Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515964
Dawei Hou, J. Na, Guanbin Gao, Guang Li
In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
{"title":"Data-Driven Adaptive Optimal Tracking Control for Completely Unknown Systems","authors":"Dawei Hou, J. Na, Guanbin Gao, Guang Li","doi":"10.1109/DDCLS.2018.8515964","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515964","url":null,"abstract":"In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"1039-1044"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90052256","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}
The paper solves the problem of decentralized robust adaptive output-feedback control for a class of large-scale stochastic time-delay nonlinear systems. Simulation results show that the closed-loop system is globally stable in probability and the output signals can converge to a small neighborhood of the origin in probability under some milder conditions.
{"title":"Decentralized Robust Adaptive Output-Feedback Control for A Class of Large-Scale Stochastic Time-Delay Nonlinear Systems","authors":"Qian Wang, Qiangde Wang, Zhengqiang Zhang, Chunling Wei","doi":"10.1109/DDCLS.2018.8516110","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516110","url":null,"abstract":"The paper solves the problem of decentralized robust adaptive output-feedback control for a class of large-scale stochastic time-delay nonlinear systems. Simulation results show that the closed-loop system is globally stable in probability and the output signals can converge to a small neighborhood of the origin in probability under some milder conditions.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"160-165"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72923096","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.2018.8516038
Ma Yabin, Chen Chen, Shu Qiqi, Wang Jian, Li Hongliang, Huang Darong
A novel method based on Hilbert Transform (HT) and Empirical Mode Decomposition (EMD) algorithm is proposed in this paper, which separates time series into intrinsic mode functions (IMFs) with different time scales and applies the Hilbert transformation for every IMF to obtain the Hilbert spectrum. Firstly, relevant theories of the proposed method are introduced. Then, based on these theoretical introductions, the fault vibration signals of rolling bearing are dealt with related algorithm. The research results demonstrate that the characteristic frequency of bearing fault can be obtained by proposed method, which is more effective compared with existing algorithm.
{"title":"Fault Diagnosis of Rolling Bearing based on EMD Combined with HHT Envelope and Wavelet Spectrum Transform","authors":"Ma Yabin, Chen Chen, Shu Qiqi, Wang Jian, Li Hongliang, Huang Darong","doi":"10.1109/DDCLS.2018.8516038","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516038","url":null,"abstract":"A novel method based on Hilbert Transform (HT) and Empirical Mode Decomposition (EMD) algorithm is proposed in this paper, which separates time series into intrinsic mode functions (IMFs) with different time scales and applies the Hilbert transformation for every IMF to obtain the Hilbert spectrum. Firstly, relevant theories of the proposed method are introduced. Then, based on these theoretical introductions, the fault vibration signals of rolling bearing are dealt with related algorithm. The research results demonstrate that the characteristic frequency of bearing fault can be obtained by proposed method, which is more effective compared with existing algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"481-485"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78794807","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.2018.8516062
Qiongxia Yu, Xuhui Bu, R. Chi, Z. Hou
High-speed trains always operate from the same departure station to the same terminal station and hence iterative learning control (ILC) is an appropriate approach for automatic train control. However, due to complex environment and unknown uncertainties, the train may not arrive at the terminal station on time, or earlier and later than the schedule time in each operation. To address this problem, a modified proportional-type (P-type) ILC is presented where the trial length in each operation can be randomly varying. Moreover, the convergence condition in 2-norm is also derived through rigorous analysis. The effectiveness of the modified P-type ILC is further verified through simulations.
{"title":"Modified P-Type ILC for High-Speed Trains with Varying Trial Lengths","authors":"Qiongxia Yu, Xuhui Bu, R. Chi, Z. Hou","doi":"10.1109/DDCLS.2018.8516062","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516062","url":null,"abstract":"High-speed trains always operate from the same departure station to the same terminal station and hence iterative learning control (ILC) is an appropriate approach for automatic train control. However, due to complex environment and unknown uncertainties, the train may not arrive at the terminal station on time, or earlier and later than the schedule time in each operation. To address this problem, a modified proportional-type (P-type) ILC is presented where the trial length in each operation can be randomly varying. Moreover, the convergence condition in 2-norm is also derived through rigorous analysis. The effectiveness of the modified P-type ILC is further verified through simulations.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"34 1","pages":"1006-1010"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78943223","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.2018.8516035
M. Chiang, An-Sheng Liu, L. Fu
This paper considers the optimal consensus of multi-agent systems using reinforcement learning control. The system is nonlinear and the number of agents can be large. The control objective is to design the controllers for each agent such that all the agents will be consensus to the leader agent. We use the Actor-Critic Network and the Deterministic Policy Gradient method to realize the controller. The policy iteration algorithm is discussed and many simulations are provided to validate the result.
