Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10165924
Xiaofang Li, Zhiwen Wang, Yanrong Lu, Hongtao Sun, Yuying Wang
This paper studies intermittent semi-global containment control for continuous descriptor multi-agent systems (MASs) with input saturation under undirected communication topology. The premise of most of the existing work is based on continuous communication between agents, however, when the communication network between agents are disturbed or attacked, the agents can only communicate intermittently with their neighbors. In view of this, firstly, using the low gain method of parametric generalized algebraic Riccati equation (GARE), we propose a distributed aperiodic intermittent containment control strategy based on state feedback. Secondly, Using the generalized Lyapunov stability theorem, exponential stability theory and mathematical induction, the sufficient conditions for realizing intermittent semi-global containment control are obtained when the control rate of the descriptor MASs is larger than a fixed value. Lastly, numerical simulation is used to verify that the control strategy is correct.
{"title":"Intermittent Semi-Global Containment Control of Descriptor Multi-Agent Systems with Input Saturation","authors":"Xiaofang Li, Zhiwen Wang, Yanrong Lu, Hongtao Sun, Yuying Wang","doi":"10.1109/DDCLS58216.2023.10165924","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10165924","url":null,"abstract":"This paper studies intermittent semi-global containment control for continuous descriptor multi-agent systems (MASs) with input saturation under undirected communication topology. The premise of most of the existing work is based on continuous communication between agents, however, when the communication network between agents are disturbed or attacked, the agents can only communicate intermittently with their neighbors. In view of this, firstly, using the low gain method of parametric generalized algebraic Riccati equation (GARE), we propose a distributed aperiodic intermittent containment control strategy based on state feedback. Secondly, Using the generalized Lyapunov stability theorem, exponential stability theory and mathematical induction, the sufficient conditions for realizing intermittent semi-global containment control are obtained when the control rate of the descriptor MASs is larger than a fixed value. Lastly, numerical simulation is used to verify that the control strategy is correct.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117209044","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166839
Mingming Lin, R. Chi, Na Lin, Zhiqing Liu
In this paper, we introduce the concept of data similarity for a class of nonlinear time-varying discrete time systems. By combining the large data processing method with the iterative learning control algorithm, an active iterative learning control algorithm is proposed. Different from the updating method in traditional iterative learning control algorithm, in this paper, the control input of the current iteration is given by using K-means algorithm and support vector machine (SVM) algorithm to pick out the closest state control input from the historical database. The control algorithm is verified by ethanol fermentation process. The simulation result shows that the active iterative learning control scheme based on data similarity can greatly improve the convergence speed of the system compared with the traditional one. It is worth noting that the historical data is used in the update process, so it will not affect the convergence and stability of the system, and it has good popularization value.
{"title":"Active iterative learning control based on big data","authors":"Mingming Lin, R. Chi, Na Lin, Zhiqing Liu","doi":"10.1109/DDCLS58216.2023.10166839","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166839","url":null,"abstract":"In this paper, we introduce the concept of data similarity for a class of nonlinear time-varying discrete time systems. By combining the large data processing method with the iterative learning control algorithm, an active iterative learning control algorithm is proposed. Different from the updating method in traditional iterative learning control algorithm, in this paper, the control input of the current iteration is given by using K-means algorithm and support vector machine (SVM) algorithm to pick out the closest state control input from the historical database. The control algorithm is verified by ethanol fermentation process. The simulation result shows that the active iterative learning control scheme based on data similarity can greatly improve the convergence speed of the system compared with the traditional one. It is worth noting that the historical data is used in the update process, so it will not affect the convergence and stability of the system, and it has good popularization value.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116948382","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166707
Lichen Jiang, Guanbin Gao, Ji Na, Yashan Xing
Industrial robots perform tasks through tools installed on the end flange. The position and orientation of the tools are essential factors that affect the motion accuracy of industrial robots. However, existing calibration methods for the tool frame mainly depend on manual observation. To solve this problem, this paper proposes an automatic calibration method of the tool frame based on the fact that the accurate position and orientation of the tools relative to the flange can be obtained through the calibration of the tool frame. First, the tool carried by the robot moves in a uniform circle at different heights. The origin and orientation calibration models of the tool frame are established respectively based on the similarity of the motion track of each point on a rigid body. Through two pairs of vertically mounted laser beam sensors, the time when the tool passes through the laser beam and the position of the corresponding robot flange are obtained. Second, the simulation platform with the robot and sensors is built in a 3-dimensional software to simulate the motion and measurement of the tool. The data required for calibration are acquired, by which the parameters of the origin and orientation of the tool frame are identified and compensated in the motion controller of the robot. Finally, the accuracy of the tool frame before and after calibration is tested in the simulation platform, and the simulation results verify the effectiveness of the proposed model and method.
