Pub Date : 2017-05-26DOI: 10.1109/DDCLS.2017.8068096
Xiangyang Li, W. Ai, Zhiqiang Gao, Senping Tian
Active disturbance rejection control (ADRC) exemplifies the spirit of the data-driven control (DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertainties, without the premise that the detailed mathematical model of the controlled system is given. Instead, it is shown that the information needed for the control system to work at high level of effectiveness can be extracted from the input-output data by the use of the extended state observer (ESO). On the other hand, it is shown in this paper that the robustness of ADRC depends on the effectiveness of ESO. Furthermore, taking advantage of the rich body of knowledge in the existing field of robust control, the estimation error in ESO is analysed and, for the purpose of improved robustness, a unique nonlinear component is added to the conventional ADRC law. The modified ADRC which is a kind of robust ADRC law is validated in simulation for a nonlinear time-varying system with parametric and functional uncertainties. It is shown that the proposed robust ADRC law provides more effective tracking performance than the conventional ADRC when the bandwidth of ESO is not wide enough.
{"title":"Robust ADRC for nonlinear time-varying system with uncertainties","authors":"Xiangyang Li, W. Ai, Zhiqiang Gao, Senping Tian","doi":"10.1109/DDCLS.2017.8068096","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068096","url":null,"abstract":"Active disturbance rejection control (ADRC) exemplifies the spirit of the data-driven control (DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertainties, without the premise that the detailed mathematical model of the controlled system is given. Instead, it is shown that the information needed for the control system to work at high level of effectiveness can be extracted from the input-output data by the use of the extended state observer (ESO). On the other hand, it is shown in this paper that the robustness of ADRC depends on the effectiveness of ESO. Furthermore, taking advantage of the rich body of knowledge in the existing field of robust control, the estimation error in ESO is analysed and, for the purpose of improved robustness, a unique nonlinear component is added to the conventional ADRC law. The modified ADRC which is a kind of robust ADRC law is validated in simulation for a nonlinear time-varying system with parametric and functional uncertainties. It is shown that the proposed robust ADRC law provides more effective tracking performance than the conventional ADRC when the bandwidth of ESO is not wide enough.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124457781","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068100
Yunkai Lv, R. Chi
A new data-driven predictive iterative learning control(ILC) is proposed for same category discrete nonlinear systems in this work. The controller design only depends on the input/output data of the system and does not need explicit mathematical model. More prediction information along the iteration axis is utilized in the learning control law to improve the control performance. The applicability of the proposed methods is proved by simulation experiments.
{"title":"Data-driven adaptive iterative learning predictive control","authors":"Yunkai Lv, R. Chi","doi":"10.1109/DDCLS.2017.8068100","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068100","url":null,"abstract":"A new data-driven predictive iterative learning control(ILC) is proposed for same category discrete nonlinear systems in this work. The controller design only depends on the input/output data of the system and does not need explicit mathematical model. More prediction information along the iteration axis is utilized in the learning control law to improve the control performance. The applicability of the proposed methods is proved by simulation experiments.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133750252","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068085
Hai-Di Dong, Te Ma, Bing He, Jianfei Zheng, Gang Liu
A novel method, consisting of fault detection, rough set generation, element isolation and parameter estimation is presented for multiple-fault diagnosis on analog circuit with tolerance. Firstly, a linear-programming concept is developed to transform fault detection of circuit with limited accessible terminals into measurement to check existence of a feasible solution under tolerance constraints. Secondly, fault characteristic equation is deduced to generate a fault rough set. It is proved that the node voltages of nominal circuit can be used in fault characteristic equation with fault tolerance. Lastly, fault detection of circuit with revised deviation restriction for suspected fault elements is proceeded to locate faulty elements and estimate their parameters. The diagnosis accuracy and parameter identification precision of the method are verified by simulation results.
{"title":"Multiple-fault diagnosis of analog circuit with fault tolerance","authors":"Hai-Di Dong, Te Ma, Bing He, Jianfei Zheng, Gang Liu","doi":"10.1109/DDCLS.2017.8068085","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068085","url":null,"abstract":"A novel method, consisting of fault detection, rough set generation, element isolation and parameter estimation is presented for multiple-fault diagnosis on analog circuit with tolerance. Firstly, a linear-programming concept is developed to transform fault detection of circuit with limited accessible terminals into measurement to check existence of a feasible solution under tolerance constraints. Secondly, fault characteristic equation is deduced to generate a fault rough set. It is proved that the node voltages of nominal circuit can be used in fault characteristic equation with fault tolerance. Lastly, fault detection of circuit with revised deviation restriction for suspected fault elements is proceeded to locate faulty elements and estimate their parameters. The diagnosis accuracy and parameter identification precision of the method are verified by simulation results.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114903263","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068077
Chunyang Fu, Lei Zhang, Xiaojun Guo, Yantao Tian
In this study, an attitude control method for multi-rotor aircraft with output constraints and various disturbances is presented. To prevent output constraints violation, a Barrier Lyapunov Function (BLF) is introduced and the controller is designed based on backstepping algorithm. To enhance the robustness of the system, a linear extended state observer (LESO) from linear active disturbance rejection control (LADRC) is employed to estimate the disturbances and compensate the impact. It is proved that the proposed control algorithm guarantees the tracking error converging to zero asymptotically. Finally, simulation experiments validate the effectiveness and superiority of the presented control method.
