Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516122
Hongfeng Tao, Q. Wei
For a class of multivariable linear, time-delay systems with actuator fault and measurement bounded disturbances in output, an iterative learning fault estimation algorithm based on extended observer is proposed. The extended observer is designed in terms of the linear matrix inequality technique such that the states and disturbances can be estimated simultaneously in every trials, then the faults and disturbances can be separated for avoiding impact to each other. Afterwards, the iterative learning fault estimation algorithm by defining estimation residual is chosen to adaptively approximate the actuator fault with initial error, then the necessary and sufficient conditions for the existence of the learning algorithm is given through λ norm theory and Bellman-Gronwall inequality, and the uniform convergence criteria of the control algorithm is also discussed. Simulation results verify the feasibility and effectiveness of this algorithm.
{"title":"Iterative Learning Fault Estimation Algorithm for Time-delay Systems Based on Extended Observer","authors":"Hongfeng Tao, Q. Wei","doi":"10.1109/DDCLS.2018.8516122","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516122","url":null,"abstract":"For a class of multivariable linear, time-delay systems with actuator fault and measurement bounded disturbances in output, an iterative learning fault estimation algorithm based on extended observer is proposed. The extended observer is designed in terms of the linear matrix inequality technique such that the states and disturbances can be estimated simultaneously in every trials, then the faults and disturbances can be separated for avoiding impact to each other. Afterwards, the iterative learning fault estimation algorithm by defining estimation residual is chosen to adaptively approximate the actuator fault with initial error, then the necessary and sufficient conditions for the existence of the learning algorithm is given through λ norm theory and Bellman-Gronwall inequality, and the uniform convergence criteria of the control algorithm is also discussed. Simulation results verify the feasibility and effectiveness of this algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"104 1","pages":"277-282"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79234892","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.2018.8516048
Qiao Zhu, Mengen Xu, Meng’qian Zheng
This work focuses on the accurate identification of Lithium-ion battery’s nonlinear parameters by using an iterative learning method. First, the 2nd-order RC model is introduced. Then, when the battery repeatedly implements a discharging trial from SOC 100% to 0%, an iterative learning based recursive least square (IL-RLS) algorithm is presented to accurately identify the nonlinear parameters of the regression model. The essential idea of IL-RLS algorithm is to improve the current parameter estimations by learning the estimation errors of the previous trails. Notably, the IL-RLS algorithm needs to be implemented offline for the long-time repetitive trials, which is the price worth paying to accurately identify the nonlinear parameters. After that, the parameters are identified as the functions of SOC by using the IL-RLS, which are verified by comparing with the result of the classic identification method for current pulses. Finally, by using the classic extended Kalman filter (EKF) as well as the parameters identified by the IL-RLS to estimate the SOC, three dynamic operation conditions are given to show the efficiency of the IL-RLS, where all the SOC estimation errors are less than 2%.
{"title":"Iterative Learning Based Model Identification and State of Charge Estimation of Lithium-Ion Battery","authors":"Qiao Zhu, Mengen Xu, Meng’qian Zheng","doi":"10.1109/DDCLS.2018.8516048","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516048","url":null,"abstract":"This work focuses on the accurate identification of Lithium-ion battery’s nonlinear parameters by using an iterative learning method. First, the 2nd-order RC model is introduced. Then, when the battery repeatedly implements a discharging trial from SOC 100% to 0%, an iterative learning based recursive least square (IL-RLS) algorithm is presented to accurately identify the nonlinear parameters of the regression model. The essential idea of IL-RLS algorithm is to improve the current parameter estimations by learning the estimation errors of the previous trails. Notably, the IL-RLS algorithm needs to be implemented offline for the long-time repetitive trials, which is the price worth paying to accurately identify the nonlinear parameters. After that, the parameters are identified as the functions of SOC by using the IL-RLS, which are verified by comparing with the result of the classic identification method for current pulses. Finally, by using the classic extended Kalman filter (EKF) as well as the parameters identified by the IL-RLS to estimate the SOC, three dynamic operation conditions are given to show the efficiency of the IL-RLS, where all the SOC estimation errors are less than 2%.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"16 1","pages":"222-228"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84556011","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.2018.8516028
Junyao You, Huan Xu, Yanjun Liu, J. Chen
A compressive sampling matching pursuit (CoSaMP) iterative algorithm is proposed in this paper to identify parameters and time-delays of a class of closed-loop systems where the forward channel is a CARMA model. Due to the unknown time-delays of both the feedback controller and the controlled plant, a high dimensional identification model with a sparse parameter vector is derived by using an overparameterized method. Then combining the CoSaMP algorithm with the iterative idea, the parameter vector is estimated and the unmeasurable noise items are updated in each iteration. Finally, the parameters of the feedback controller are extracted based on the model equivalence principle and time-delays are estimated according to the sparse characteristic of the parameter vector. The proposed method can simultaneously estimate the parameters and time-delays from a small number of sampled data. The simulation results illustrate that the proposed algorithm is effective.
