Pub Date : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455687
Chen Liu, Xiaoe Ruan, Shuzhen An
In this study, an optimal iterative learning control scheme is designed for discrete linear time-varying systems with varying trial lengths. Since the trial lengths are different from iteration to iteration, the theoretical information is used to compensate the absent section at the current iteration. In order to obtain the fast convergence speed, an iteration performance index is to maximize the declining quantity of the tracking error of two adjacent iterations, and the argument is the iteration-time-varying learning gain vector. The bigger the difference value, the faster the convergence speed. Furthermore, the optimal iterative learning control scheme is adaptive to the tracking error, which can guarantee the convergence of the tracking error. Numerical simulations are shown to verify the effectiveness of the proposed scheme.
{"title":"Optimal Iterative Learning Control for Discrete Linear Time-Varying Systems with Varying Trial Lengths","authors":"Chen Liu, Xiaoe Ruan, Shuzhen An","doi":"10.1109/DDCLS52934.2021.9455687","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455687","url":null,"abstract":"In this study, an optimal iterative learning control scheme is designed for discrete linear time-varying systems with varying trial lengths. Since the trial lengths are different from iteration to iteration, the theoretical information is used to compensate the absent section at the current iteration. In order to obtain the fast convergence speed, an iteration performance index is to maximize the declining quantity of the tracking error of two adjacent iterations, and the argument is the iteration-time-varying learning gain vector. The bigger the difference value, the faster the convergence speed. Furthermore, the optimal iterative learning control scheme is adaptive to the tracking error, which can guarantee the convergence of the tracking error. Numerical simulations are shown to verify the effectiveness of the proposed scheme.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127643820","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455650
Yaohui Ding, Xiao-dong Li
This paper proposes an adaptive Iterative Learning Control (ILC) method for no-parameterized nonlinear continuous systems to track iteration-dependent reference trajectory. The adaptive ILC method releases the general requirement in adaptive ILC community that the control gain matrices of the plants are real asymmetric or even positive-definite. Under the iteration-dependent reference trajectory and unknown external disturbance, the proposed adaptive ILC controller with a simple structure, which includes only two iterative variables, is able to guarantee the convergence of ILC tracking errors. A numerical example is used to verify the effectiveness of the proposed Adaptive ILC method.
{"title":"An Adaptive ILC Method for Non-Parameterized Nonlinear Continuous Systems to Track Iteration-Dependent Trajectory","authors":"Yaohui Ding, Xiao-dong Li","doi":"10.1109/DDCLS52934.2021.9455650","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455650","url":null,"abstract":"This paper proposes an adaptive Iterative Learning Control (ILC) method for no-parameterized nonlinear continuous systems to track iteration-dependent reference trajectory. The adaptive ILC method releases the general requirement in adaptive ILC community that the control gain matrices of the plants are real asymmetric or even positive-definite. Under the iteration-dependent reference trajectory and unknown external disturbance, the proposed adaptive ILC controller with a simple structure, which includes only two iterative variables, is able to guarantee the convergence of ILC tracking errors. A numerical example is used to verify the effectiveness of the proposed Adaptive ILC method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774210","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455617
Guofa Sun, Huipeng Du, Gang Wang
For a class of nonlinear discrete-time systems with input backlash, the fuzzy backlash model is used to replace the backlash inverse model, and the fuzzy backlash model is approximated by the linear combination of linear term and disturbance like term. The internal unknown function and external unknown disturbance of the closed-loop system are defined as the total disturbance of the system, and the discrete-time high-order sliding mode differentiator is used as the precise disturbance observer, and the total disturbance was controlled by an adaptive controller. Finally, Lyapunov theorem is used to prove the stability of the controller, and a simulation example is used to verify the feasibility of the scheme.
{"title":"Fuzzy control of discrete nonlinear systems with backlash","authors":"Guofa Sun, Huipeng Du, Gang Wang","doi":"10.1109/DDCLS52934.2021.9455617","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455617","url":null,"abstract":"For a class of nonlinear discrete-time systems with input backlash, the fuzzy backlash model is used to replace the backlash inverse model, and the fuzzy backlash model is approximated by the linear combination of linear term and disturbance like term. The internal unknown function and external unknown disturbance of the closed-loop system are defined as the total disturbance of the system, and the discrete-time high-order sliding mode differentiator is used as the precise disturbance observer, and the total disturbance was controlled by an adaptive controller. Finally, Lyapunov theorem is used to prove the stability of the controller, and a simulation example is used to verify the feasibility of the scheme.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128038193","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455629
Wenqi Lin, Bo Peng, Long Jin
Legged robots have unparalleled advantages over the traditional four-wheel and crawler ones. Particularly, they possess higher maneuverability in complex environments and can play a more significant role in military and emergency missions. To adapt the legged robot to the above situations, the primary task is to make the robot move freely like a human or animal, but this is a complicated and expensive project. The paper is dedicated to designing a novel and cheap quadruped robot and developing a complete and adequate control system. The overall design architecture is proposed, focusing on ease of manufacture and low cost of manufacture. Specifically, the control system runs on stm32, and the movement of the quadruped robot is controlled by a direct current motor that can be driven towards different directions by manipulating the terminal control device. Among the quadruped robots of similar performances, the quadruped robot BlackDog is cheaper and more stable in walking, and its structure is simple and easy to be implemented.