{"title":"A fast calibration method of the tool frame for industrial robots","authors":"Lichen Jiang, Guanbin Gao, Ji Na, Yashan Xing","doi":"10.1109/DDCLS58216.2023.10166707","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166707","url":null,"abstract":"Industrial robots perform tasks through tools installed on the end flange. The position and orientation of the tools are essential factors that affect the motion accuracy of industrial robots. However, existing calibration methods for the tool frame mainly depend on manual observation. To solve this problem, this paper proposes an automatic calibration method of the tool frame based on the fact that the accurate position and orientation of the tools relative to the flange can be obtained through the calibration of the tool frame. First, the tool carried by the robot moves in a uniform circle at different heights. The origin and orientation calibration models of the tool frame are established respectively based on the similarity of the motion track of each point on a rigid body. Through two pairs of vertically mounted laser beam sensors, the time when the tool passes through the laser beam and the position of the corresponding robot flange are obtained. Second, the simulation platform with the robot and sensors is built in a 3-dimensional software to simulate the motion and measurement of the tool. The data required for calibration are acquired, by which the parameters of the origin and orientation of the tool frame are identified and compensated in the motion controller of the robot. Finally, the accuracy of the tool frame before and after calibration is tested in the simulation platform, and the simulation results verify the effectiveness of the proposed model and method.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116292175","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167264
Chiqiang Liu, Dazi Li
With the wide application of multi-intelligent reinforcement learning (MARL), its development becomes more and more mature. Multi-agent Proximal Policy Optimization (MAPPO) extended by Proximal Policy Optimization (PPO) algorithm has attracted the attention of researchers with its superior performance. However, the increase in the number of agents in multi-agent cooperation tasks leads to overfitting problems and suboptimal policies due to the fixed clip range that limits the step size of updates. In this paper, MAPPO via Non-fixed Value Clipping (NVC-MAPPO) algorithm is proposed based on MAPPO, and Gaussian noise is introduced in the value function and the clipping function, respectively, and rewriting the clipping function into a form called non-fixed value clipping function. In the end, experiments are conducted on StarCraftII Multi-Agent Challenge (SMAC) to verify that the algorithm can effectively prevent the step size from changing too much while enhancing the exploration ability of the agents, which has improved the performance compared with MAPPO.
{"title":"Multi-agent Proximal Policy Optimization via Non-fixed Value Clipping","authors":"Chiqiang Liu, Dazi Li","doi":"10.1109/DDCLS58216.2023.10167264","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167264","url":null,"abstract":"With the wide application of multi-intelligent reinforcement learning (MARL), its development becomes more and more mature. Multi-agent Proximal Policy Optimization (MAPPO) extended by Proximal Policy Optimization (PPO) algorithm has attracted the attention of researchers with its superior performance. However, the increase in the number of agents in multi-agent cooperation tasks leads to overfitting problems and suboptimal policies due to the fixed clip range that limits the step size of updates. In this paper, MAPPO via Non-fixed Value Clipping (NVC-MAPPO) algorithm is proposed based on MAPPO, and Gaussian noise is introduced in the value function and the clipping function, respectively, and rewriting the clipping function into a form called non-fixed value clipping function. In the end, experiments are conducted on StarCraftII Multi-Agent Challenge (SMAC) to verify that the algorithm can effectively prevent the step size from changing too much while enhancing the exploration ability of the agents, which has improved the performance compared with MAPPO.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121405751","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166958
Minghao Jiang, Dong-dong Zheng
In this paper, a novel adaptive impedance control strategy for the flexible joint robot (FJR) is proposed. To simplify the controller design process, the singular perturbation technique is used to decompose the original high-order system into low-order subsystems. To reduce the mismatch of the system model, the neural network is used to estimate the friction and unknown system dynamic, where an improved optimal bounded ellipsoid (IOBE) algorithm is adopted to optimize the weight matrix of the neural network, which can fix the learning gain matrix vanishing or unbounded growth in traditional OBE algorithm. Different from traditional impedance controllers with fixed impedance parameters, in this paper, the variable stiffness and damping coefficients are used, which can maintain a fast response speed when the FJR is moving freely and can show more compliance characteristics when the FJR is interacting with the environment. The stability of the closed-loop system is proved via the Lyapunov approach and the effectiveness of the algorithm is verified by simulations.