{"title":"Reinforcement Learning Control for Consensus of the Leader-Follower Multi-Agent Systems","authors":"M. Chiang, An-Sheng Liu, L. Fu","doi":"10.1109/DDCLS.2018.8516035","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516035","url":null,"abstract":"This paper considers the optimal consensus of multi-agent systems using reinforcement learning control. The system is nonlinear and the number of agents can be large. The control objective is to design the controllers for each agent such that all the agents will be consensus to the leader agent. We use the Actor-Critic Network and the Deterministic Policy Gradient method to realize the controller. The policy iteration algorithm is discussed and many simulations are provided to validate the result.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"20 1","pages":"1152-1157"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76619043","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.2018.8515909
Binquan Zhou, Zhuoqi Wang, Yueyang Zhai, H. Yuan
We propose a couple of data-driven analysis methods for the state controllability and state observability of a class of discrete linear time-invariant (LTI) systems with delays, which have unknown parameter matrices. To analyze the state controlla-bility and the state observability, these data-driven methods first transform the system model into an augmented state-space model, and then use the state/output data that were previously measured, to directly build the controllability/observability matrices of this augmented model. Our methods have two main advantages over the traditional model-based characteristics analysis approaches. First, the unknown parameter matrices are not necessary to be identified for verifying the state controllability/observability of the system, but these characteristics can be directly verified according to the measured data, thus our methods have less workload. Second, their computational complexity is lower for the construction of the state controllability/observability matrices.
{"title":"Data-Driven Analysis Methods for Controllability and Observability of A Class of Discrete LTI Systems with Delays","authors":"Binquan Zhou, Zhuoqi Wang, Yueyang Zhai, H. Yuan","doi":"10.1109/DDCLS.2018.8515909","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515909","url":null,"abstract":"We propose a couple of data-driven analysis methods for the state controllability and state observability of a class of discrete linear time-invariant (LTI) systems with delays, which have unknown parameter matrices. To analyze the state controlla-bility and the state observability, these data-driven methods first transform the system model into an augmented state-space model, and then use the state/output data that were previously measured, to directly build the controllability/observability matrices of this augmented model. Our methods have two main advantages over the traditional model-based characteristics analysis approaches. First, the unknown parameter matrices are not necessary to be identified for verifying the state controllability/observability of the system, but these characteristics can be directly verified according to the measured data, thus our methods have less workload. Second, their computational complexity is lower for the construction of the state controllability/observability matrices.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"147 1","pages":"380-384"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76865873","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.2018.8515935
Hui Yu, Z. Shuhua, Chi Rong-hu
In this work, a novel data-driven optimal ILC with an extended state observer for a class of nonlinear non-affine discrete-time repetitive system has been proposed. The main feature of the approach is that the controller design depends merely on the I/O data, and an ESO has been introduced for the estimation of disturbance and uncertainty. The final simulation results verify the effectiveness of the proposed method.
{"title":"A Data-driven Optimal ILC Method Incorporated with Extended State Observer for Nonlinear Discrete-time Repetitive Systems","authors":"Hui Yu, Z. Shuhua, Chi Rong-hu","doi":"10.1109/DDCLS.2018.8515935","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515935","url":null,"abstract":"In this work, a novel data-driven optimal ILC with an extended state observer for a class of nonlinear non-affine discrete-time repetitive system has been proposed. The main feature of the approach is that the controller design depends merely on the I/O data, and an ESO has been introduced for the estimation of disturbance and uncertainty. The final simulation results verify the effectiveness of the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"516 1","pages":"77-80"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77126248","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.2018.8516023
Zidong Ai
In this work, we conduct stability analysis for a class of multi-module impulsive control systems via an event-driven scheme. By designing some event-driven conditions and a proper event-driven impulsive control law, we establish some sufficient stability criteria for the considered systems. The proposed event-driven control scheme is advantageous to reduce the utilization of communication and computation resources. Further, we study the impulsive synchronization problem for two continuous-time dynamical systems with different initial values. Finally, an example of Chua’s circuit with simulations results are provided to illustrate the validity of the method.
