Pub Date : 2019-05-01DOI: 10.1109/DDCLS.2019.8908851
Chunyu Yang, Jianguo Zhao, Shanshan Zhong, Linna Zhou
In this note, the infinite horizon optimal output tracking control problem for two-time-scale systems is investigated. The problem is transformed into an optimal regulator problem of the augmented system which is constructed based on the command generator and the original system. The adaptive dynamics programming technique is utilized to learn the optimal solution in real time without relying on the knowledge of system dynamics. By the structured cost function parameter matrix for a full-order model, ill-conditioned numerical issue of two-time-scale systems is overcome. The proposed algorithm has a rigorous convergence proof. Finally, a DC system is given to show the feasibility of the proposed scheme by simulation.
{"title":"Adaptive Optimal Output Tracking Control of Completely Unknown Linear Two-Time-Scale Systems","authors":"Chunyu Yang, Jianguo Zhao, Shanshan Zhong, Linna Zhou","doi":"10.1109/DDCLS.2019.8908851","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908851","url":null,"abstract":"In this note, the infinite horizon optimal output tracking control problem for two-time-scale systems is investigated. The problem is transformed into an optimal regulator problem of the augmented system which is constructed based on the command generator and the original system. The adaptive dynamics programming technique is utilized to learn the optimal solution in real time without relying on the knowledge of system dynamics. By the structured cost function parameter matrix for a full-order model, ill-conditioned numerical issue of two-time-scale systems is overcome. The proposed algorithm has a rigorous convergence proof. Finally, a DC system is given to show the feasibility of the proposed scheme by simulation.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"19 1","pages":"494-498"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73931214","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}
Industrial process data has the characteristics of complexity, variability and noisy, which brings challenges to data-driven production predictive modeling for industrial processes basing on the traditional extreme learning machine (ELM). Therefore, this paper proposes an improved ELM based on auto-encoder (AE) (AE-ELM). The AE can extract the main features with lower-dimension by eliminating the linear correlation among the original complex data. Then, the main features are used as the inputs of the ELM. For the purpose of verifying the effectiveness of the proposed method, the AE-ELM model has been experimented on the production prediction of the pure terephthalic acid (PTA). The experimental results prove that the AE-ELM is less sensitive to the structure of the traditional ELM and principal components extraction based robust ELM (PCE-RELM). Moreover, the modeling accuracy can be improved by 2.4%, which has certain guiding significance for process modeling and production prediction.
{"title":"An Improved Extreme Learning Machine Based on Auto-Encoder for Production Predictive Modeling of Industrial Processes","authors":"Zhiqiang Geng, Qingchao Meng, Yongming Han, Qin Wei, Zhi Ouyang","doi":"10.1109/DDCLS.2019.8908949","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908949","url":null,"abstract":"Industrial process data has the characteristics of complexity, variability and noisy, which brings challenges to data-driven production predictive modeling for industrial processes basing on the traditional extreme learning machine (ELM). Therefore, this paper proposes an improved ELM based on auto-encoder (AE) (AE-ELM). The AE can extract the main features with lower-dimension by eliminating the linear correlation among the original complex data. Then, the main features are used as the inputs of the ELM. For the purpose of verifying the effectiveness of the proposed method, the AE-ELM model has been experimented on the production prediction of the pure terephthalic acid (PTA). The experimental results prove that the AE-ELM is less sensitive to the structure of the traditional ELM and principal components extraction based robust ELM (PCE-RELM). Moreover, the modeling accuracy can be improved by 2.4%, which has certain guiding significance for process modeling and production prediction.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"2013 1","pages":"708-712"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74078931","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908857
Mingdong Hou, Yinsong Wang
For a class of discrete-time nonlinear systems with disturbances, a data-driven discrete-time fractional-order terminal sliding mode control (DD-DFOTSMC) method is proposed in this paper. The algorithm is based on the compact form dynamic linearization (CFDL) technique, and the controller is designed based on the discrete terminal sliding mode technology and the Grünwald-Letnikov fractional-order definition. The parameter of the CFDL data model is called pseudo-partial derivative (PPD) and is estimated using merely I/O measurement data of the system. Theoretical analysis proves the stability of the proposed algorithm, and simulation studies demonstrate that the proposed method has higher precision and faster response speed. Finally, the effectiveness of the proposed method is validated through a continuous stirred tank reactor (CSTR) process.
