is shown that the estimation and control errors are ultimately bounded with probability. Finally, the effectiveness of the proposed method is verified by simulation.
{"title":"Cooperative visual pursuit control with learning of target motion via distributed Gaussian processes under varying visibility","authors":"Junya Yamauchi, Makoto Saito, Marco Omainska, Takeshi Hatanaka, M. Fujita","doi":"10.1080/18824889.2022.2155454","DOIUrl":"https://doi.org/10.1080/18824889.2022.2155454","url":null,"abstract":"This paper considers vision-based cooperative control for robotic networks pursuing a target object based on distributed Gaussian processes. We consider a situation where networked multiple robots are learning unknown motion of the target as a Gaussian process from different datasets. In this scene, some robots may lose sight of the target due to the limited field of view. To address the issue, we introduce a notion of time varying visibility set. Then, we propose a control law based on a distributed Gaussian process model, which is constructed from the Gaussian process model of each robot. By applying the proposed law to the error system, it is shown that the estimation and control errors are ultimately bounded with probability. Finally, the effectiveness of the proposed method is verified by simulation.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128032217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/18824889.2022.2111037
Jinhui Yang, K. Hirata, Yukinori Nakamura, Kunihisa Okano, K. Katoh
In this paper, a model-based design problem of tube pumps are considered to achieve an ultra-low flow rate pulsation. Starting from the modelling of conventional tube pumps having two rollers, we clarify the fact that even the 1 degree of freedom (DoF) structure can realize a flat flow rate by adjusting the rotation speed. Then, it is shown that the 2 DoF setting reduces the amount of the required acceleration of the pushing roller. Equivalently, this can be achieved by a deceleration of the leaving roller. An alternative way to realize it with a constant rotation speed is to modify the wall curve profile at the leaving sector. Its modelling and an optimal design procedure based on this model are given. Finally, we propose a control strategy combining a quasi-optimal wall curve with slight speed adjustment at the bottom of the periodic operation cycle to achieve an almost pulsation-free flow rate.
{"title":"Model-based design of tube pumps with ultra-low flow rate pulsation","authors":"Jinhui Yang, K. Hirata, Yukinori Nakamura, Kunihisa Okano, K. Katoh","doi":"10.1080/18824889.2022.2111037","DOIUrl":"https://doi.org/10.1080/18824889.2022.2111037","url":null,"abstract":"In this paper, a model-based design problem of tube pumps are considered to achieve an ultra-low flow rate pulsation. Starting from the modelling of conventional tube pumps having two rollers, we clarify the fact that even the 1 degree of freedom (DoF) structure can realize a flat flow rate by adjusting the rotation speed. Then, it is shown that the 2 DoF setting reduces the amount of the required acceleration of the pushing roller. Equivalently, this can be achieved by a deceleration of the leaving roller. An alternative way to realize it with a constant rotation speed is to modify the wall curve profile at the leaving sector. Its modelling and an optimal design procedure based on this model are given. Finally, we propose a control strategy combining a quasi-optimal wall curve with slight speed adjustment at the bottom of the periodic operation cycle to achieve an almost pulsation-free flow rate.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124510010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/18824889.2022.2097402
Yasushi Iwatani, T. Takei
Rimless wheel robots are ground robots whose wheels have no rims or tires but only spokes. This paper considers torso-actuated wheel robots. Each robot considered in this paper is composed of a single wheel and a single torso connected to the wheel centre by an actuated revolute joint. There have been proposed several controllers for torso-actuated rimless wheel robots; however, their locomotion properties are poorly understood. For example, similarities and differences between the rimmed and the rimless wheel robots are unknown. This paper first investigates steady locomotion of a torso-actuated rimmed wheel robot theoretically. Then, steady locomotion of a torso-actuated rimless wheel robot is evaluated for various parameters by numerical simulations. Finally, similarities and differences between the rimmed and the rimless wheel robots are discussed. In particular, it is shown that the speed-maximizing posture is substantially shared among the rimmed and the rimless wheel robots. On the other hand, some other properties are not shared among them. The most significant difference is the relationship between the maximum steady speed and the wheel radius. It is concave in some parameter space for the rimless wheel robot, but is linear in the whole parameter space for the rimmed wheel robot.