{"title":"Iterative Identification for A Class of Closed-loop Systems Based on A Greedy Algorithm","authors":"Junyao You, Huan Xu, Yanjun Liu, J. Chen","doi":"10.1109/DDCLS.2018.8516028","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516028","url":null,"abstract":"A compressive sampling matching pursuit (CoSaMP) iterative algorithm is proposed in this paper to identify parameters and time-delays of a class of closed-loop systems where the forward channel is a CARMA model. Due to the unknown time-delays of both the feedback controller and the controlled plant, a high dimensional identification model with a sparse parameter vector is derived by using an overparameterized method. Then combining the CoSaMP algorithm with the iterative idea, the parameter vector is estimated and the unmeasurable noise items are updated in each iteration. Finally, the parameters of the feedback controller are extracted based on the model equivalence principle and time-delays are estimated according to the sparse characteristic of the parameter vector. The proposed method can simultaneously estimate the parameters and time-delays from a small number of sampled data. The simulation results illustrate that the proposed algorithm is effective.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"2 1","pages":"934-938"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82648819","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.2018.8515980
Xiongnan He, Songchen Jiang, Qiuye Sun
Nowadays, more and more residential cars apply various of services of energy saving to help themselves improve performances and decrease cost. As for the car air conditioning, some put forward ideas that using neuron-fuzzy method can precisely control the cooling capacity, the other hold the view that power line communication based photovoltaic (PV) system can effectively manage the energy. In this paper, it aims to deal with the shortcomings that aforementioned do not take the realistic environment and the neuron-fuzzy method’s disadvantages into consideration. As a result, this paper comes up an intelligent car temperature control system(ICTCS),which, comparing with conventional temperature control systems, has two main advantages——one is using three criterions, namely light intensity outside cars(I),temperature inside cars(T) and sunshine incident angle(α), to judge what kind of environment the car is in on earth and decide car cooling capacity over , the other is applying neuron-fuzzy system to train the comprehensive temperature to try its best to decrease faster. It will refrigerate in different stalls in the standard of difference between temperature inside cars and calculated most suitable temperature. Applying the above system into actual experiments, we can find under the premise that cooling effect stays nearly the same, the energy consumption gets decreased, which is to say, the ICTCS gets good results.
{"title":"An Intelligent Car Temperature Control System","authors":"Xiongnan He, Songchen Jiang, Qiuye Sun","doi":"10.1109/DDCLS.2018.8515980","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515980","url":null,"abstract":"Nowadays, more and more residential cars apply various of services of energy saving to help themselves improve performances and decrease cost. As for the car air conditioning, some put forward ideas that using neuron-fuzzy method can precisely control the cooling capacity, the other hold the view that power line communication based photovoltaic (PV) system can effectively manage the energy. In this paper, it aims to deal with the shortcomings that aforementioned do not take the realistic environment and the neuron-fuzzy method’s disadvantages into consideration. As a result, this paper comes up an intelligent car temperature control system(ICTCS),which, comparing with conventional temperature control systems, has two main advantages——one is using three criterions, namely light intensity outside cars(I),temperature inside cars(T) and sunshine incident angle(α), to judge what kind of environment the car is in on earth and decide car cooling capacity over , the other is applying neuron-fuzzy system to train the comprehensive temperature to try its best to decrease faster. It will refrigerate in different stalls in the standard of difference between temperature inside cars and calculated most suitable temperature. Applying the above system into actual experiments, we can find under the premise that cooling effect stays nearly the same, the energy consumption gets decreased, which is to say, the ICTCS gets good results.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"27 1","pages":"1058-1063"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83273852","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.2018.8515978
Hao Luo, Kuan Li, M. Huo, Shen Yin, O. Kaynak
This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.