{"title":"Design and Implementation of A Novel Quadruped Robot","authors":"Wenqi Lin, Bo Peng, Long Jin","doi":"10.1109/DDCLS52934.2021.9455629","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455629","url":null,"abstract":"Legged robots have unparalleled advantages over the traditional four-wheel and crawler ones. Particularly, they possess higher maneuverability in complex environments and can play a more significant role in military and emergency missions. To adapt the legged robot to the above situations, the primary task is to make the robot move freely like a human or animal, but this is a complicated and expensive project. The paper is dedicated to designing a novel and cheap quadruped robot and developing a complete and adequate control system. The overall design architecture is proposed, focusing on ease of manufacture and low cost of manufacture. Specifically, the control system runs on stm32, and the movement of the quadruped robot is controlled by a direct current motor that can be driven towards different directions by manipulating the terminal control device. Among the quadruped robots of similar performances, the quadruped robot BlackDog is cheaper and more stable in walking, and its structure is simple and easy to be implemented.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126721547","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455582
Yang Zhang, Chao-hong Yang, Qiang Liu
Disturbance processing is necessary for image-based deep learning of abnormal diagnosis for fused processes, e.g., fused magnesium furnace (FMF), since the disturbance of water mist, furnace body, and environment will inevitably affect the visual image relevant to the identification of working conditions. To address this issue, this paper proposes a new predictive neural network (PredNet)-based unsupervised learning method for sequence images processing of fused magnesium furnace. This method consists of a residual extraction of the original sequence images, a feature learning of disturbance via PredNets, and a single frame de-mean operation. Finally, the proposed method is compared to the one using original data and the one using residual extraction method using the collected sequence images from the furnace shell of a real FMF. The application results demonstrate the effectiveness of the proposed method.
{"title":"PredNet Based Sequence Image Disturbance Processing of Fused Magnesium Furnaces","authors":"Yang Zhang, Chao-hong Yang, Qiang Liu","doi":"10.1109/DDCLS52934.2021.9455582","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455582","url":null,"abstract":"Disturbance processing is necessary for image-based deep learning of abnormal diagnosis for fused processes, e.g., fused magnesium furnace (FMF), since the disturbance of water mist, furnace body, and environment will inevitably affect the visual image relevant to the identification of working conditions. To address this issue, this paper proposes a new predictive neural network (PredNet)-based unsupervised learning method for sequence images processing of fused magnesium furnace. This method consists of a residual extraction of the original sequence images, a feature learning of disturbance via PredNets, and a single frame de-mean operation. Finally, the proposed method is compared to the one using original data and the one using residual extraction method using the collected sequence images from the furnace shell of a real FMF. The application results demonstrate the effectiveness of the proposed method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561566","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455692
Jiamin Xie, Yimeng Song, Xiaolong Lv, H. Shi, Bing Song
In the industrial production, for the close-loop control, not all faults will affect product quality. To detect quality related fault effectively, a novel method named key variable-slow feature analysis (KV-SFA) is proposed in this work to extend the SFA algorithm to the domain of online quality-related fault detection. Firstly, key quality related process variables are selected via the combination of the least absolute shrinkage and selection operator (LASSO) method and the mechanism knowledge. Secondly, the SFA is conducted in the key variables space to extract slow features for establishing fault detection model. Then, the monitoring statistics are constructed and the control limits are estimated. Finally, the validity and effectiveness of the proposed KV-SFA method are proved through an industrial process.
