Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885900
Chuang Zheng, Lin Li, Chanying Li
In this paper, we study the input-to-state stabilization for a class of nonlinear systems with completely unknown disturbances. If the growth rate of the nonlinear system is slower than linearity, we show that the system is stable by a feedback controller based on an adjustable parameter quantizer. Especially, when the systems are linear, the systems under consideration degenerates to the stablizable systems in the conventional sense.
{"title":"On stabilization of a class of nonlinear systems with quantized feedback","authors":"Chuang Zheng, Lin Li, Chanying Li","doi":"10.1109/ICICIP.2016.7885900","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885900","url":null,"abstract":"In this paper, we study the input-to-state stabilization for a class of nonlinear systems with completely unknown disturbances. If the growth rate of the nonlinear system is slower than linearity, we show that the system is stable by a feedback controller based on an adjustable parameter quantizer. Especially, when the systems are linear, the systems under consideration degenerates to the stablizable systems in the conventional sense.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131276938","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885912
Hengbo Ma, Tianyu Tan, Hongpeng Zhou, Tianyi Gao
Parkinson disease has become a serious problem in the old people. There is no precise method to diagnosis Parkinson disease now. Considering the significance and difficulty of recognizing the Parkinson disease, the measurement of samples' voices is regard as one of the best non-invasive ways to find the real patient. Support Vector Machine is one of the most effective tools to classify in machine learning, and it has been applied successfully in many areas. In this paper, we implement the SVM-recursive feature elimination which has not been used before for selecting the subset including the most important features for classification from the original features. We also implement SVM with PCA for selecting the principle components for diagnosis PD set with 22 features in order to compare. At last, we discuss the relationship between SVM-RFE and SVM with PCA specially in the experiment. The experiment illustrates that the SVM-RFE has the better performance than other methods in general.
{"title":"Support Vector Machine-recursive feature elimination for the diagnosis of Parkinson disease based on speech analysis","authors":"Hengbo Ma, Tianyu Tan, Hongpeng Zhou, Tianyi Gao","doi":"10.1109/ICICIP.2016.7885912","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885912","url":null,"abstract":"Parkinson disease has become a serious problem in the old people. There is no precise method to diagnosis Parkinson disease now. Considering the significance and difficulty of recognizing the Parkinson disease, the measurement of samples' voices is regard as one of the best non-invasive ways to find the real patient. Support Vector Machine is one of the most effective tools to classify in machine learning, and it has been applied successfully in many areas. In this paper, we implement the SVM-recursive feature elimination which has not been used before for selecting the subset including the most important features for classification from the original features. We also implement SVM with PCA for selecting the principle components for diagnosis PD set with 22 features in order to compare. At last, we discuss the relationship between SVM-RFE and SVM with PCA specially in the experiment. The experiment illustrates that the SVM-RFE has the better performance than other methods in general.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131563935","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885907
Jian Han, C. Wang, B. Li
Star-sensor has an excellent capability of providing high accurate attitude, which depends on the accuracy of location to a large extent. At present, star-sensor is restricted by the accuracy of its location information which is normally given by Initial Navigation System, but this location information involves non-ignorable inaccuracy due to the accumulated error. In prior research, star-sensor needs to sense horizon level to do positioning, but this needs cooperation of other equipment or specific maneuvering which diminishes the nature of autonomy and concealment, at the same time, lowers dynamic performances and introduces more integration error. In this paper, a novel method has been proposed to solve the problems above, which needs no auxiliary information from outside. Location gained by this method is an absolute information which will not divergent over time. Above all, Celestial Navigation System can achieve better autonomous navigation capability and higher navigation accuracy by utilizing this method.