{"title":"A Data Driven Fractional-order Terminal Sliding Mode Control Method","authors":"Mingdong Hou, Yinsong Wang","doi":"10.1109/DDCLS.2019.8908857","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908857","url":null,"abstract":"For a class of discrete-time nonlinear systems with disturbances, a data-driven discrete-time fractional-order terminal sliding mode control (DD-DFOTSMC) method is proposed in this paper. The algorithm is based on the compact form dynamic linearization (CFDL) technique, and the controller is designed based on the discrete terminal sliding mode technology and the Grünwald-Letnikov fractional-order definition. The parameter of the CFDL data model is called pseudo-partial derivative (PPD) and is estimated using merely I/O measurement data of the system. Theoretical analysis proves the stability of the proposed algorithm, and simulation studies demonstrate that the proposed method has higher precision and faster response speed. Finally, the effectiveness of the proposed method is validated through a continuous stirred tank reactor (CSTR) process.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"8 1","pages":"42-46"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74743651","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 enterprise workshop production site is full of complex information and in complicated situation, so the traditional monitoring system cannot meet the demands of the information interaction between workshop management and operational levels. In order to solve these problems, this paper is about to build virtual models that accurately map workshop resources and a virtual monitoring system that reflects the production status in real time which can help the workshop managers to timely and roundly monitor the manufacturing resources such as the workshop equipment and production status. The system builds the data logic model of the workshop manufacturing resources based on the 3D workshop model, which adopts Thrift framework to establish the data interaction between the virtual workshop layer and bottom layer, and uses the real-time production data to drive the 3D virtual model. Then the system renders the virtual scene and builds a human-computer interaction interface on the Unity3D platform. Finally, this paper takes a workshop system as an example to establish a virtual monitoring system integrating VR and AR technology, and verifies the effectiveness of the system.
{"title":"Development and Application of Workshop Virtual Monitoring System Based on Unity","authors":"Luyao Xia, Jianfeng Lu, Chenling Zhang, Sheng Wang, Hao Zhang","doi":"10.1109/DDCLS.2019.8908847","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908847","url":null,"abstract":"The enterprise workshop production site is full of complex information and in complicated situation, so the traditional monitoring system cannot meet the demands of the information interaction between workshop management and operational levels. In order to solve these problems, this paper is about to build virtual models that accurately map workshop resources and a virtual monitoring system that reflects the production status in real time which can help the workshop managers to timely and roundly monitor the manufacturing resources such as the workshop equipment and production status. The system builds the data logic model of the workshop manufacturing resources based on the 3D workshop model, which adopts Thrift framework to establish the data interaction between the virtual workshop layer and bottom layer, and uses the real-time production data to drive the 3D virtual model. Then the system renders the virtual scene and builds a human-computer interaction interface on the Unity3D platform. Finally, this paper takes a workshop system as an example to establish a virtual monitoring system integrating VR and AR technology, and verifies the effectiveness of the system.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"13 1","pages":"928-933"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765452","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909070
Y. Lv, X. Ren, Shuangyi Hu, Linwei Li, J. Na
The data-driven optimal tracking controls (OTC) for the unknown multi-input system are proposed in this paper, and a novel tuning law is used to update NN weights in the learning scheme. First, the formula of the OTC for the multi-input NZS game is presented. A three-layer neural network (NN) data-driven model is introduced to approximate the unknown system, and the input dynamics are obtained. Then, to solve the OTC as a regulation optimal problem, an augmentation multi-input system is constructed with the tracking error and command trajectory. Moreover, we use a reinforcement learning based data-driven NN method to online learn the optimal value functions for each input, which is directly used to calculate the optimal tracking control associated with each performance index function. The convergence of the NN weights is proved. Finally, a simulation is presented to verify the feasibility of our algorithm in this paper.
{"title":"Data-Driven Tracking Controls of Multi-input Augmented Systems Based on ADP Algorithm","authors":"Y. Lv, X. Ren, Shuangyi Hu, Linwei Li, J. Na","doi":"10.1109/DDCLS.2019.8909070","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909070","url":null,"abstract":"The data-driven optimal tracking controls (OTC) for the unknown multi-input system are proposed in this paper, and a novel tuning law is used to update NN weights in the learning scheme. First, the formula of the OTC for the multi-input NZS game is presented. A three-layer neural network (NN) data-driven model is introduced to approximate the unknown system, and the input dynamics are obtained. Then, to solve the OTC as a regulation optimal problem, an augmentation multi-input system is constructed with the tracking error and command trajectory. Moreover, we use a reinforcement learning based data-driven NN method to online learn the optimal value functions for each input, which is directly used to calculate the optimal tracking control associated with each performance index function. The convergence of the NN weights is proved. Finally, a simulation is presented to verify the feasibility of our algorithm in this paper.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"54 1","pages":"534-538"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80157444","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908859
Lingwei Wu, P. Mei, Z. Lin, Na Su
This paper presents a discrete multi-periodic repetitive control design approach for the problem of a general multi-periodic disturbance rejection. Both the nonlinear-saturation function and the measure of multi-periodic disturbance rejection are suggested to form an attracting law (AL), and by which an multi-periodic repetitive controller is developed. The multi-periodic disturbances are rejected, and the perfect tracking is achieved. In order to characterize the tracking performance, the absolute attractive layer and the steady-state error band are derived. Simulation results are given to verify the effectiveness and superiority of the proposed method.