{"title":"Steady locomotion in torso-actuated rimless wheel robots","authors":"Yasushi Iwatani, T. Takei","doi":"10.1080/18824889.2022.2097402","DOIUrl":"https://doi.org/10.1080/18824889.2022.2097402","url":null,"abstract":"Rimless wheel robots are ground robots whose wheels have no rims or tires but only spokes. This paper considers torso-actuated wheel robots. Each robot considered in this paper is composed of a single wheel and a single torso connected to the wheel centre by an actuated revolute joint. There have been proposed several controllers for torso-actuated rimless wheel robots; however, their locomotion properties are poorly understood. For example, similarities and differences between the rimmed and the rimless wheel robots are unknown. This paper first investigates steady locomotion of a torso-actuated rimmed wheel robot theoretically. Then, steady locomotion of a torso-actuated rimless wheel robot is evaluated for various parameters by numerical simulations. Finally, similarities and differences between the rimmed and the rimless wheel robots are discussed. In particular, it is shown that the speed-maximizing posture is substantially shared among the rimmed and the rimless wheel robots. On the other hand, some other properties are not shared among them. The most significant difference is the relationship between the maximum steady speed and the wheel radius. It is concave in some parameter space for the rimless wheel robot, but is linear in the whole parameter space for the rimmed wheel robot.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134624353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/18824889.2022.2143634
Atsede G. Gebremedhin, Y. Fujisaki
This paper presents a distributed model reference adaptive control scheme for optimal tracking of an interconnected dynamical system in the presence of system/interconnection uncertainties. A reference model selection which achieves an optimal tracking for the nominal system is introduced by using linear quadratic regulator theory. Then an adaptive control law is developed for the uncertain interconnected dynamical system, where it employs the specified reference model. It is shown that the control law achieves the desired behaviour such that the output of the system asymptotically tracks the output of the reference model in the presence of the uncertainties. An explicit error bound regarding optimal tracking is also established.
{"title":"Distributed adaptive control for optimal tracking of uncertain interconnected dynamical systems","authors":"Atsede G. Gebremedhin, Y. Fujisaki","doi":"10.1080/18824889.2022.2143634","DOIUrl":"https://doi.org/10.1080/18824889.2022.2143634","url":null,"abstract":"This paper presents a distributed model reference adaptive control scheme for optimal tracking of an interconnected dynamical system in the presence of system/interconnection uncertainties. A reference model selection which achieves an optimal tracking for the nominal system is introduced by using linear quadratic regulator theory. Then an adaptive control law is developed for the uncertain interconnected dynamical system, where it employs the specified reference model. It is shown that the control law achieves the desired behaviour such that the output of the system asymptotically tracks the output of the reference model in the presence of the uncertainties. An explicit error bound regarding optimal tracking is also established.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/18824889.2022.2104006
Masaki Kanai, M. Yamakita
This study proposes a linear Model Predictive Control (MPC) method that combines high prediction accuracy with low computational cost, using a lifted bilinear model based on Koopman theory. In recent years, there has been a growing interest in approaches to learning prediction models that determine the performance of MPC through lifting linearization and lifting bilinearization based on Koopman theory. In these methods, a linear or bilinear model reflecting nonlinear characteristics of the target system can be obtained by lifting the states to a higher dimensional space with observable functions (observables). In particular, lifting bilinearization provides more accurate models for the nonlinear input affine system, but when combined with MPC, it requires solving a nonlinear optimization problem, which is computationally expensive. Therefore, in this study, we formulate a linear MPC using a lifted bilinear model that can make accurate predictions for the input affine system, thereby realizing an MPC algorithm with high accuracy and low computational cost. In the proposed method, we first formulate a prediction error correction method for the lifted bilinear model by introducing constraints based on the observables and corrective inputs. Furthermore, we propose a linear MPC that can make accurate predictions using the lifted bilinear model by utilizing prior-predicted states based on the optimal solution at the previous time. We evaluate the effectiveness of the proposed method through numerical simulations using a planar drone.
