Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10054761
Yingxiang Li, Yingke Gao, Zhiwen Su, Shi-tao Chen, Longjun Liu
Optical flow algorithms based on deep learning have achieved excellent performance on multiple datasets, bringing new opportunity for optical flow estimation. Recurrent All-Pairs Field Transforms (RAFT) is one of the most powerful deep network based optical flow algorithms, but it is difficult to process in real time on the resource-limited embedded platform. In this paper, we propose RAFT-Lite by compressing the original RAFT model, which is more lightweight and suitable for hardware deployment. We further propose a hardware accelerating architecture on FPGA for RAFT-Lite, which provides an efficient scheduling strategy for the convolution in RAFT to achieve efficient pipeline and resource reuse. On Xilinx ZCU102 evaluation board, the accelerated hardware system can reach 10.4fps processing images with a resolution of 512*396, which is 6.8x of i7-10700@2.90GHz and 46x of ARM Cortex-A53@1.50GHz. Besides, the power consumption is 13.103W.
{"title":"FPGA Accelerated Real-time Recurrent All-Pairs Field Transforms for Optical Flow","authors":"Yingxiang Li, Yingke Gao, Zhiwen Su, Shi-tao Chen, Longjun Liu","doi":"10.1109/CAC57257.2022.10054761","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054761","url":null,"abstract":"Optical flow algorithms based on deep learning have achieved excellent performance on multiple datasets, bringing new opportunity for optical flow estimation. Recurrent All-Pairs Field Transforms (RAFT) is one of the most powerful deep network based optical flow algorithms, but it is difficult to process in real time on the resource-limited embedded platform. In this paper, we propose RAFT-Lite by compressing the original RAFT model, which is more lightweight and suitable for hardware deployment. We further propose a hardware accelerating architecture on FPGA for RAFT-Lite, which provides an efficient scheduling strategy for the convolution in RAFT to achieve efficient pipeline and resource reuse. On Xilinx ZCU102 evaluation board, the accelerated hardware system can reach 10.4fps processing images with a resolution of 512*396, which is 6.8x of i7-10700@2.90GHz and 46x of ARM Cortex-A53@1.50GHz. Besides, the power consumption is 13.103W.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122523566","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10055992
Jiaheng, L. Hou, Jiaqi Li
This paper presents the design of a simple underactuated finger mechanism. The finger has 1 degrees of actuation (DOAs) and 3 degrees of freedom (DOFs), and can perform adaptive grasping. The grasping methods of fingers are classified into three-phalanx and single-phalanx contacts. The working spaces of a single finger was analysed. In addition, the principle of virtual work was used to analyse the conditions of θ1 >90° and θ1 <90° in three-phalanx contact and perform static analysis of single-phalanx contact. The under-actuation and self-adaptability of the manipulator were verified through the Grab experiment, and the force in the grasping process was investigated to analyse the establishment of static equilibrium in different cases
{"title":"Design and Experiments of a Underactuated Finger","authors":"Jiaheng, L. Hou, Jiaqi Li","doi":"10.1109/CAC57257.2022.10055992","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055992","url":null,"abstract":"This paper presents the design of a simple underactuated finger mechanism. The finger has 1 degrees of actuation (DOAs) and 3 degrees of freedom (DOFs), and can perform adaptive grasping. The grasping methods of fingers are classified into three-phalanx and single-phalanx contacts. The working spaces of a single finger was analysed. In addition, the principle of virtual work was used to analyse the conditions of θ1 >90° and θ1 <90° in three-phalanx contact and perform static analysis of single-phalanx contact. The under-actuation and self-adaptability of the manipulator were verified through the Grab experiment, and the force in the grasping process was investigated to analyse the establishment of static equilibrium in different cases","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122532775","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10055938
Zhengya Ma, Xiaoxu Wang, Rui Li, Haoran Cui
The paper presents a robust identification method using variational inference (VI) for Hammerstein models in the presence of process noise and non-Gaussian colored measurement noise. First of all the measurements and process output are described as Student’s t and Gaussian distribution by using introduced variational parameters. Then the conjugate prior information of introduced parameters is framed for sake of a closed-loop solution. By applying the idea of VI, estimates of system parameters are got by minimizing Kullback-Leibler (KL) divergence. Finally, a numerical simulation example is used to show the effectiveness of the proposed identification method compared with the traditional method.