{"title":"Attracting Law Based Discrete Multi-Periodic Repetitive Control","authors":"Lingwei Wu, P. Mei, Z. Lin, Na Su","doi":"10.1109/DDCLS.2019.8908859","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908859","url":null,"abstract":"This paper presents a discrete multi-periodic repetitive control design approach for the problem of a general multi-periodic disturbance rejection. Both the nonlinear-saturation function and the measure of multi-periodic disturbance rejection are suggested to form an attracting law (AL), and by which an multi-periodic repetitive controller is developed. The multi-periodic disturbances are rejected, and the perfect tracking is achieved. In order to characterize the tracking performance, the absolute attractive layer and the steady-state error band are derived. Simulation results are given to verify the effectiveness and superiority of the proposed method.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"35 1","pages":"889-894"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79004756","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908898
Dongwei He, Fengling Hu, Sheng Li, Xiongxiong He, Liping Chang, Ni Zhang, Qianru Jiang, Zhongchao Wang
A novel automatic polyp recognition scheme called Annular Spatial Pyramid Matching (ASPM) with Locality-Constrained Linear Coding (LLC) is proposed by considering the annular structure of the intestinal images at multilevel. Firstly, detailed texture features extracted from the samples including normal and polyp images are calculated and then LLC method is employed on these features to obtain a sparse representation. Secondly, a strategy of annular region segmentation based on Spatial Pyramid Matching is proposed to improve the effectiveness of processing for intestinal images. Then, the final representation for each image is obtained by max-pooling the codes of features. Finally, SVM classifier is developed to carry out polyp images classification tasks. The experimental results indicate that the proposed algorithm outperforms the analysed state-of-the-art methods on the polyps recognition.
{"title":"Intestinal Polyps Recognition Based on Annular Spatial Pyramid Matching with Locality-Constrained Linear Coding for Gastroscopy Diagnosis","authors":"Dongwei He, Fengling Hu, Sheng Li, Xiongxiong He, Liping Chang, Ni Zhang, Qianru Jiang, Zhongchao Wang","doi":"10.1109/DDCLS.2019.8908898","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908898","url":null,"abstract":"A novel automatic polyp recognition scheme called Annular Spatial Pyramid Matching (ASPM) with Locality-Constrained Linear Coding (LLC) is proposed by considering the annular structure of the intestinal images at multilevel. Firstly, detailed texture features extracted from the samples including normal and polyp images are calculated and then LLC method is employed on these features to obtain a sparse representation. Secondly, a strategy of annular region segmentation based on Spatial Pyramid Matching is proposed to improve the effectiveness of processing for intestinal images. Then, the final representation for each image is obtained by max-pooling the codes of features. Finally, SVM classifier is developed to carry out polyp images classification tasks. The experimental results indicate that the proposed algorithm outperforms the analysed state-of-the-art methods on the polyps recognition.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"739 1","pages":"551-556"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76862970","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908977
Yuxin Wu, De-yuan Meng, Jingyao Zhang, L. Cheng
For social networks, the interactions among agents and the relative self-confidence of each agent compared with the effects of its neighbors generally change over time. This requires using time-varying signed digraphs to describe the opinion forming processes of agents, where the positive and negative edges can represent cooperations and antagonisms, respectively. In this paper, an improved opinion dynamics model instead of the conventional Laplacian-type model is exploited with allowance of the potential variation of relative self-confidence of each agent, which can be reflected by the diagonal dominance degree. It is shown that both the structural characteristics of social networks and the diagonal dominance degrees determine the opinion forming performances, and some sufficient conditions related to these two factors are proposed to establish the bipartite consensus and stability results of agents. Two simulation examples are provided to illustrate the obtained opinion forming behaviors.