{"title":"Linear model predictive control with lifted bilinear models by Koopman-based approach","authors":"Masaki Kanai, M. Yamakita","doi":"10.1080/18824889.2022.2104006","DOIUrl":"https://doi.org/10.1080/18824889.2022.2104006","url":null,"abstract":"This study proposes a linear Model Predictive Control (MPC) method that combines high prediction accuracy with low computational cost, using a lifted bilinear model based on Koopman theory. In recent years, there has been a growing interest in approaches to learning prediction models that determine the performance of MPC through lifting linearization and lifting bilinearization based on Koopman theory. In these methods, a linear or bilinear model reflecting nonlinear characteristics of the target system can be obtained by lifting the states to a higher dimensional space with observable functions (observables). In particular, lifting bilinearization provides more accurate models for the nonlinear input affine system, but when combined with MPC, it requires solving a nonlinear optimization problem, which is computationally expensive. Therefore, in this study, we formulate a linear MPC using a lifted bilinear model that can make accurate predictions for the input affine system, thereby realizing an MPC algorithm with high accuracy and low computational cost. In the proposed method, we first formulate a prediction error correction method for the lifted bilinear model by introducing constraints based on the observables and corrective inputs. Furthermore, we propose a linear MPC that can make accurate predictions using the lifted bilinear model by utilizing prior-predicted states based on the optimal solution at the previous time. We evaluate the effectiveness of the proposed method through numerical simulations using a planar drone.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131637330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates distributed online assignment of charging stations for a drone network in a persistent coverage control task. To ensure persistency not only in motion but also in energy, drones need to go back to charging stations before running out of their batteries. Coverage control schemes with energy persistency were presented in the literature based on so-called control barrier functions. These methodologies, however, assume a fixed correspondence between a drone and a charging station, but always returning to a preassigned station is not necessarily an efficient decision, namely the constraint may hinder the monitoring behaviour of the drones. Dynamically reassigning charging stations to drones is thus expected to enhance the coverage performance. To this end, we formulate an online assignment problem of charging stations with parameters determined by the control barrier function values in real time, and exactly relax the formulated optimization problem to a linear programming problem. We then propose a distributed solution to the problem based on ADMM and the overall partially distributed control architecture including persistent coverage control and online assignment of charging stations. The control system is finally demonstrated through Monte Carlo simulation.
{"title":"Distributed online assignment of charging stations in persistent coverage control tasks based on LP relaxation and ADMM","authors":"Zhiyuan Lu, Shunya Yamashita, Junya Yamauchi, Takeshi Hatanaka","doi":"10.1080/18824889.2022.2125246","DOIUrl":"https://doi.org/10.1080/18824889.2022.2125246","url":null,"abstract":"This paper investigates distributed online assignment of charging stations for a drone network in a persistent coverage control task. To ensure persistency not only in motion but also in energy, drones need to go back to charging stations before running out of their batteries. Coverage control schemes with energy persistency were presented in the literature based on so-called control barrier functions. These methodologies, however, assume a fixed correspondence between a drone and a charging station, but always returning to a preassigned station is not necessarily an efficient decision, namely the constraint may hinder the monitoring behaviour of the drones. Dynamically reassigning charging stations to drones is thus expected to enhance the coverage performance. To this end, we formulate an online assignment problem of charging stations with parameters determined by the control barrier function values in real time, and exactly relax the formulated optimization problem to a linear programming problem. We then propose a distributed solution to the problem based on ADMM and the overall partially distributed control architecture including persistent coverage control and online assignment of charging stations. The control system is finally demonstrated through Monte Carlo simulation.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116678551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-14DOI: 10.1080/18824889.2022.2077588
M. Kubo, Hiroshi Sato, Nhuhai Phung, A. Yamaguchi
The best-of-n problem [Valentini G, Ferrante E, Dorigo M. The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives. Frontiers in Robotics and AI. 2017;4(9):1–18.] is a collective decision-making problem in which many robots (agents) select the best option among a set of n alternatives and is focused on the field of distributed autonomous robotic systems and swarm robotics. It is desirable to develop a collective decision-making algorithm that can work even when there are a lot of social–behavioural alternatives (n>2) to realize an intelligent system that can solve more complicated problems. However, previous studies mainly focused on binary collective decision-making scenarios (n = 2). In this paper, we propose a collective decision-making algorithm for the best-of-n problem with a large number of options by using short-term experience memory with a trial and error approach at the group level. After proposing this decision-making process, we show typical behaviour. Next, we show the convergence of this algorithm when a quadratic function is used for the bias corresponding to the individual characteristic. Next, the bias distribution proposed shows that an equilibrium point where all candidates have the same number of supports is unstable, and consensus states are a stable fixed point. Therefore, dynamics is expected to converge towards a consensus. Simulation results and mathematical analysis show that the average time required to find the best option is nearly proportional to the number of options and does not depend on the number of robots.
