Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515967
Jing Jin, Shu Zhang, L. Li, T. Zou
With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In the traditional system decomposition methods based on graph theory, the weight on each edge of the graph is set by state space equation to reflect the mutual influence of variables in the system. But in the actual industrial process, the acquisition of state space equation is more difficult. In this paper, a system decomposition method based on Pearson correlation coefficient and graph theory is proposed to avoid the use of state space equations. At first, a directed graph is established to represent the actual process of the industrial system and the weights on corresponding edges in the directed graph are set by the Pearson correlation coefficients between two nodes connected by these edges. Then the directed graph is decomposed into several initial subgraphs and the subgraphs will be fused according to a certain rule. Here, a fusion index is defined to select the optimal fusion results in each fusion process. After each fusion process, the termination condition is required to determine whether to continue the next round of fusion process. When the fusion process ends, the subsets obtained at this time are the results of the system decomposition. When the system decomposition is finished, the online subsystems modeling will be carried out by RPLS algorithm. Finally, the proposed algorithm is applied in the Tennessee Eastman process to verify the validity.
{"title":"A Novel System Decomposition Method Based on Pearson Correlation and Graph Theory*","authors":"Jing Jin, Shu Zhang, L. Li, T. Zou","doi":"10.1109/DDCLS.2018.8515967","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515967","url":null,"abstract":"With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In the traditional system decomposition methods based on graph theory, the weight on each edge of the graph is set by state space equation to reflect the mutual influence of variables in the system. But in the actual industrial process, the acquisition of state space equation is more difficult. In this paper, a system decomposition method based on Pearson correlation coefficient and graph theory is proposed to avoid the use of state space equations. At first, a directed graph is established to represent the actual process of the industrial system and the weights on corresponding edges in the directed graph are set by the Pearson correlation coefficients between two nodes connected by these edges. Then the directed graph is decomposed into several initial subgraphs and the subgraphs will be fused according to a certain rule. Here, a fusion index is defined to select the optimal fusion results in each fusion process. After each fusion process, the termination condition is required to determine whether to continue the next round of fusion process. When the fusion process ends, the subsets obtained at this time are the results of the system decomposition. When the system decomposition is finished, the online subsystems modeling will be carried out by RPLS algorithm. Finally, the proposed algorithm is applied in the Tennessee Eastman process to verify the validity.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"819-824"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86112311","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515952
Lingwei Wu, Mingxuan Sun, Guang Chen
This paper studies the tracking control of uncertain discrete-time systems, a discrete power attracting law is presented for designing the controller. The system has a faster convergence speed obviously and no chattering phenomenon. A measure of the order O(T 3) disturbance-rejection is embedded in the attracting law, so that the steady-state error (SSE) magnitude of the developed method is of the order O(T 3). In order to characterize the tracking performance, we derive the expressions for the range of the power monotone decreasing (PMD) region, the power absolute attractive (PAA) layer and SSE band. Computer simulation results are given to validate the effectiveness and superiority of the presented control method.
{"title":"Convergence Performance of Discrete Power Attracting Law","authors":"Lingwei Wu, Mingxuan Sun, Guang Chen","doi":"10.1109/DDCLS.2018.8515952","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515952","url":null,"abstract":"This paper studies the tracking control of uncertain discrete-time systems, a discrete power attracting law is presented for designing the controller. The system has a faster convergence speed obviously and no chattering phenomenon. A measure of the order O(T 3) disturbance-rejection is embedded in the attracting law, so that the steady-state error (SSE) magnitude of the developed method is of the order O(T 3). In order to characterize the tracking performance, we derive the expressions for the range of the power monotone decreasing (PMD) region, the power absolute attractive (PAA) layer and SSE band. Computer simulation results are given to validate the effectiveness and superiority of the presented control method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"202 1","pages":"974-978"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76624764","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516091
Mengen Xu, Qiao Zhu, Meng’qian Zheng
This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery in electric vehicles (EVs) based on a fractional order adaptive extended Kalman filter (FOAEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery. Then, the parameters of the FOM are identified by a genetic algorithm. The efficiency of the FOM is verified by comparing with the integral order one. After that, a FOAEKF algorithm is developed to deal with the state estimation problem of the FOM. Finally, two dynamic operation conditions are given to show the efficiency of the FOAEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for integral order one.