{"title":"Attitude control for multi-rotor aircraft with output constraints","authors":"Chunyang Fu, Lei Zhang, Xiaojun Guo, Yantao Tian","doi":"10.1109/DDCLS.2017.8068077","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068077","url":null,"abstract":"In this study, an attitude control method for multi-rotor aircraft with output constraints and various disturbances is presented. To prevent output constraints violation, a Barrier Lyapunov Function (BLF) is introduced and the controller is designed based on backstepping algorithm. To enhance the robustness of the system, a linear extended state observer (LESO) from linear active disturbance rejection control (LADRC) is employed to estimate the disturbances and compensate the impact. It is proved that the proposed control algorithm guarantees the tracking error converging to zero asymptotically. Finally, simulation experiments validate the effectiveness and superiority of the presented control method.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717585","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068052
Qi Xiong, L. Jia, Yong Chen
A novel identification algorithm is presented in this paper for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises. The independent identical distribution (iid) Gaussian random signals are adopted to identify the Wiener system, leading to the separation of linear part from nonlinear counterpart in the identification problem. Therefore, correlation analysis method can be used for the identification of the linear part. Moreover, least-squares-based parameter identification algorithm that can avoid the impact of colored noise is proposed to identify the static nonlinear part. Lastly, an example is used to verify the effectiveness of the proposed method.
{"title":"A SISO neuro-fuzzy wiener model identification by correlation analysis method","authors":"Qi Xiong, L. Jia, Yong Chen","doi":"10.1109/DDCLS.2017.8068052","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068052","url":null,"abstract":"A novel identification algorithm is presented in this paper for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises. The independent identical distribution (iid) Gaussian random signals are adopted to identify the Wiener system, leading to the separation of linear part from nonlinear counterpart in the identification problem. Therefore, correlation analysis method can be used for the identification of the linear part. Moreover, least-squares-based parameter identification algorithm that can avoid the impact of colored noise is proposed to identify the static nonlinear part. Lastly, an example is used to verify the effectiveness of the proposed method.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132779000","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8067716
Xia Wu, Zengqiang Chen, Mingwei Sun, Qinglin Sun
An improved algorithm called active disturbance rejection generalized predictive control which combines advantages of active disturbance rejection control and generalized predictive control is proposed for time-delay systems in this paper to reduce the limitations of active disturbance rejection control (ADRC) in plants with large time-delay and improve the imperfections of generalized predictive control method such as huge computation and strong dependence on mathematical model. The method proposed in this paper can deduce the general solution to the Diophantine equations off-line without the system parameter identification because of the extended state observer. Hence, the online computation burden of this improved method is reduced typically and its application is enlarged. Simulation results show that this proposed design turns out to be a new solution for the large time-delay systems.
{"title":"Active disturbance rejection generalized predictive control and its application on large time-delay systems","authors":"Xia Wu, Zengqiang Chen, Mingwei Sun, Qinglin Sun","doi":"10.1109/DDCLS.2017.8067716","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8067716","url":null,"abstract":"An improved algorithm called active disturbance rejection generalized predictive control which combines advantages of active disturbance rejection control and generalized predictive control is proposed for time-delay systems in this paper to reduce the limitations of active disturbance rejection control (ADRC) in plants with large time-delay and improve the imperfections of generalized predictive control method such as huge computation and strong dependence on mathematical model. The method proposed in this paper can deduce the general solution to the Diophantine equations off-line without the system parameter identification because of the extended state observer. Hence, the online computation burden of this improved method is reduced typically and its application is enlarged. Simulation results show that this proposed design turns out to be a new solution for the large time-delay systems.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846183","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068058
Binbin Gao, Rongmin Cao, Z. Hou, Huixing Zhou
In this paper, the Compact Form Dynamic Linearization based Model-Free Adaptive Control (CFDL-MFAC) algorithm in MIMO (Multiple Input Multiple Output) case is introduced first, then the structure and model of the polishing robot are given and discussed. Next, the CFDL-MFAC in MIMO case is applied to control the polishing robot system. The simulation results show that the CFDL based model-free adaptive control algorithm has a good control performance, especially it can adaptively decouple the coupled outputs for MIMO system.