n个最优问题[Valentini G, Ferrante E, Dorigo M.]机器人群中的n个最优问题:形式化、技术状态和新视角。机器人和人工智能的前沿。2017; 4(9):队。是一个集体决策问题，其中许多机器人(代理)从一组n个备选方案中选择最佳方案，主要关注分布式自主机器人系统和群体机器人领域。开发一种即使在存在大量社会行为选择(n>2)时也能工作的集体决策算法，以实现可以解决更复杂问题的智能系统是可取的。然而，以往的研究主要集中在二元集体决策场景(n = 2)。在本文中，我们提出了一种集体决策算法，通过在群体层面上使用短期经验记忆和试错方法来解决具有大量选项的n最佳问题。在提出这个决策过程后，我们表现出典型的行为。接下来，我们展示了当一个二次函数用于对应于个体特征的偏差时，该算法的收敛性。其次，提出的偏差分布表明，所有候选者拥有相同支持数的平衡点是不稳定的，共识状态是稳定的不动点。因此，动态有望趋同于一个共识。仿真结果和数学分析表明，寻找最佳选项所需的平均时间与选项数量几乎成正比，而与机器人数量无关。
{"title":"Collective decision-making algorithm for the best-of-n problem in multiple options","authors":"M. Kubo, Hiroshi Sato, Nhuhai Phung, A. Yamaguchi","doi":"10.1080/18824889.2022.2077588","DOIUrl":"https://doi.org/10.1080/18824889.2022.2077588","url":null,"abstract":"The best-of-n problem [Valentini G, Ferrante E, Dorigo M. The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives. Frontiers in Robotics and AI. 2017;4(9):1–18.] is a collective decision-making problem in which many robots (agents) select the best option among a set of n alternatives and is focused on the field of distributed autonomous robotic systems and swarm robotics. It is desirable to develop a collective decision-making algorithm that can work even when there are a lot of social–behavioural alternatives (n>2) to realize an intelligent system that can solve more complicated problems. However, previous studies mainly focused on binary collective decision-making scenarios (n = 2). In this paper, we propose a collective decision-making algorithm for the best-of-n problem with a large number of options by using short-term experience memory with a trial and error approach at the group level. After proposing this decision-making process, we show typical behaviour. Next, we show the convergence of this algorithm when a quadratic function is used for the bias corresponding to the individual characteristic. Next, the bias distribution proposed shows that an equilibrium point where all candidates have the same number of supports is unstable, and consensus states are a stable fixed point. Therefore, dynamics is expected to converge towards a consensus. Simulation results and mathematical analysis show that the average time required to find the best option is nearly proportional to the number of options and does not depend on the number of robots.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116080750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a creation method of comprehensive cases and specifications for hardware and software combined test (HSCT) to detect undesirable events of an industrial product controlled by software using hazard and operability studies (HAZOP). By testing with created HSCT specifications, the proposed method detects undesirable events of the industrial product. To remove undesirable events in industrial products, it is important to confirm the behaviour of as many undesirable events as possible in HSCT. However, since the HSCT cases were manually created by engineers, the coverage of the HSCT cases was insufficient. This paper proposes a comprehensive creation method of HSCT cases and specifications to resolve the problem of insufficient coverage of HSCT cases. The following countermeasures are implemented to realize the proposed method. (1) Define the method for extracting the parameters used in HAZOP from the hardware and software specifications, and define the information necessary to create HSCT cases and specifications. (2) Prepare HAZOP guide words for HSCT. (3) Define the format of parameters and guide words and the procedure for creating HSCT cases by combining parameters and guide words. (4) Propose a creation procedure of HSCT specifications corresponding to the HSCT cases. (5) Propose an adequate HSCT procedure that includes methods (1)–(4). As a result of the application of the proposed method, 25% more adequate HSCT cases are created and 40% more undesirable events were detected in comparison with the manual creation. Additionally, the creation time was reduced by 27%.