{"title":"Robust Estimation for Hammerstein Models Based on Variational Inference","authors":"Zhengya Ma, Xiaoxu Wang, Rui Li, Haoran Cui","doi":"10.1109/CAC57257.2022.10055938","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055938","url":null,"abstract":"The paper presents a robust identification method using variational inference (VI) for Hammerstein models in the presence of process noise and non-Gaussian colored measurement noise. First of all the measurements and process output are described as Student’s t and Gaussian distribution by using introduced variational parameters. Then the conjugate prior information of introduced parameters is framed for sake of a closed-loop solution. By applying the idea of VI, estimates of system parameters are got by minimizing Kullback-Leibler (KL) divergence. Finally, a numerical simulation example is used to show the effectiveness of the proposed identification method compared with the traditional method.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114138520","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 : 2022-11-25DOI: 10.1109/cac57257.2022.10055493
Li Yilun, Zhang Yishu, Yao Zhiyuan, Feng Juan, Li Yang, Zhang Chengye
A hybrid prediction model based on wavelet transform (WT) -sample entropy (SE) -improved particle swarm optimization (IPSO) -weighted least squares support vector machine (WLSSVM) -iterative error correction is proposed to solve the problem of low accuracy and poor stability of photovoltaic output prediction under grid-connected conditions. Firstly, WT is used to reduce the noise in the collected power signal, and SE is used to quantify the weather type. Then IPSO is used to optimize the main parameters of WLSSVM. Finally, power prediction model and error prediction model are established respectively, and the final prediction power is obtained by superposition of power prediction value and error at all levels. Finally, the proposed model is compared with other prediction models, and the results show that the method has high prediction accuracy.
{"title":"Research on PV Power Prediction Model Based on Hybrid Prediction","authors":"Li Yilun, Zhang Yishu, Yao Zhiyuan, Feng Juan, Li Yang, Zhang Chengye","doi":"10.1109/cac57257.2022.10055493","DOIUrl":"https://doi.org/10.1109/cac57257.2022.10055493","url":null,"abstract":"A hybrid prediction model based on wavelet transform (WT) -sample entropy (SE) -improved particle swarm optimization (IPSO) -weighted least squares support vector machine (WLSSVM) -iterative error correction is proposed to solve the problem of low accuracy and poor stability of photovoltaic output prediction under grid-connected conditions. Firstly, WT is used to reduce the noise in the collected power signal, and SE is used to quantify the weather type. Then IPSO is used to optimize the main parameters of WLSSVM. Finally, power prediction model and error prediction model are established respectively, and the final prediction power is obtained by superposition of power prediction value and error at all levels. Finally, the proposed model is compared with other prediction models, and the results show that the method has high prediction accuracy.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114258966","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10054668
Qichen Meng, Yiwei Shen, Juping Liang, Kun Xiang, Guozhu Zhang
Focused on the key single machine of the Alpha sun orientation subsystem in the power sub-system of the Wentian and Mengtian experimental cabin of the space station - the in-cabin and extra-vehicle drive controller which needed to autonomously complete the cabin segment (Wentian, Mengtian) and cabin space (inside and outside the cabin) identification and independently fulfill the needs of the differentiated control of flexible sailboards in the Wentian and Mengtian experimental cabins by receiving the commands of the space station digital management sub-system and the GNC sub-system according to the requirements of the space station’s energy assurance tasks, this paper proposed an in-cabin and extra-cabin drive controller autonomous identification and differentiated control method. At the same time, combined with the different working modes inside and outside the cabin, the drive controller executed the information collection and processing of other functional components in the Alpha sun orientation subsystem, and completed the closed-loop control and fault detection processing of the Alpha sun orientation device, and sent the different processing information to the GNC sub-system and digital tube sub-system. As a key single machine of the power supply subsystem which was a key sub-system of China’s space station, an important national project, the in-cabin and out-cabin drive controller was very important to ensure the energy security of the entire space station.