{"title":"Model-free adaptive MIMO control algorithm application in polishing robot","authors":"Binbin Gao, Rongmin Cao, Z. Hou, Huixing Zhou","doi":"10.1109/DDCLS.2017.8068058","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068058","url":null,"abstract":"In this paper, the Compact Form Dynamic Linearization based Model-Free Adaptive Control (CFDL-MFAC) algorithm in MIMO (Multiple Input Multiple Output) case is introduced first, then the structure and model of the polishing robot are given and discussed. Next, the CFDL-MFAC in MIMO case is applied to control the polishing robot system. The simulation results show that the CFDL based model-free adaptive control algorithm has a good control performance, especially it can adaptively decouple the coupled outputs for MIMO system.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902225","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068056
Panpan Gu, Senping Tian
In this paper, the problem of iterative learning control is considered for a class of switched singular systems. And the considered switched singular systems with arbitrary switching rules are operated in a fixed time interval repetitively. Based on the singular value decomposition method, the switched singular systems are transformed into the switched differential-algebraic systems. Then an iterative learning control algorithm, which is composed of D-type and P-type learning algorithms, is proposed. Using the contraction mapping principle, it is shown that the algorithm can guarantee the state tracking error to converge uniformly to zero as the iteration increases. Finally, a numerical example is constructed to illustrate the effectiveness of the presented algorithm.
{"title":"Iterative learning control for switched singular systems","authors":"Panpan Gu, Senping Tian","doi":"10.1109/DDCLS.2017.8068056","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068056","url":null,"abstract":"In this paper, the problem of iterative learning control is considered for a class of switched singular systems. And the considered switched singular systems with arbitrary switching rules are operated in a fixed time interval repetitively. Based on the singular value decomposition method, the switched singular systems are transformed into the switched differential-algebraic systems. Then an iterative learning control algorithm, which is composed of D-type and P-type learning algorithms, is proposed. Using the contraction mapping principle, it is shown that the algorithm can guarantee the state tracking error to converge uniformly to zero as the iteration increases. Finally, a numerical example is constructed to illustrate the effectiveness of the presented algorithm.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115129579","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 : 2017-05-26DOI: 10.1109/DDCLS.2017.8068046
Zhijiang Lou, Youqing Wang
Several studies have adopted hidden Markov model (HMM) to monitor multimode processes. The drawback of HMM is that its inherent duration probability density is exponential and hence it is inappropriate for the modeling of multimode processes. To address this problem, hidden semi-Markov model (HSMM), which introduces the mode duration probability into HMM, is combined with principal component analysis (PCA) in this paper, named as HSMM-PCA. With the restriction of mode duration probability, HSMM-PCA can successfully identify the operation mode affiliation and build the precise PCA model for each mode. As a result, HSMM-PCA is more sensitive to abnormal conditions and has better fault detection ability for multimode processes.
{"title":"Hidden semi-Markov model based monitoring algorithm for multimode processes","authors":"Zhijiang Lou, Youqing Wang","doi":"10.1109/DDCLS.2017.8068046","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068046","url":null,"abstract":"Several studies have adopted hidden Markov model (HMM) to monitor multimode processes. The drawback of HMM is that its inherent duration probability density is exponential and hence it is inappropriate for the modeling of multimode processes. To address this problem, hidden semi-Markov model (HSMM), which introduces the mode duration probability into HMM, is combined with principal component analysis (PCA) in this paper, named as HSMM-PCA. With the restriction of mode duration probability, HSMM-PCA can successfully identify the operation mode affiliation and build the precise PCA model for each mode. As a result, HSMM-PCA is more sensitive to abnormal conditions and has better fault detection ability for multimode processes.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126330182","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068050
Xu Yang, Jingjing Gao, Lei Zhang, Xiaoli Li, L. Gu, Jiarui Cui, Chao-nan Tong
This paper deals with the issue on air conditioning energy consumption and system monitoring of different data in building. Various environmental parameters inside the building are changed in real time, while the conventional air conditioning energy consumption forecasting with the load simulation software cannot adapt to these variations. Therefore, the air conditioning energy consumption forecasting model is established based on extreme learning machine (ELM) algorithm, within the interior environmental parameters of the building. These parameters are obtained through the building monitoring system which takes into account the environmental parameters, number of people, region area and energy consumption. The performance and effectiveness of the proposed forecasting model of air conditioning energy consumption are demonstrated through a case study of a building from practical engineering.
{"title":"A forecasting method of air conditioning energy consumption based on extreme learning machine algorithm","authors":"Xu Yang, Jingjing Gao, Lei Zhang, Xiaoli Li, L. Gu, Jiarui Cui, Chao-nan Tong","doi":"10.1109/DDCLS.2017.8068050","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068050","url":null,"abstract":"This paper deals with the issue on air conditioning energy consumption and system monitoring of different data in building. Various environmental parameters inside the building are changed in real time, while the conventional air conditioning energy consumption forecasting with the load simulation software cannot adapt to these variations. Therefore, the air conditioning energy consumption forecasting model is established based on extreme learning machine (ELM) algorithm, within the interior environmental parameters of the building. These parameters are obtained through the building monitoring system which takes into account the environmental parameters, number of people, region area and energy consumption. The performance and effectiveness of the proposed forecasting model of air conditioning energy consumption are demonstrated through a case study of a building from practical engineering.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115650910","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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