{"title":"High accuracy method of positioning based on multi-star-sensor","authors":"Jian Han, C. Wang, B. Li","doi":"10.1109/ICICIP.2016.7885907","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885907","url":null,"abstract":"Star-sensor has an excellent capability of providing high accurate attitude, which depends on the accuracy of location to a large extent. At present, star-sensor is restricted by the accuracy of its location information which is normally given by Initial Navigation System, but this location information involves non-ignorable inaccuracy due to the accumulated error. In prior research, star-sensor needs to sense horizon level to do positioning, but this needs cooperation of other equipment or specific maneuvering which diminishes the nature of autonomy and concealment, at the same time, lowers dynamic performances and introduces more integration error. In this paper, a novel method has been proposed to solve the problems above, which needs no auxiliary information from outside. Location gained by this method is an absolute information which will not divergent over time. Above all, Celestial Navigation System can achieve better autonomous navigation capability and higher navigation accuracy by utilizing this method.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116655476","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885883
J. Zheng, Hongfang Wang, Hongpeng Zhou, Tianyi Gao
As model-based methods have difficulty to solve more and more complex processes fault detection problems today, data-driven based techniques have been wildly used in industrial systems monitoring because of its ability to process unknown physical model. However, conventional static data-driven fault detection method have problems in processing nonlinear systems fault detection with deterministic disturbances in nonlinear system. In order to deal with this, a method called just-in-time learning based data-driven (JITL-DD) was invented. In this method, JITL is used for learning the nonlinear model and the disturbances to predict the output. The residuals of the predict and real one will be processed by static data-driven method to decide wether it has fault. In this article, A numerical example will be used to test the algorithm and a case study of CSTH are proposed to show the performance of JITL-DD method. As comparisons, JITL-PCA method is also employed to solve the same problem.
{"title":"A using of just-in-time learning based data driven method in continuous stirred tank heater","authors":"J. Zheng, Hongfang Wang, Hongpeng Zhou, Tianyi Gao","doi":"10.1109/ICICIP.2016.7885883","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885883","url":null,"abstract":"As model-based methods have difficulty to solve more and more complex processes fault detection problems today, data-driven based techniques have been wildly used in industrial systems monitoring because of its ability to process unknown physical model. However, conventional static data-driven fault detection method have problems in processing nonlinear systems fault detection with deterministic disturbances in nonlinear system. In order to deal with this, a method called just-in-time learning based data-driven (JITL-DD) was invented. In this method, JITL is used for learning the nonlinear model and the disturbances to predict the output. The residuals of the predict and real one will be processed by static data-driven method to decide wether it has fault. In this article, A numerical example will be used to test the algorithm and a case study of CSTH are proposed to show the performance of JITL-DD method. As comparisons, JITL-PCA method is also employed to solve the same problem.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114757761","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885906
Q. Ma, Xinyu Pan, Sitian Qin
The global asymptotic stability of anti-periodic solution for Cohen-Grossberg neural networks (CGNNs) is investigated. The CGNNs we consider have impulsive effects and multiple delays. By constructing a suitable Lyapunov function, we prove the existence of the globally asymptotically stable anti-periodic solution for impulsive CGNNs. Several numerical examples are presented to illustrate the validity and improvement of our results.
{"title":"Global asymptotic stability of anti-periodic solution for impulsive Cohen-Grossberg neural networks with multiple delays","authors":"Q. Ma, Xinyu Pan, Sitian Qin","doi":"10.1109/ICICIP.2016.7885906","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885906","url":null,"abstract":"The global asymptotic stability of anti-periodic solution for Cohen-Grossberg neural networks (CGNNs) is investigated. The CGNNs we consider have impulsive effects and multiple delays. By constructing a suitable Lyapunov function, we prove the existence of the globally asymptotically stable anti-periodic solution for impulsive CGNNs. Several numerical examples are presented to illustrate the validity and improvement of our results.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"3 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120807858","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885881
Hongpeng Zhou, Hao Ju, Tianyu Tan, Tianyi Gao
Intelligent transportation is a hot research field in Cyber-Physical System (CPS). In order to improve the driving safety, many studies have been conducted to predict collision probability and send out warning signal timely. However, most of these studies are model based with limited prediction accuracy. Moreover, the abundant historical data is leave-off. In this paper, a data-driven method is proposed to achieve the same objective, which could acquire a more satisfactory result and provide an accurate prediction for two key performance indicator(i.e. throttle and brake). A vehicle cyber-physical system (VCPS) benchmark is built on the professional software CarSim. The algorithm just-in time learning (JITL) would process motivation data produced by the benchmark and compute out the prediction result. For testifying the advantages of the proposed method, the other two fitting algorithms (i.e. PLS and KPLS) are compared with it. The simulation results prove that JITL could consume much lesser time and receive a more precise prediction.