{"title":"Research on adaptive and differentiated control method of drive controller in Wentian and Mengtian experimental cabin of space station","authors":"Qichen Meng, Yiwei Shen, Juping Liang, Kun Xiang, Guozhu Zhang","doi":"10.1109/CAC57257.2022.10054668","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054668","url":null,"abstract":"Focused on the key single machine of the Alpha sun orientation subsystem in the power sub-system of the Wentian and Mengtian experimental cabin of the space station - the in-cabin and extra-vehicle drive controller which needed to autonomously complete the cabin segment (Wentian, Mengtian) and cabin space (inside and outside the cabin) identification and independently fulfill the needs of the differentiated control of flexible sailboards in the Wentian and Mengtian experimental cabins by receiving the commands of the space station digital management sub-system and the GNC sub-system according to the requirements of the space station’s energy assurance tasks, this paper proposed an in-cabin and extra-cabin drive controller autonomous identification and differentiated control method. At the same time, combined with the different working modes inside and outside the cabin, the drive controller executed the information collection and processing of other functional components in the Alpha sun orientation subsystem, and completed the closed-loop control and fault detection processing of the Alpha sun orientation device, and sent the different processing information to the GNC sub-system and digital tube sub-system. As a key single machine of the power supply subsystem which was a key sub-system of China’s space station, an important national project, the in-cabin and out-cabin drive controller was very important to ensure the energy security of the entire space station.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114588427","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10055665
Shibo Li, Hui Lv, Yadong Chen
This paper is focused on the bounded synchronization problem of coupled time-delayed neural networks with discontinuous activation functions. Firstly, an event-triggered control scheme is developed in order to save the limited communication resources. Then, based on Filippov solution, a control protocol is given to guarantee the leaderless bounded synchronization for linear coupled neural networks, while excluding the Zeno behavior. Moreover, a novel sufficient criterion under the strongly connected networks is derived by employing the Lyapunov-Krasovskii function theory and linear matrix inequality (LMI). Finally, the validity of the theoretical results is verified through simulation.
{"title":"Bounded Synchronization of Coupled Discontinuous Neural Networks Under an Event-Triggered Strategy*","authors":"Shibo Li, Hui Lv, Yadong Chen","doi":"10.1109/CAC57257.2022.10055665","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055665","url":null,"abstract":"This paper is focused on the bounded synchronization problem of coupled time-delayed neural networks with discontinuous activation functions. Firstly, an event-triggered control scheme is developed in order to save the limited communication resources. Then, based on Filippov solution, a control protocol is given to guarantee the leaderless bounded synchronization for linear coupled neural networks, while excluding the Zeno behavior. Moreover, a novel sufficient criterion under the strongly connected networks is derived by employing the Lyapunov-Krasovskii function theory and linear matrix inequality (LMI). Finally, the validity of the theoretical results is verified through simulation.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121882798","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10054924
N. Li, Yue Wang, Ziyun Wang, Yan Wang
Aiming at the problem of the battery manufacturing capacity prediction, this paper presents a prediction method based on reinforcement learning network combination model. First, the combined model expression for the battery manufacturing capacity prediction is designed. Then, reinforcement learning is used to construct the hidden layer learning environment of recurrent neural network and long-short-termmemory network model, to obtain the optimal number of hidden layers, and then to construct the weight learning environment of the battery manufacturing capacity combination prediction model and a combined forecasting model of battery manufacturing capacity after iterative training. Finally, a case simulation on actual battery workshop data shows the effectiveness and practicability of the proposed algorithm on solving the battery manufacturing capacity prediction problem.
{"title":"Prediction of battery manufacturing capacity based on reinforcement learning network combination model","authors":"N. Li, Yue Wang, Ziyun Wang, Yan Wang","doi":"10.1109/CAC57257.2022.10054924","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054924","url":null,"abstract":"Aiming at the problem of the battery manufacturing capacity prediction, this paper presents a prediction method based on reinforcement learning network combination model. First, the combined model expression for the battery manufacturing capacity prediction is designed. Then, reinforcement learning is used to construct the hidden layer learning environment of recurrent neural network and long-short-termmemory network model, to obtain the optimal number of hidden layers, and then to construct the weight learning environment of the battery manufacturing capacity combination prediction model and a combined forecasting model of battery manufacturing capacity after iterative training. Finally, a case simulation on actual battery workshop data shows the effectiveness and practicability of the proposed algorithm on solving the battery manufacturing capacity prediction problem.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116861604","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10054814
Chen Wei, Yong He, Xing-Chen Shangguan
Dissipative analysis about delayed neural networks is explored in the research. Firstly, the Firstly, the strengthened Lyapunov-Krasovskii functional (LKF) has been built. After that, the terms having time-varying delay cubic are then formed in the LKF’s derivative by disassembling the partial integral terms in the functional into the terms that contain time-varying delay. By using the negative definite lemma of cubic function to determine its negative qualitativeness, the low conservative dissipation condition of $({mathcal{Q}},{mathcal{S}},{mathcal{R}})$-γ-neural network is obtained. The developed criterion’s superiority and effectiveness is demonstrated by the numerical example at last.