{"title":"Neural network-based variable impedance control of flexible joint robots","authors":"Minghao Jiang, Dong-dong Zheng","doi":"10.1109/DDCLS58216.2023.10166958","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166958","url":null,"abstract":"In this paper, a novel adaptive impedance control strategy for the flexible joint robot (FJR) is proposed. To simplify the controller design process, the singular perturbation technique is used to decompose the original high-order system into low-order subsystems. To reduce the mismatch of the system model, the neural network is used to estimate the friction and unknown system dynamic, where an improved optimal bounded ellipsoid (IOBE) algorithm is adopted to optimize the weight matrix of the neural network, which can fix the learning gain matrix vanishing or unbounded growth in traditional OBE algorithm. Different from traditional impedance controllers with fixed impedance parameters, in this paper, the variable stiffness and damping coefficients are used, which can maintain a fast response speed when the FJR is moving freely and can show more compliance characteristics when the FJR is interacting with the environment. The stability of the closed-loop system is proved via the Lyapunov approach and the effectiveness of the algorithm is verified by simulations.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123401279","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166520
Honghai Ji, Yuxin Wu, Shida Liu, Li Wang, Lingling Fan, Shuangshuang Xiong
This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consider packet loss and topology change. Thus, based on Kalman consensus filtering algorithm and Data-driven filtering technique, we proposed a modified Data-driven Distributed information-weighted Kalman Consensus Filter to estimate the state. Finally, the effectiveness of the designed algorithm is validated by a simulation example.
{"title":"A Modified Data-driven Distributed Information-Weighted Kalman Consensus Filtering with Switching Topology and Packet Loss","authors":"Honghai Ji, Yuxin Wu, Shida Liu, Li Wang, Lingling Fan, Shuangshuang Xiong","doi":"10.1109/DDCLS58216.2023.10166520","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166520","url":null,"abstract":"This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consider packet loss and topology change. Thus, based on Kalman consensus filtering algorithm and Data-driven filtering technique, we proposed a modified Data-driven Distributed information-weighted Kalman Consensus Filter to estimate the state. Finally, the effectiveness of the designed algorithm is validated by a simulation example.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"177 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123571796","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167075
Lin Gao, Tianhong Pan
Model Predictive Control (MPC) has been widely used in the permanent magnet synchronous motor. However, in the finite control set MPC, only one voltage vector is applied, which leads to high current harmonics and torque fluctuations. Meanwhile, three-vector MPC inevitably increases the switching frequency of inverter. In this article, a multi-vector switching control approach is established. Based on the location information of the created reference voltage vector, the relevant control technique is implemented. The proposed control method with single-vector, two-vector and three-vector composite modes of action is designed to achieve low switching frequency with excellent steady-state performance. The proposed method's effectiveness is confirmed by the experimental results.
{"title":"Composite Multi-Vector Model Predictive Control for Permanent Magnet Synchronous Motor","authors":"Lin Gao, Tianhong Pan","doi":"10.1109/DDCLS58216.2023.10167075","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167075","url":null,"abstract":"Model Predictive Control (MPC) has been widely used in the permanent magnet synchronous motor. However, in the finite control set MPC, only one voltage vector is applied, which leads to high current harmonics and torque fluctuations. Meanwhile, three-vector MPC inevitably increases the switching frequency of inverter. In this article, a multi-vector switching control approach is established. Based on the location information of the created reference voltage vector, the relevant control technique is implemented. The proposed control method with single-vector, two-vector and three-vector composite modes of action is designed to achieve low switching frequency with excellent steady-state performance. The proposed method's effectiveness is confirmed by the experimental results.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817863","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166913
Xiaofeng Yuan, Zhenzhen Jia, Lingjian Ye, Kai Wang, Yalin Wang
The industrial processes are commonly characterized by nonlinearities and dynamics. Therefore, long short-term memory (LSTM) networks are often adopted to extract the nonlinear dynamic features for the prediction of industrial quality indicators. However, traditional LSTM only captures the temporal characteristics of input variables but ignores the output variables. Therefore, a multi-model integrated method (MMIM) is proposed for simultaneously extracting the input and output temporal characteristics in this study. In the MMIM, a LSTM and other static models are used to collect the temporal and static characteristics for the inputs, while a RNN is applied to predict the output variable. The effectiveness and performance are verified on an industrial hydrocracking plant for the prediction of light naphtha isopentane and heavy naphtha quality.