{"title":"Quality-related Process Monitoring of Industrial Processes based on Key Variable-Slow Feature Analysis","authors":"Jiamin Xie, Yimeng Song, Xiaolong Lv, H. Shi, Bing Song","doi":"10.1109/DDCLS52934.2021.9455692","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455692","url":null,"abstract":"In the industrial production, for the close-loop control, not all faults will affect product quality. To detect quality related fault effectively, a novel method named key variable-slow feature analysis (KV-SFA) is proposed in this work to extend the SFA algorithm to the domain of online quality-related fault detection. Firstly, key quality related process variables are selected via the combination of the least absolute shrinkage and selection operator (LASSO) method and the mechanism knowledge. Secondly, the SFA is conducted in the key variables space to extract slow features for establishing fault detection model. Then, the monitoring statistics are constructed and the control limits are estimated. Finally, the validity and effectiveness of the proposed KV-SFA method are proved through an industrial process.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121891831","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455574
Shida Liu, Xuyun Wang, Li Wang, Xiaoping Zhang, Zhonghe He
Aiming at the bus driving safety problems caused by the abnormal behavior of the bus driver during the driving process, this paper proposes a deep learning-based analysis strategy for the abnormal behavior of the bus driver. The program defines the abnormal behaviors of bus drivers and categorizes them into behaviors such as smoking, drinking, and making phone calls. The YOLOv5 (You Only Look Once-Version 5) convolutional neural network algorithm is used as the core technique, and the abnormal behavior data of the drivers in the actual bus is used to produce the abnormal behavior data of the bus drivers. Collected and carried out automatic detection experiments to test the feasibility and effectiveness of drivers' abnormal behaviors. The experimental results show that the detection of abnormal behaviors of bus drivers is fast and accurate, the scheme is feasible and effective, and the detection effect can meet the application requirements.
针对公交司机在行驶过程中的异常行为导致的公交行驶安全问题，本文提出了一种基于深度学习的公交司机异常行为分析策略。该程序定义了公交车司机的异常行为，并将其分类为吸烟、饮酒、打电话等行为。采用YOLOv5 (You Only Look one - version 5)卷积神经网络算法作为核心技术，利用实际公交中司机的异常行为数据生成公交司机的异常行为数据。收集并进行自动检测实验，检验驾驶员异常行为的可行性和有效性。实验结果表明，该方法对公交司机异常行为的检测快速准确，方案可行有效，检测效果能够满足应用要求。
{"title":"Abnormal Behavior Analysis Strategy of Bus Drivers Based on Deep Learning","authors":"Shida Liu, Xuyun Wang, Li Wang, Xiaoping Zhang, Zhonghe He","doi":"10.1109/DDCLS52934.2021.9455574","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455574","url":null,"abstract":"Aiming at the bus driving safety problems caused by the abnormal behavior of the bus driver during the driving process, this paper proposes a deep learning-based analysis strategy for the abnormal behavior of the bus driver. The program defines the abnormal behaviors of bus drivers and categorizes them into behaviors such as smoking, drinking, and making phone calls. The YOLOv5 (You Only Look Once-Version 5) convolutional neural network algorithm is used as the core technique, and the abnormal behavior data of the drivers in the actual bus is used to produce the abnormal behavior data of the bus drivers. Collected and carried out automatic detection experiments to test the feasibility and effectiveness of drivers' abnormal behaviors. The experimental results show that the detection of abnormal behaviors of bus drivers is fast and accurate, the scheme is feasible and effective, and the detection effect can meet the application requirements.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125048305","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455459
Chenhui Jiang, Dong Ding, Jiancheng Zhang, Ze Tang
The article studies the cluster synchronization for a kind of nonlinear coupled complex network with time-varying delay. Considering the networks may subject to certain uncertainties, the model of complex networks consisting of nonidentical systems with randomly occurring disturbance which described by Bernoulli stochastic variable is established. Secondly, a kind of pinning feedback controllers under randomly occurring disturbance is proposed in order to not only synchronize the systems in the same clusters but also weaken the mutual influence among clusters, which will be imposed on the systems in current cluster which have directed connections with the systems in the other clusters. Then, sufficient conditions for the realization of the cluster synchronization are derived in terms of the QUAD function class, the NCF function class and the Lyapunov stability theorem. Furthermore, the optimal feedback control gain is obtained by designing the adaptive updating laws. Finally, a numerical experiment is presented to illustrate the effectiveness of theoretical analysis.