{"title":"Exponential Stability for Event-Driven Impulsive Control Systems","authors":"Zidong Ai","doi":"10.1109/DDCLS.2018.8516023","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516023","url":null,"abstract":"In this work, we conduct stability analysis for a class of multi-module impulsive control systems via an event-driven scheme. By designing some event-driven conditions and a proper event-driven impulsive control law, we establish some sufficient stability criteria for the considered systems. The proposed event-driven control scheme is advantageous to reduce the utilization of communication and computation resources. Further, we study the impulsive synchronization problem for two continuous-time dynamical systems with different initial values. Finally, an example of Chua’s circuit with simulations results are provided to illustrate the validity of the method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"704-707"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83802633","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.2018.8515997
W. Zhou, Miao Yu
This technical note addresses an adaptive iterative learning control (AILC) problem for nonlinear dynamical systems with partially unknown iteration-varying parameter. Referring to the scheme of state-space, an AILC effort is presented for randomly varying reference tracking together with initial shift problem in iteration domain. Furthermore, the AILC technique is extended to systems with several parameters in discussion. A simulation example confirms the validity of the proposed method.
{"title":"Adaptive Iterative Learning Control Mechanism for Nonlinear Systems subject to High-Order Internal Model","authors":"W. Zhou, Miao Yu","doi":"10.1109/DDCLS.2018.8515997","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515997","url":null,"abstract":"This technical note addresses an adaptive iterative learning control (AILC) problem for nonlinear dynamical systems with partially unknown iteration-varying parameter. Referring to the scheme of state-space, an AILC effort is presented for randomly varying reference tracking together with initial shift problem in iteration domain. Furthermore, the AILC technique is extended to systems with several parameters in discussion. A simulation example confirms the validity of the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"31 1","pages":"599-604"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72948754","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.2018.8515987
Xiaocheng Zhang, Wenchao Xue, H. Fang, Xingkang He
This paper studies the state estimation problem for a class of continuous-time stochastic systems with unknown nonlinear dynamics and measurement noise. Enlightened by the extended state observer (ESO) in timely estimating both the internal unknown dynamics and the external disturbance of systems, the paper constructs the extended state based KalmanBucy filter (ESKBF) to achieve better filtering performance. It is shown that ESKBF can provide the upper bound of the covariance matrix of estimation error, which is critical in evaluating the filtering precision. Besides, the stability of ESKBF is rigorously proven in the presence of unknown nonlinear dynamics, while the stability of traditional Kalman-Bucy filter is hard to be guaranteed under the same condition. Moreover, the asymptotic optimality of ESKBF for time-invariant system under constant disturbance is given. Finally, numerical simulations show the effectiveness of the method.
{"title":"On Extended State Based Kalman-Bucy Filter","authors":"Xiaocheng Zhang, Wenchao Xue, H. Fang, Xingkang He","doi":"10.1109/DDCLS.2018.8515987","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515987","url":null,"abstract":"This paper studies the state estimation problem for a class of continuous-time stochastic systems with unknown nonlinear dynamics and measurement noise. Enlightened by the extended state observer (ESO) in timely estimating both the internal unknown dynamics and the external disturbance of systems, the paper constructs the extended state based KalmanBucy filter (ESKBF) to achieve better filtering performance. It is shown that ESKBF can provide the upper bound of the covariance matrix of estimation error, which is critical in evaluating the filtering precision. Besides, the stability of ESKBF is rigorously proven in the presence of unknown nonlinear dynamics, while the stability of traditional Kalman-Bucy filter is hard to be guaranteed under the same condition. Moreover, the asymptotic optimality of ESKBF for time-invariant system under constant disturbance is given. Finally, numerical simulations show the effectiveness of the method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"81 1","pages":"1158-1163"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76716184","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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