{"title":"A Data-Driven Process Monitoring Approach with Disturbance Decoupling*","authors":"Hao Luo, Kuan Li, M. Huo, Shen Yin, O. Kaynak","doi":"10.1109/DDCLS.2018.8515978","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515978","url":null,"abstract":"This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"569-574"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82621118","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.2018.8516060
Yunkai Lv, R. Chi, Na Lin
In this work, a control scheme with compensation along the iteration axis is discussed for discrete time nonlinear systems with random data loss. The loss of output data from sensor to controller is considered, and the data missing is described through a variable satisfying the Bernoulli distribution. The lost output value is estimated by using the time-varying parameter and the output value of the last iteration to compensate the influence of data loss on the plant. A numerical simulation example verifies the validity of the algorithm.
{"title":"A Data-driven Optimal Iterative Learning Control with Data Loss Compensation","authors":"Yunkai Lv, R. Chi, Na Lin","doi":"10.1109/DDCLS.2018.8516060","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516060","url":null,"abstract":"In this work, a control scheme with compensation along the iteration axis is discussed for discrete time nonlinear systems with random data loss. The loss of output data from sensor to controller is considered, and the data missing is described through a variable satisfying the Bernoulli distribution. The lost output value is estimated by using the time-varying parameter and the output value of the last iteration to compensate the influence of data loss on the plant. A numerical simulation example verifies the validity of the algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"5 1","pages":"180-183"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82706257","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.2018.8516103
Lizhi Cui, Xuhui Bu, Junqi Yang, Yi Yang, Weina He
Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which needed to know the number of sources in advance. In this paper, a new model based on the Generalized Reference Curve Model (GRCM) for the DOA estimation was proposed, which do not need to know the sources number in advance. And the comparison of the performance between the proposed model and the MUSIC model was given to demonstrate the effectiveness of our method. The algorithm of Multi-target Intermittent Particle Swarm Optimization (MIPSO) was adopted to solve the model proposed in this paper, and the performance of the MIPSO was analyzed through a simulation. The result shown that:(1) the GRCM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with much shorter operation time and high precision.
{"title":"Direction of Arrival Estimation Based on Generalized Reference Curve Model","authors":"Lizhi Cui, Xuhui Bu, Junqi Yang, Yi Yang, Weina He","doi":"10.1109/DDCLS.2018.8516103","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516103","url":null,"abstract":"Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which needed to know the number of sources in advance. In this paper, a new model based on the Generalized Reference Curve Model (GRCM) for the DOA estimation was proposed, which do not need to know the sources number in advance. And the comparison of the performance between the proposed model and the MUSIC model was given to demonstrate the effectiveness of our method. The algorithm of Multi-target Intermittent Particle Swarm Optimization (MIPSO) was adopted to solve the model proposed in this paper, and the performance of the MIPSO was analyzed through a simulation. The result shown that:(1) the GRCM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with much shorter operation time and high precision.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"78 1","pages":"650-653"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91238161","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.2018.8516031
Yang Liu, Wenxu Yan, Dezhi Xu, Weilin Yang, Wentao Zhang
In conventional PMSM DTC system, the electromagnetic torque displays the excellent dynamic performance. While, the dynamic performance of the speed is not satisfying. In this paper, the model free intelligent proportional-integral controller is designed to improve the dynamic performance of the speed. The purpose of this work is to compare the proposed controller with the classical PI controller for the dynamic performance of the speed. Simulation results show the effectiveness of the proposed controller in ameliorating the dynamic performance of the speed.