{"title":"Randomly Occurring Cluster Synchronization of Complex Networks via Adaptive Pinning Control","authors":"Chenhui Jiang, Dong Ding, Jiancheng Zhang, Ze Tang","doi":"10.1109/DDCLS52934.2021.9455459","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455459","url":null,"abstract":"The article studies the cluster synchronization for a kind of nonlinear coupled complex network with time-varying delay. Considering the networks may subject to certain uncertainties, the model of complex networks consisting of nonidentical systems with randomly occurring disturbance which described by Bernoulli stochastic variable is established. Secondly, a kind of pinning feedback controllers under randomly occurring disturbance is proposed in order to not only synchronize the systems in the same clusters but also weaken the mutual influence among clusters, which will be imposed on the systems in current cluster which have directed connections with the systems in the other clusters. Then, sufficient conditions for the realization of the cluster synchronization are derived in terms of the QUAD function class, the NCF function class and the Lyapunov stability theorem. Furthermore, the optimal feedback control gain is obtained by designing the adaptive updating laws. Finally, a numerical experiment is presented to illustrate the effectiveness of theoretical analysis.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166985","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455464
Xin Deng, Boxian Zhang, Ke Liu, Jin Wang, Pengfei Yang, Chengxin Hu
The effective features of the motor imagery (MI) electroencephalogram (EEG) signals plays a significant role to improve the classification accuracy for the brain-computer interface (BCI) system. Some traditional methods usually extract the frequency or spatial features without considering the related information between different channels that would affect the classification performance. This paper proposes a new method for feature extraction of EEG signals based on the fusion of time-frequency and spatial features. At the beginning, the common spatial pattern (CSP) algorithm is adopted to extract the spatial features. Then the discrete wavelet transform (DWT) and the wavelet packet decomposition (WPD) are used to extract the µ rhythm of the motor imagery EEG signals as the time-frequency features. After that, by combining the spatial and time-frequency features, the time-frequency-spatial feature is formed. Based on different kinds of features, the experimental data are classified by using the support vector machine (SVM), as well as the sparse representation classification (SRC) algorithm with the elastomeric network (EN) and L1 norm, respectively. The experimental results show that the SRC with EN has a better performance on either the time-frequency feature or spatial feature than the SRC with L1 norm does. In contrast, the SVM and the SRC with Ll norm perform better than the SRC with EN based on the time-frequency-spatial feature. The study concludes that the time-frequency-spatial feature cooperating with the certain classifiers can achieve the good classification effect for the MI EEG signals, which not only reduces the operation time but also improves the classification accuracy.
{"title":"The Classification of Motor Imagery EEG Signals Based on the Time-Frequency-Spatial Feature","authors":"Xin Deng, Boxian Zhang, Ke Liu, Jin Wang, Pengfei Yang, Chengxin Hu","doi":"10.1109/DDCLS52934.2021.9455464","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455464","url":null,"abstract":"The effective features of the motor imagery (MI) electroencephalogram (EEG) signals plays a significant role to improve the classification accuracy for the brain-computer interface (BCI) system. Some traditional methods usually extract the frequency or spatial features without considering the related information between different channels that would affect the classification performance. This paper proposes a new method for feature extraction of EEG signals based on the fusion of time-frequency and spatial features. At the beginning, the common spatial pattern (CSP) algorithm is adopted to extract the spatial features. Then the discrete wavelet transform (DWT) and the wavelet packet decomposition (WPD) are used to extract the µ rhythm of the motor imagery EEG signals as the time-frequency features. After that, by combining the spatial and time-frequency features, the time-frequency-spatial feature is formed. Based on different kinds of features, the experimental data are classified by using the support vector machine (SVM), as well as the sparse representation classification (SRC) algorithm with the elastomeric network (EN) and L1 norm, respectively. The experimental results show that the SRC with EN has a better performance on either the time-frequency feature or spatial feature than the SRC with L1 norm does. In contrast, the SVM and the SRC with Ll norm perform better than the SRC with EN based on the time-frequency-spatial feature. The study concludes that the time-frequency-spatial feature cooperating with the certain classifiers can achieve the good classification effect for the MI EEG signals, which not only reduces the operation time but also improves the classification accuracy.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267125","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455506
Lei Liu, Yi He, Cunwu Han
In this paper, the problem of tracking control for uncertain singularly perturbed systems is studied. Firstly, the uncertain singularly perturbed system and the uncertain external system are combined to form an augmented system, and the optimal tracking problem is transformed into a new standard linear quadratic optimization problem. Then, based on the minimum principle, the minimum value of quadratic performance index and the tracking optimal controller of the system are obtained. For the controller with a feasible approximate solution of the generalized Riccati equation, the design method of the controller can be obtained in the form of linear matrix inequality (LMI). Finally, a numerical example is given to demonstrate the viability and rightness of the proposed conclusion.
{"title":"Optimal Tracking Control for Uncertain Singularly Perturbed Systems","authors":"Lei Liu, Yi He, Cunwu Han","doi":"10.1109/DDCLS52934.2021.9455506","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455506","url":null,"abstract":"In this paper, the problem of tracking control for uncertain singularly perturbed systems is studied. Firstly, the uncertain singularly perturbed system and the uncertain external system are combined to form an augmented system, and the optimal tracking problem is transformed into a new standard linear quadratic optimization problem. Then, based on the minimum principle, the minimum value of quadratic performance index and the tracking optimal controller of the system are obtained. For the controller with a feasible approximate solution of the generalized Riccati equation, the design method of the controller can be obtained in the form of linear matrix inequality (LMI). Finally, a numerical example is given to demonstrate the viability and rightness of the proposed conclusion.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128284689","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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