{"title":"Analysis of Opinion Dynamics in Social Networks Subject to Time-Varying Topologies","authors":"Yuxin Wu, De-yuan Meng, Jingyao Zhang, L. Cheng","doi":"10.1109/DDCLS.2019.8908977","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908977","url":null,"abstract":"For social networks, the interactions among agents and the relative self-confidence of each agent compared with the effects of its neighbors generally change over time. This requires using time-varying signed digraphs to describe the opinion forming processes of agents, where the positive and negative edges can represent cooperations and antagonisms, respectively. In this paper, an improved opinion dynamics model instead of the conventional Laplacian-type model is exploited with allowance of the potential variation of relative self-confidence of each agent, which can be reflected by the diagonal dominance degree. It is shown that both the structural characteristics of social networks and the diagonal dominance degrees determine the opinion forming performances, and some sufficient conditions related to these two factors are proposed to establish the bipartite consensus and stability results of agents. Two simulation examples are provided to illustrate the obtained opinion forming behaviors.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"8 1","pages":"147-152"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81674174","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909011
Lixue Xu, Xiubo Wang, Xudong Wang
The application of deep learning sonar target detection is severely limited due to the small amount of sonar images, especially for submarine shipwreck. Aiming to overcome the over-fit of training problem and improve accuracy of detection, we proposed a method which combine deep generation networks and transfer learning for sonar shipwrecks detection. Specifically, in deep generation network, we used similarity measurement to improved optimization, which generate high quality fake image and laid the further foundation of data. Then, in transfer learning detection, we used multi-layer adaptation and multi-core MMD to fine-tune and frozen pre-trained model, prevent the problem of over-fit and improve the generalization and stability of the system. And we combined the methods of regional suggestion and regression for target detection to guarantee precision of detection. Finally, the contrast experiment of sonar shipwrecks is carried out the effectiveness of the proposed method.
{"title":"Shipwrecks Detection Based on Deep Generation Network and Transfer Learning with Small Amount of Sonar Images","authors":"Lixue Xu, Xiubo Wang, Xudong Wang","doi":"10.1109/DDCLS.2019.8909011","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909011","url":null,"abstract":"The application of deep learning sonar target detection is severely limited due to the small amount of sonar images, especially for submarine shipwreck. Aiming to overcome the over-fit of training problem and improve accuracy of detection, we proposed a method which combine deep generation networks and transfer learning for sonar shipwrecks detection. Specifically, in deep generation network, we used similarity measurement to improved optimization, which generate high quality fake image and laid the further foundation of data. Then, in transfer learning detection, we used multi-layer adaptation and multi-core MMD to fine-tune and frozen pre-trained model, prevent the problem of over-fit and improve the generalization and stability of the system. And we combined the methods of regional suggestion and regression for target detection to guarantee precision of detection. Finally, the contrast experiment of sonar shipwrecks is carried out the effectiveness of the proposed method.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"6 1","pages":"638-643"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86880161","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909029
Lei Liu, Yuqian Liu, Cunwu Han, Xiaoping Zhang
In this paper, the problem of unified optimization of upper stability bound $varepsilon^{ast}$ and tracking performance index $J^{ast}$ for singularly perturbed systems is considered. First, an optimal output tracking controller is given based on the method of minimum value principle, such that the original system achieves asymptotically stable and asymptotic tracking of the tracking system and the minimum value of quadratic performance index can be obtained. Furthermore, based on Nash game theory, an algorithm to optimize ($varepsilon^{ast}, J^{ast}$) simultaneously which transfers multi-objective problem into a single objective problem as well we determines the objective weights. Finally, one numerical example is given to illustrate the correctness and feasibility of the proposed results.
{"title":"Unified Optimization of Upper Stability Bound and Tracking Performance Index for Singularly Perturbed Systems","authors":"Lei Liu, Yuqian Liu, Cunwu Han, Xiaoping Zhang","doi":"10.1109/DDCLS.2019.8909029","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909029","url":null,"abstract":"In this paper, the problem of unified optimization of upper stability bound $varepsilon^{ast}$ and tracking performance index $J^{ast}$ for singularly perturbed systems is considered. First, an optimal output tracking controller is given based on the method of minimum value principle, such that the original system achieves asymptotically stable and asymptotic tracking of the tracking system and the minimum value of quadratic performance index can be obtained. Furthermore, based on Nash game theory, an algorithm to optimize ($varepsilon^{ast}, J^{ast}$) simultaneously which transfers multi-objective problem into a single objective problem as well we determines the objective weights. Finally, one numerical example is given to illustrate the correctness and feasibility of the proposed results.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"142-146"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82764368","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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