{"title":"Direct Torque Control of PMSM Based on Model Free iPI Controller","authors":"Yang Liu, Wenxu Yan, Dezhi Xu, Weilin Yang, Wentao Zhang","doi":"10.1109/DDCLS.2018.8516031","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516031","url":null,"abstract":"In conventional PMSM DTC system, the electromagnetic torque displays the excellent dynamic performance. While, the dynamic performance of the speed is not satisfying. In this paper, the model free intelligent proportional-integral controller is designed to improve the dynamic performance of the speed. The purpose of this work is to compare the proposed controller with the classical PI controller for the dynamic performance of the speed. Simulation results show the effectiveness of the proposed controller in ameliorating the dynamic performance of the speed.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"17 1","pages":"970-973"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89341340","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.2018.8516101
Penghu Wang, Hao Tang, K. Lv
The automatic generation control (AGC) in isolated microgrid with multiple distributed energy resources is concerned in this study. First, the load frequency control (LFC) model of an isolated microgrid, which contains diesel engine generators, super-magnetic magnetic energy storage, wind turbines and photovoltaic power system, is established through the analysis of the power generation characteristics of each distributed generation (DG). The LFC model of an isolated microgrid is built by MATLAB/Simulink with diesel generators as frequency control units. Based on the AGC principle of power grid, the AGC controller of the microgrid system is designed by the Q learning algorithm based on the discount compensation model to complete the frequency control. The simulation results verify the feasibility of the isolated microgrid model, showing the efficient dynamic performance of Q controller by compared with PI controller.
{"title":"Simulation Model for the AGC System of Isolated Microgrid Based on Q-learning Method","authors":"Penghu Wang, Hao Tang, K. Lv","doi":"10.1109/DDCLS.2018.8516101","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516101","url":null,"abstract":"The automatic generation control (AGC) in isolated microgrid with multiple distributed energy resources is concerned in this study. First, the load frequency control (LFC) model of an isolated microgrid, which contains diesel engine generators, super-magnetic magnetic energy storage, wind turbines and photovoltaic power system, is established through the analysis of the power generation characteristics of each distributed generation (DG). The LFC model of an isolated microgrid is built by MATLAB/Simulink with diesel generators as frequency control units. Based on the AGC principle of power grid, the AGC controller of the microgrid system is designed by the Q learning algorithm based on the discount compensation model to complete the frequency control. The simulation results verify the feasibility of the isolated microgrid model, showing the efficient dynamic performance of Q controller by compared with PI controller.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"31 1","pages":"1213-1217"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89540769","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.2018.8515911
Kezhen Han, X. Zong, Shi Li
In this paper, the data-driven robust preview control problem is addressed based on Markov parameters sequence identification and augmented modelling technique. The involved analysis and synthesis are composed of three parts. First, data-based state-space model is established by augmenting input/output data, finite window previewable signals and tracking errors. Then, the Markov parameters sequence is identified, which enables the determination of data model matrices. In the following, the mixed linear quadratic (LQ) and H∞ criterions are used to optimize the robust preview control gains, and the specified preview control policy containing data feedback control, integral operation and preview action is finally obtained. The application to injection velocity control of injection molding process verifies the effectiveness of proposed results.
{"title":"Markov Parameters Sequence Identification Oriented Data-Driven LQ=H∞ Robust Preview Control","authors":"Kezhen Han, X. Zong, Shi Li","doi":"10.1109/DDCLS.2018.8515911","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515911","url":null,"abstract":"In this paper, the data-driven robust preview control problem is addressed based on Markov parameters sequence identification and augmented modelling technique. The involved analysis and synthesis are composed of three parts. First, data-based state-space model is established by augmenting input/output data, finite window previewable signals and tracking errors. Then, the Markov parameters sequence is identified, which enables the determination of data model matrices. In the following, the mixed linear quadratic (LQ) and H∞ criterions are used to optimize the robust preview control gains, and the specified preview control policy containing data feedback control, integral operation and preview action is finally obtained. The application to injection velocity control of injection molding process verifies the effectiveness of proposed results.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"15 1","pages":"17-21"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89987077","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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