{"title":"Dissipative analysis of delayed neural networks based on the negative definite lemma of cubic functions","authors":"Chen Wei, Yong He, Xing-Chen Shangguan","doi":"10.1109/CAC57257.2022.10054814","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054814","url":null,"abstract":"Dissipative analysis about delayed neural networks is explored in the research. Firstly, the Firstly, the strengthened Lyapunov-Krasovskii functional (LKF) has been built. After that, the terms having time-varying delay cubic are then formed in the LKF’s derivative by disassembling the partial integral terms in the functional into the terms that contain time-varying delay. By using the negative definite lemma of cubic function to determine its negative qualitativeness, the low conservative dissipation condition of $({mathcal{Q}},{mathcal{S}},{mathcal{R}})$-γ-neural network is obtained. The developed criterion’s superiority and effectiveness is demonstrated by the numerical example at last.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117046511","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10056091
XiaoYan Wang, Daping Li, Wanghui Bu
Anomaly detection and localization are critical in modern manufacturing for the quality control of products. A particular challenge is that the collecting and labeling of anomaly examples are usually infeasible before implementation. To tackle the problem, a novel two-stage framework is proposed in this paper to build anomaly estimators with normal data only. Specifically, unsupervised deep representations are learned first by a modified SimSiam where an adaptation for one-class learning is implemented. Then the non-parametric method is adopted to model the distribution of training data on the learned representations as the one-class classifier to detect anomaly. Moreover, we model the distribution with different hierarchy level’s features of the convolutional neural network to achieve both image-level and pixel-level detections. Experiments are conducted on MVTec anomaly detection dataset. Competitive results of 92.6% AUROC score for image-level detection and 95.4% for pixel-level detection are obtained to demonstrate the effectiveness of the proposed method.
{"title":"Patch Density Estimation for Anomaly Detection with Deep Pyramid Features","authors":"XiaoYan Wang, Daping Li, Wanghui Bu","doi":"10.1109/CAC57257.2022.10056091","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10056091","url":null,"abstract":"Anomaly detection and localization are critical in modern manufacturing for the quality control of products. A particular challenge is that the collecting and labeling of anomaly examples are usually infeasible before implementation. To tackle the problem, a novel two-stage framework is proposed in this paper to build anomaly estimators with normal data only. Specifically, unsupervised deep representations are learned first by a modified SimSiam where an adaptation for one-class learning is implemented. Then the non-parametric method is adopted to model the distribution of training data on the learned representations as the one-class classifier to detect anomaly. Moreover, we model the distribution with different hierarchy level’s features of the convolutional neural network to achieve both image-level and pixel-level detections. Experiments are conducted on MVTec anomaly detection dataset. Competitive results of 92.6% AUROC score for image-level detection and 95.4% for pixel-level detection are obtained to demonstrate the effectiveness of the proposed method.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117076261","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 : 2022-11-25DOI: 10.1109/CAC57257.2022.10055197
Yufan Fang, Zhaoping Du, Hui Ye, Zhilin Zou, Shuai Shen
We mainly study the controller design problem of discrete network cascade control systems (NCCSs) with state delay and event-triggered control are studied firstly in this paper. For the reason that use network bandwidth resources effectively, a delayed event-triggered mechanism is introduced into the system with time delay. Firstly, Considering the influence of network delay and event-triggered control, and on account of the above conditions, the NCCS model with state delay is established. After that, We can construct a suitable Lyapunov function and provide sufficient conditions of system stability. On this basis, the co-design method of the four event generator parameters and two gain of proportional (P) controllers. Finally, based on the actual needs of industry, a Matlab simulation example of NCCS based event-triggered control in a thermal power plant is given to show the availability of this way, which shows that this method can save network bandwidth resources.
{"title":"Event-Triggered Control for a Class of Discrete-Time Networked Cascade Control Systems With State Delay","authors":"Yufan Fang, Zhaoping Du, Hui Ye, Zhilin Zou, Shuai Shen","doi":"10.1109/CAC57257.2022.10055197","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055197","url":null,"abstract":"We mainly study the controller design problem of discrete network cascade control systems (NCCSs) with state delay and event-triggered control are studied firstly in this paper. For the reason that use network bandwidth resources effectively, a delayed event-triggered mechanism is introduced into the system with time delay. Firstly, Considering the influence of network delay and event-triggered control, and on account of the above conditions, the NCCS model with state delay is established. After that, We can construct a suitable Lyapunov function and provide sufficient conditions of system stability. On this basis, the co-design method of the four event generator parameters and two gain of proportional (P) controllers. Finally, based on the actual needs of industry, a Matlab simulation example of NCCS based event-triggered control in a thermal power plant is given to show the availability of this way, which shows that this method can save network bandwidth resources.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129598148","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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