{"title":"Industrial Soft Sensor Prediction based on Multi-model Integrated Method","authors":"Xiaofeng Yuan, Zhenzhen Jia, Lingjian Ye, Kai Wang, Yalin Wang","doi":"10.1109/DDCLS58216.2023.10166913","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166913","url":null,"abstract":"The industrial processes are commonly characterized by nonlinearities and dynamics. Therefore, long short-term memory (LSTM) networks are often adopted to extract the nonlinear dynamic features for the prediction of industrial quality indicators. However, traditional LSTM only captures the temporal characteristics of input variables but ignores the output variables. Therefore, a multi-model integrated method (MMIM) is proposed for simultaneously extracting the input and output temporal characteristics in this study. In the MMIM, a LSTM and other static models are used to collect the temporal and static characteristics for the inputs, while a RNN is applied to predict the output variable. The effectiveness and performance are verified on an industrial hydrocracking plant for the prediction of light naphtha isopentane and heavy naphtha quality.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123421130","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167346
Yan Li, Yang Zhou, Li Jia, Yilin Zhao
Fault detection is of great significance for industrial processes as it ensures the stable operation of systems and the safety of personnel. However, factors such as equipment aging and environmental changes often cause data deviations in industrial data that cannot be accurately detected by ordinary models. The copula function can clearly describe the relationship between random variables and has a simple structure that is suitable for transferring knowledge. Therefore, this paper proposes a transfer learning method based on the C-vine copula. The method first determines the structure and parameters of the C-vine copula based on data from the source domain, and then fine-tunes with a small amount of data from the target domain. Experimental results show that the proposed model has higher detection accuracy and can express the relationship between variables more clearly than machine learning and deep transfer models.
{"title":"Industrial Fault Detection Based on C-Vine Copula Model and Transfer Learning Strategy","authors":"Yan Li, Yang Zhou, Li Jia, Yilin Zhao","doi":"10.1109/DDCLS58216.2023.10167346","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167346","url":null,"abstract":"Fault detection is of great significance for industrial processes as it ensures the stable operation of systems and the safety of personnel. However, factors such as equipment aging and environmental changes often cause data deviations in industrial data that cannot be accurately detected by ordinary models. The copula function can clearly describe the relationship between random variables and has a simple structure that is suitable for transferring knowledge. Therefore, this paper proposes a transfer learning method based on the C-vine copula. The method first determines the structure and parameters of the C-vine copula based on data from the source domain, and then fine-tunes with a small amount of data from the target domain. Experimental results show that the proposed model has higher detection accuracy and can express the relationship between variables more clearly than machine learning and deep transfer models.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127900683","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166696
Wancheng Yue, Junsheng Ren, Weiwei Bai
This paper proposed a method of online non-parameter identification of nonlinear ship motion systems. Firstly, we use Mariner to generate a certain amount of ship motion data to train the LWPR model. Then the ship travels along a set track. During this process, the sensors continuously obtain the distance, radial velocity and azimuth of the ship relative to the ship, and then completes the construction of simulation data. Next, the performance of the algorithm is verified which uses the Kalman filtering framework. Finally, the estimated value is further used for updating the LWPR model to achieve the purpose of online learning, and the updated model will be used for the next prediction. The experimental results show that the online modeling and tracking method proposed in this paper has higher tracking accuracy than the parameter estimation techniques.
{"title":"Online non-parametric modeling for ship maneuvering motion using local weighted projection regression and extended Kalman filter","authors":"Wancheng Yue, Junsheng Ren, Weiwei Bai","doi":"10.1109/DDCLS58216.2023.10166696","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166696","url":null,"abstract":"This paper proposed a method of online non-parameter identification of nonlinear ship motion systems. Firstly, we use Mariner to generate a certain amount of ship motion data to train the LWPR model. Then the ship travels along a set track. During this process, the sensors continuously obtain the distance, radial velocity and azimuth of the ship relative to the ship, and then completes the construction of simulation data. Next, the performance of the algorithm is verified which uses the Kalman filtering framework. Finally, the estimated value is further used for updating the LWPR model to achieve the purpose of online learning, and the updated model will be used for the next prediction. The experimental results show that the online modeling and tracking method proposed in this paper has higher tracking accuracy than the parameter estimation techniques.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505365","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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