Sina Dehghan, Tiebiao Zhao, Y. Chen, Taymaz Homayouni
RIOTS is a Matlab toolbox capable of solving a very general form of integer order optimal control problems. In this paper, we present an approach for implementing Model Predictive Control (MPC) to control a general form of fractional order systems using RIOTS toolbox. This approach is based on time-response-invariant approximation of fractional order system with an integer order model to be used as the internal model in MPC. The implementation of this approach is demonstrated to control a coupled MIMO commensurate fractional order model. Moreover, the performance and its application process is compared to examples reported in the literature.
{"title":"Model Predictive Control of General Fractional Order Systems Using a General Purpose Optimal Control Problem Solver: RIOTS","authors":"Sina Dehghan, Tiebiao Zhao, Y. Chen, Taymaz Homayouni","doi":"10.1115/detc2019-97489","DOIUrl":"https://doi.org/10.1115/detc2019-97489","url":null,"abstract":"\u0000 RIOTS is a Matlab toolbox capable of solving a very general form of integer order optimal control problems. In this paper, we present an approach for implementing Model Predictive Control (MPC) to control a general form of fractional order systems using RIOTS toolbox. This approach is based on time-response-invariant approximation of fractional order system with an integer order model to be used as the internal model in MPC. The implementation of this approach is demonstrated to control a coupled MIMO commensurate fractional order model. Moreover, the performance and its application process is compared to examples reported in the literature.","PeriodicalId":166402,"journal":{"name":"Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025923","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}
A new fractional-order current-controlled memristor is proposed by the fact of the memory loss. Excited by sinusoidal current, the generalized hysteresis loops of the new fractional-order memristor are no longer symmetrical to the origin and the time to reach the steady state is longer than the integer-order memristor’s. The dynamical behaviors of a new fractional-order memristive circuit system whose state variables have different derivation orders are investigated by theoretical analyses and simulated numerically. It is shown that the new fractional-order memristive circuit system goes into chaos by period-doubling bifurcation; the periodic windows are induced by the discontinuous change of derivative order between variables.
{"title":"Periodic-Doubling Bifurcation of a Circuit With a Fractional-Order Memristor","authors":"Y. Yu, Y. Chen","doi":"10.1115/detc2019-98178","DOIUrl":"https://doi.org/10.1115/detc2019-98178","url":null,"abstract":"\u0000 A new fractional-order current-controlled memristor is proposed by the fact of the memory loss. Excited by sinusoidal current, the generalized hysteresis loops of the new fractional-order memristor are no longer symmetrical to the origin and the time to reach the steady state is longer than the integer-order memristor’s. The dynamical behaviors of a new fractional-order memristive circuit system whose state variables have different derivation orders are investigated by theoretical analyses and simulated numerically. It is shown that the new fractional-order memristive circuit system goes into chaos by period-doubling bifurcation; the periodic windows are induced by the discontinuous change of derivative order between variables.","PeriodicalId":166402,"journal":{"name":"Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535494","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}
When controlling complex non-linear systems, classic flat-phase specification (FPS) method for tuning fractional order controllers employs graphic method. However, following this step of graphic method, the tuning method cannot work automatically. In this study, a novel optimization method is employed to enable it to work automatically. An approximation is used to avoid solving derivatives, thereby simplify computation of the method. Frequency-domain analysis reveals that, compared with the classic FPS method, this method is capable of covering more conditions, especially those with larger phase margin. A linear model and a non-linear model (Simscape) are used to demonstrate that the proposed method can ensure both transient performance and robustness. For the relevant working folder, please refer to: http://bit.ly/npm-simscape-code. For video demonstrations, please click: http://bit.ly/npm_simscape_video.