智能交通是信息物理系统(CPS)中的一个研究热点。为了提高行车安全性，人们进行了大量的碰撞概率预测和及时发出预警信号的研究。然而，这些研究大多是基于模型的，预测精度有限。此外，丰富的历史资料是留下的。本文提出了一种数据驱动的方法来实现同样的目标，该方法可以获得更令人满意的结果，并对两个关键绩效指标(即:油门和刹车)。在专业软件CarSim上建立了车辆网络物理系统(VCPS)基准。jit (just-in - time learning)算法对基准测试产生的动机数据进行处理并计算预测结果。为了验证所提方法的优越性，将另外两种拟合算法(PLS和KPLS)与所提方法进行了比较。仿真结果表明，JITL可以节省大量的时间，获得更精确的预测结果。
{"title":"A KPI prediction approach with JITL for vehicular Cyber Physical System","authors":"Hongpeng Zhou, Hao Ju, Tianyu Tan, Tianyi Gao","doi":"10.1109/ICICIP.2016.7885881","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885881","url":null,"abstract":"Intelligent transportation is a hot research field in Cyber-Physical System (CPS). In order to improve the driving safety, many studies have been conducted to predict collision probability and send out warning signal timely. However, most of these studies are model based with limited prediction accuracy. Moreover, the abundant historical data is leave-off. In this paper, a data-driven method is proposed to achieve the same objective, which could acquire a more satisfactory result and provide an accurate prediction for two key performance indicator(i.e. throttle and brake). A vehicle cyber-physical system (VCPS) benchmark is built on the professional software CarSim. The algorithm just-in time learning (JITL) would process motivation data produced by the benchmark and compute out the prediction result. For testifying the advantages of the proposed method, the other two fitting algorithms (i.e. PLS and KPLS) are compared with it. The simulation results prove that JITL could consume much lesser time and receive a more precise prediction.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130759792","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885893
Cheng Guo, M. Zhang, Yiqun Liu, Shaoping Ma
Recent recommender systems work well in terms of prediction accuracy, making use of a variety of features, such as users' personal information, purchasing history, browsing history and comments. However, traditional recommendation models have not made full use of item information and met difficulties with cold-start problems. On the other hands, visual information on item images is one of the most basic and informative features of the item, which has not been well-studied and applied in recommendation yet. In this paper, we introduce “visual similarity” between different items into recommendation, which measures the probability between items that are similar in terms of visual effect or “styles”. Observations on real e-commercial site data show that users tend to buy similar items, or items with similar “style”, indicating that visual information can be considered as a reliable feature in recommending process. Furthermore, a new matrix supplement approach is proposed to integrate item-item similarity matrix and traditional user-item matrix for collaborative filtering. Finally, a novel recommendation model is proposed which leverages visual similarity to collaborative filtering. Experiments on e-commercial website data shows that the proposed approaches result in superior performance compared with traditional recommendation algorithms, including Baseline Predictor, KNN (k-nearest-neighbors) and SVD (Singular Value Decomposition). Results also verifies that visual information does help relieve the “cold-start” problem in recommendation.
最近的推荐系统在预测准确性方面表现良好，利用了各种功能，如用户的个人信息、购买历史、浏览历史和评论。然而，传统的推荐模型没有充分利用项目信息，存在冷启动问题。另一方面，物品图像上的视觉信息是物品最基本的信息特征之一，在推荐中还没有得到很好的研究和应用。在本文中，我们将不同项目之间的“视觉相似性”引入到推荐中，它衡量的是在视觉效果或“风格”方面相似的项目之间的概率。对真实电子商务网站数据的观察表明，用户倾向于购买相似的商品，或者具有相似“风格”的商品，这表明视觉信息可以被认为是推荐过程中可靠的特征。在此基础上，提出了一种新的矩阵补充方法，将物品-物品相似度矩阵与传统的用户-物品矩阵相结合进行协同过滤。最后，提出了一种利用视觉相似性进行协同过滤的推荐模型。在电子商务网站数据上的实验表明，与Baseline Predictor、KNN (k-nearest-neighbors)和SVD (Singular Value Decomposition)等传统推荐算法相比，本文提出的推荐方法具有更好的性能。结果还验证了视觉信息确实有助于缓解推荐中的“冷启动”问题。
{"title":"A picture is worth a thousand words: Introducing visual similarity into recommendation","authors":"Cheng Guo, M. Zhang, Yiqun Liu, Shaoping Ma","doi":"10.1109/ICICIP.2016.7885893","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885893","url":null,"abstract":"Recent recommender systems work well in terms of prediction accuracy, making use of a variety of features, such as users' personal information, purchasing history, browsing history and comments. However, traditional recommendation models have not made full use of item information and met difficulties with cold-start problems. On the other hands, visual information on item images is one of the most basic and informative features of the item, which has not been well-studied and applied in recommendation yet. In this paper, we introduce “visual similarity” between different items into recommendation, which measures the probability between items that are similar in terms of visual effect or “styles”. Observations on real e-commercial site data show that users tend to buy similar items, or items with similar “style”, indicating that visual information can be considered as a reliable feature in recommending process. Furthermore, a new matrix supplement approach is proposed to integrate item-item similarity matrix and traditional user-item matrix for collaborative filtering. Finally, a novel recommendation model is proposed which leverages visual similarity to collaborative filtering. Experiments on e-commercial website data shows that the proposed approaches result in superior performance compared with traditional recommendation algorithms, including Baseline Predictor, KNN (k-nearest-neighbors) and SVD (Singular Value Decomposition). Results also verifies that visual information does help relieve the “cold-start” problem in recommendation.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116799647","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885905