{"title":"A State of Charge Estimation Approach Based on Fractional Order Adaptive Extended Kalman Filter for Lithium-ion Batteries","authors":"Mengen Xu, Qiao Zhu, Meng’qian Zheng","doi":"10.1109/DDCLS.2018.8516091","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516091","url":null,"abstract":"This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery in electric vehicles (EVs) based on a fractional order adaptive extended Kalman filter (FOAEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery. Then, the parameters of the FOM are identified by a genetic algorithm. The efficiency of the FOM is verified by comparing with the integral order one. After that, a FOAEKF algorithm is developed to deal with the state estimation problem of the FOM. Finally, two dynamic operation conditions are given to show the efficiency of the FOAEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for integral order one.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"118 1","pages":"271-276"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73049984","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516042
Xiaojing Han, Ningwei Cheng, Yuechao Ma
This paper deals with the problem of sampled-data control for T-S fuzzy systems with quantized signals. Based on the constructed Lyapunov-Krasovskii functional(LKF), Jensen’s inequality and Free weight matrix, some sufficient conditions are obtained in the form of linear matrix inequalities(LMIs). By combining the input delay approach and dynamic quantizer, the sampled-data controller is designed to guarantee that T-S fuzzy systems with quantized signals is asymptotically stable. Finally, a numerical example is presented to verify the feasibility and effectiveness of the proposed methods.
{"title":"Sampled-data Control for T-S Fuzzy Systems with Quantized Signals","authors":"Xiaojing Han, Ningwei Cheng, Yuechao Ma","doi":"10.1109/DDCLS.2018.8516042","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516042","url":null,"abstract":"This paper deals with the problem of sampled-data control for T-S fuzzy systems with quantized signals. Based on the constructed Lyapunov-Krasovskii functional(LKF), Jensen’s inequality and Free weight matrix, some sufficient conditions are obtained in the form of linear matrix inequalities(LMIs). By combining the input delay approach and dynamic quantizer, the sampled-data controller is designed to guarantee that T-S fuzzy systems with quantized signals is asymptotically stable. Finally, a numerical example is presented to verify the feasibility and effectiveness of the proposed methods.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"125 1","pages":"145-149"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79077212","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516115
Nannan Rong, Zhanshan Wang, Huaguang Zhang
This paper investigates the fixed-time stabilization issue for a class of nonlinear interconnected systems with discontinuous interconnections and nonidentical perturbations. Firstly, according to the differential inclusion theory, the solutions of such discontinuous interconnected system are defined in the sense of Filippov. In addition, an improved fixed-time lemma, in which the regional bound r can be freely chosen in [0, 1], is proposed to realize the fixed-time stabilization and estimate the settling time. Then, through designing a state feedback controller and utilizing generalized Lyapunov functional method, sufficient criteria are derived to guarantee the fixed-time stabilization of the discontinuous interconnected system. Especially, the upper bound of the convergence time is estimated by a fixed time, which is independent of initial conditions. Finally, the proposed methodology and results are verified by an example.
{"title":"Fixed-Time Stabilization for Interconnected Systems with Discontinuous Interconnections and Nonidentical Perturbations","authors":"Nannan Rong, Zhanshan Wang, Huaguang Zhang","doi":"10.1109/DDCLS.2018.8516115","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516115","url":null,"abstract":"This paper investigates the fixed-time stabilization issue for a class of nonlinear interconnected systems with discontinuous interconnections and nonidentical perturbations. Firstly, according to the differential inclusion theory, the solutions of such discontinuous interconnected system are defined in the sense of Filippov. In addition, an improved fixed-time lemma, in which the regional bound r can be freely chosen in [0, 1], is proposed to realize the fixed-time stabilization and estimate the settling time. Then, through designing a state feedback controller and utilizing generalized Lyapunov functional method, sufficient criteria are derived to guarantee the fixed-time stabilization of the discontinuous interconnected system. Especially, the upper bound of the convergence time is estimated by a fixed time, which is independent of initial conditions. Finally, the proposed methodology and results are verified by an example.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"13 6 1","pages":"71-76"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78639255","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515983
Darong Sorn, Yong Chen
This paper is about a methodology for the optimization of a Permanent Magnet Brushless Direct Current (PM-BLDC) motor. The most advantage of this proposed method is its mathematical modeling effectiveness. In specific, it is focused on multi-objective optimization by using a Radial Basis Function (RBF) Neural Network simulated in the Matlab environment. The aim of this optimization process was to maximize the efficiency and to minimize the permanent magnet mass, active mass, and volume of the motor. In order to verify results, two-dimensional models were developed and thoroughly analyzed using Finite Element Analysis (FEA) in Ansys-Maxwell. Moreover, the comparison of the RBFNN and Genetic Algorithm (GA) results were also figured out and the comparison showed that the RBFNN has better ability in finding the optimal solutions and also has less computational time than GA.