{"title":"A creation method of comprehensive cases and specifications for hardware and software combined test to detect undesirable events of an industrial product using HAZOP","authors":"Masakazu Takahashi, Kouji Ueno, Yunarso Anang, Yoshimichi Watanabe","doi":"10.1080/18824889.2022.2069914","DOIUrl":"https://doi.org/10.1080/18824889.2022.2069914","url":null,"abstract":"This paper proposes a creation method of comprehensive cases and specifications for hardware and software combined test (HSCT) to detect undesirable events of an industrial product controlled by software using hazard and operability studies (HAZOP). By testing with created HSCT specifications, the proposed method detects undesirable events of the industrial product. To remove undesirable events in industrial products, it is important to confirm the behaviour of as many undesirable events as possible in HSCT. However, since the HSCT cases were manually created by engineers, the coverage of the HSCT cases was insufficient. This paper proposes a comprehensive creation method of HSCT cases and specifications to resolve the problem of insufficient coverage of HSCT cases. The following countermeasures are implemented to realize the proposed method. (1) Define the method for extracting the parameters used in HAZOP from the hardware and software specifications, and define the information necessary to create HSCT cases and specifications. (2) Prepare HAZOP guide words for HSCT. (3) Define the format of parameters and guide words and the procedure for creating HSCT cases by combining parameters and guide words. (4) Propose a creation procedure of HSCT specifications corresponding to the HSCT cases. (5) Propose an adequate HSCT procedure that includes methods (1)–(4). As a result of the application of the proposed method, 25% more adequate HSCT cases are created and 40% more undesirable events were detected in comparison with the manual creation. Additionally, the creation time was reduced by 27%.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124503820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-18DOI: 10.1080/18824889.2022.2069652
S. Masuda, Hiroshi Ito
The Society of Instrument and Control Engineers (SICE) annually organizes an international conference covering a broad range of fields from measurement and control to system analysis and design, from theory to application, and from software to hardware. In 2021, the SICE annual conference was held on 8–10 September in an online format due to COVID-19. The technical programme consisted of five plenary talks including one sponsored talk, technical sessions, one tutorial, and three workshops. The technical sessions included 9 special sessions (2 special panel sessions), and 61 traditional general sessions (24 organized sessions and 37 regular sessions). There were 298 presentations from 12 countries including Japan, China, South Korea, Thailand, Singapore, Taiwan, Slovenia, India, Indonesia, Italy, Malaysia, and Afghanistan. It was successfully held with more than 490 participants. This special issue of SICE JCMSI was planned to gather and disseminate high-quality researches based on extensions of the results presented as Regular Papers in the SICE Annual Conference 2021, and improved expositions of the high-quality results presented as Position Papers in the conference. As a result, 22 papers were submitted to this special issue. Several papers which were not produced in time will appear in regular issues. This special issue received papers from a broad range of research fields covering measurement, control, system integration, system and information, industrial applications, and life engineering. The studies reported in this issue range from algorithmic developments to experimental achievements. The tackled problems include optimization, learning, modelling, identification, image estimation, nondestructive inner inspection, adaptive control, data-driven control, swarm robotics, chemical plants, high-mobility robots, concrete structures, quantum computation, and many other theoretical and practical challenges. Reading the papers in this special issue, you can explore some of the interesting and inspiring trends in the research fields promoted by the SICE.
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