Geng Wang, Guoqiang Ye
For cameras obeying the unified projection model, a set of independent visual features are designed with a virtual unitary spherical projection process. Then, image based visual servoing is formulated as a nonlinear constrained optimization problem by nonlinear model predictive control in the feature space. Feature jacobian is calculated to define the local model, which is used to predict the evolution of the visual features with respect to the camera velocity over a finite-prediction horizon. Iterative equations for constrained variables about visibility, camera velocity and task space limitation, are designed to meet both 2D and 3D constraints. Finally, simulation results with a classical perspective camera are presented to verify the effectiveness and improved behaviors of proposed method.
{"title":"Spherical image based visual servoing via nonlinear model predictive control","authors":"Geng Wang, Guoqiang Ye","doi":"10.1109/ICICIP.2016.7885905","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885905","url":null,"abstract":"For cameras obeying the unified projection model, a set of independent visual features are designed with a virtual unitary spherical projection process. Then, image based visual servoing is formulated as a nonlinear constrained optimization problem by nonlinear model predictive control in the feature space. Feature jacobian is calculated to define the local model, which is used to predict the evolution of the visual features with respect to the camera velocity over a finite-prediction horizon. Iterative equations for constrained variables about visibility, camera velocity and task space limitation, are designed to meet both 2D and 3D constraints. Finally, simulation results with a classical perspective camera are presented to verify the effectiveness and improved behaviors of proposed method.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123801429","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885892
Jeonghyeok Kim, Sanggil Kang, Heezin Lee
Small-footprint airborne LiDAR scanning systems are effective in modelling forest structures and can also improve trail detection. We propose a trail detection method through a statistical analysis from the LiDAR points. To do that, we statistically analyze features of trails for detecting a trail and digitized each feature and combine the results to distinguish between trail and non-trail areas. Our proposed method shows the feasibility of trail detection by using airborne LiDAR points gathered in dense mixed forest.
{"title":"A trail detection method using statistical analysis of trail features in dense forest","authors":"Jeonghyeok Kim, Sanggil Kang, Heezin Lee","doi":"10.1109/ICICIP.2016.7885892","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885892","url":null,"abstract":"Small-footprint airborne LiDAR scanning systems are effective in modelling forest structures and can also improve trail detection. We propose a trail detection method through a statistical analysis from the LiDAR points. To do that, we statistically analyze features of trails for detecting a trail and digitized each feature and combine the results to distinguish between trail and non-trail areas. Our proposed method shows the feasibility of trail detection by using airborne LiDAR points gathered in dense mixed forest.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122390330","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 : 2016-12-01DOI: 10.1109/ICICIP.2016.7885890
Qiancheng Xu, Hao Xia
This paper proposed an event-triggered mechanism for first-order unstable processes. A control system based on a new event-triggered mechanism and a PI controller is designed for set-point tracking and the load disturbance rejection. A stability analysis and a tuning method are provided. The effectiveness and feasibility of the proposed method is demonstrated by a simulation example.
{"title":"Event-based control for first-order unstable processes","authors":"Qiancheng Xu, Hao Xia","doi":"10.1109/ICICIP.2016.7885890","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885890","url":null,"abstract":"This paper proposed an event-triggered mechanism for first-order unstable processes. A control system based on a new event-triggered mechanism and a PI controller is designed for set-point tracking and the load disturbance rejection. A stability analysis and a tuning method are provided. The effectiveness and feasibility of the proposed method is demonstrated by a simulation example.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130335120","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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