{"title":"Design optimization of Permanent Magnet Brushless Direct Current Motor using Radial Basis Function Neural Network","authors":"Darong Sorn, Yong Chen","doi":"10.1109/DDCLS.2018.8515983","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515983","url":null,"abstract":"This paper is about a methodology for the optimization of a Permanent Magnet Brushless Direct Current (PM-BLDC) motor. The most advantage of this proposed method is its mathematical modeling effectiveness. In specific, it is focused on multi-objective optimization by using a Radial Basis Function (RBF) Neural Network simulated in the Matlab environment. The aim of this optimization process was to maximize the efficiency and to minimize the permanent magnet mass, active mass, and volume of the motor. In order to verify results, two-dimensional models were developed and thoroughly analyzed using Finite Element Analysis (FEA) in Ansys-Maxwell. Moreover, the comparison of the RBFNN and Genetic Algorithm (GA) results were also figured out and the comparison showed that the RBFNN has better ability in finding the optimal solutions and also has less computational time than GA.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"314 1","pages":"38-43"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75450971","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516014
Mingtao Zhang, Bocheng Chen, You Wu, Wei-wei Deng, Xuelei Zhang, Yi Liu
Current soft sensors for the Mooney viscosity prediction in rubber mixing processes only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is presented to online predict the Mooney viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into the JSELM model to enhance its prediction performance. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. The superiority of JSELM is validated via the industrial Mooney viscosity prediction.
{"title":"Online Semi-supervised Quality Prediction Model for Batch Mixing Process","authors":"Mingtao Zhang, Bocheng Chen, You Wu, Wei-wei Deng, Xuelei Zhang, Yi Liu","doi":"10.1109/DDCLS.2018.8516014","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516014","url":null,"abstract":"Current soft sensors for the Mooney viscosity prediction in rubber mixing processes only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is presented to online predict the Mooney viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into the JSELM model to enhance its prediction performance. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. The superiority of JSELM is validated via the industrial Mooney viscosity prediction.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"893-898"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77966553","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516037
Xiaohong Yin, Xinli Wang, Ximei Liu, R. Chi, Mingming Lin, Fanglin Liu
The air-conditioning system has played an indispensable role in daily life, which can provide a comfortable and healthy residential environment for people. The vapor compressor refrigeration cycle (VCC) system, one of the core cycles of HVAC system, produces a cooling effect. In this research, an iterative learning control (ILC) strategy is proposed for the VCC system. In the first place, the least-square method of system identification has been adopted to obtain a data driven model. Moreover, in order to hold superheat degree of VCC system on a safe level, an ILC controller is developed. Finally, a simulation is provided to test the validity of the proposed controller.
{"title":"An Iterative Learning Controller for Superheat Degree of VCC System","authors":"Xiaohong Yin, Xinli Wang, Ximei Liu, R. Chi, Mingming Lin, Fanglin Liu","doi":"10.1109/DDCLS.2018.8516037","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516037","url":null,"abstract":"The air-conditioning system has played an indispensable role in daily life, which can provide a comfortable and healthy residential environment for people. The vapor compressor refrigeration cycle (VCC) system, one of the core cycles of HVAC system, produces a cooling effect. In this research, an iterative learning control (ILC) strategy is proposed for the VCC system. In the first place, the least-square method of system identification has been adopted to obtain a data driven model. Moreover, in order to hold superheat degree of VCC system on a safe level, an ILC controller is developed. Finally, a simulation is provided to test the validity of the proposed controller.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"9 1","pages":"949-953"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81987417","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515915
Atmane Khellal, Hongbin Ma, Qing Fei
Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett’s theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyperparameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.
{"title":"Ensemble of Extreme Learning Machines for Regression","authors":"Atmane Khellal, Hongbin Ma, Qing Fei","doi":"10.1109/DDCLS.2018.8515915","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515915","url":null,"abstract":"Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett’s theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyperparameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"1052-1057"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87667930","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516008
Shuhua Zhang, Yu Hui, R. Chi
Linearization technique is inevitable for a nonlinear control system design. However, the traditional linearization methods require model information, which is difficult to obtain for the complex nonlinear system. In this article, a new local dynamic linearization method is proposed via a mean-value theorem and can be estimated by using the I/O data only. Then a new adaptive iterative learning control is proposed by using the optimal technology. The simulation verifies the monotonic convergence and practicability of this method.
{"title":"Data-driven Adaptive Iterative Learning Control Based on a Local Dynamic Linearization","authors":"Shuhua Zhang, Yu Hui, R. Chi","doi":"10.1109/DDCLS.2018.8516008","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516008","url":null,"abstract":"Linearization technique is inevitable for a nonlinear control system design. However, the traditional linearization methods require model information, which is difficult to obtain for the complex nonlinear system. In this article, a new local dynamic linearization method is proposed via a mean-value theorem and can be estimated by using the I/O data only. Then a new adaptive iterative learning control is proposed by using the optimal technology. The simulation verifies the monotonic convergence and practicability of this method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"24 1","pages":"184-188"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84846716","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：