{"title":"Neighborhood Optimization Method for Shaping Bode Plot With Larger Phase Margin","authors":"Bo Shang, Chengdong Wu, Y. Chen","doi":"10.1115/detc2019-97288","DOIUrl":"https://doi.org/10.1115/detc2019-97288","url":null,"abstract":"\u0000 When controlling complex non-linear systems, classic flat-phase specification (FPS) method for tuning fractional order controllers employs graphic method. However, following this step of graphic method, the tuning method cannot work automatically. In this study, a novel optimization method is employed to enable it to work automatically. An approximation is used to avoid solving derivatives, thereby simplify computation of the method. Frequency-domain analysis reveals that, compared with the classic FPS method, this method is capable of covering more conditions, especially those with larger phase margin. A linear model and a non-linear model (Simscape) are used to demonstrate that the proposed method can ensure both transient performance and robustness. For the relevant working folder, please refer to: http://bit.ly/npm-simscape-code. For video demonstrations, please click: http://bit.ly/npm_simscape_video.","PeriodicalId":166402,"journal":{"name":"Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124641968","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}
The mushroom cultivation is an important smart agriculture in Taiwan. This study uses the deep learning object detection method to inspect the cap flaws or positional imperfection in the automatic production of the mushroom PP-bag packaging. This study uses the UR robotic arm and integrated 3D vision module, and uses the extra positioning axis to achieve the purpose of multi-positioning inspections by robot arm. Projecting the structured LED light sources to the object to be inspected has the advantages of a larger identification ranges and complex objects detection. A duallens CMOS industrial camera is used to capture images, and a 3D point cloud image of a basket of PP-bag packages is created by software calculation, which can obtain detailed information on the appearance of the whole basket of PP-bag packages. Deep learning is performed by the training set with labelling, and the image recognition such as the cap flaws in the PP-bag package or positional shift is performed after the training is completed. In this paper, the image data is divided into four sets of datasets, and the same training parameters are used for individual training. With images of dataset1 and the ambient illumination level of 200 lm to 800 lm, the matching score is up to 0.989. The clamping force and the opening degree are adjusted by the variable jaws. The clamping force of the jaws is maintained at 20 N to prevent the clamping force from damaging the dimensions of the PP-bag package and existing holes inside it, making the product unusable. Using the variable jaws and repeating 30 times of clamping experiments, the hole diameter inside the PP-bag package can still be maintained within around 25 mm, which can meet the needs of the mushroom PP-bag packaging.
{"title":"Deep Learning Automatic Inspections of Mushroom Substrate Packaging for PP-Bag Cultivations","authors":"R. Jou, Tseng-Wei Li","doi":"10.1115/detc2019-97011","DOIUrl":"https://doi.org/10.1115/detc2019-97011","url":null,"abstract":"\u0000 The mushroom cultivation is an important smart agriculture in Taiwan. This study uses the deep learning object detection method to inspect the cap flaws or positional imperfection in the automatic production of the mushroom PP-bag packaging. This study uses the UR robotic arm and integrated 3D vision module, and uses the extra positioning axis to achieve the purpose of multi-positioning inspections by robot arm. Projecting the structured LED light sources to the object to be inspected has the advantages of a larger identification ranges and complex objects detection. A duallens CMOS industrial camera is used to capture images, and a 3D point cloud image of a basket of PP-bag packages is created by software calculation, which can obtain detailed information on the appearance of the whole basket of PP-bag packages. Deep learning is performed by the training set with labelling, and the image recognition such as the cap flaws in the PP-bag package or positional shift is performed after the training is completed. In this paper, the image data is divided into four sets of datasets, and the same training parameters are used for individual training. With images of dataset1 and the ambient illumination level of 200 lm to 800 lm, the matching score is up to 0.989. The clamping force and the opening degree are adjusted by the variable jaws. The clamping force of the jaws is maintained at 20 N to prevent the clamping force from damaging the dimensions of the PP-bag package and existing holes inside it, making the product unusable. Using the variable jaws and repeating 30 times of clamping experiments, the hole diameter inside the PP-bag package can still be maintained within around 25 mm, which can meet the needs of the mushroom PP-bag packaging.","PeriodicalId":166402,"journal":{"name":"Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130217595","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}
Discrete fractional order systems have attracted more and more attention in recent years. Nabla Laplace transform is an important tool to deal with the problem of nabla discrete fractional order systems, but there is still much room for its development. In this paper, 14 lemmas are listed to conclude the existing properties and 14 theorems are developed to describe the innovative features. On one hand, these properties make the Ntransform more effective and efficient. On the other hand, they enrich the discrete fractional order system theory.
{"title":"Some Fundamental Properties on the Sampling Free Nabla Laplace Transform","authors":"Yiheng Wei, Yuquan Chen, Yong Wang, Yang Chen","doi":"10.1115/DETC2019-97351","DOIUrl":"https://doi.org/10.1115/DETC2019-97351","url":null,"abstract":"\u0000 Discrete fractional order systems have attracted more and more attention in recent years. Nabla Laplace transform is an important tool to deal with the problem of nabla discrete fractional order systems, but there is still much room for its development. In this paper, 14 lemmas are listed to conclude the existing properties and 14 theorems are developed to describe the innovative features. On one hand, these properties make the Ntransform more effective and efficient. On the other hand, they enrich the discrete fractional order system theory.","PeriodicalId":166402,"journal":{"name":"Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522708","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}
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