Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046620
Xiaoqing Yuan, Tao Wu, Huan Zou, Xiangbin Ye
As a human-robot coupling system, human-robot interaction plays an important role in the power-assisted exoskeleton robot. Accurately identifying and perceiving the wearer’s motion intention is one of the research difficulties of exoskeleton robots. Aiming at the problem that the wearer’s motion intention is difficult to perceive during human-computer interaction, this paper uses the harmony search algorithm to optimize the one-versus-one support vector machines, and combines with the finite state machine to propose a new intention perception algorithm. The experimental results show that the algorithm can quickly and accurately perceive the wearer’s movement intention, and can identify the abnormal movement state transition in time to ensure the security of the system.
{"title":"Research on Intention Perception Algorithm of Exoskeleton Based on Multi-sensor Information Fusion","authors":"Xiaoqing Yuan, Tao Wu, Huan Zou, Xiangbin Ye","doi":"10.1109/ICARCE55724.2022.10046620","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046620","url":null,"abstract":"As a human-robot coupling system, human-robot interaction plays an important role in the power-assisted exoskeleton robot. Accurately identifying and perceiving the wearer’s motion intention is one of the research difficulties of exoskeleton robots. Aiming at the problem that the wearer’s motion intention is difficult to perceive during human-computer interaction, this paper uses the harmony search algorithm to optimize the one-versus-one support vector machines, and combines with the finite state machine to propose a new intention perception algorithm. The experimental results show that the algorithm can quickly and accurately perceive the wearer’s movement intention, and can identify the abnormal movement state transition in time to ensure the security of the system.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131506664","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046521
F. Liang, Xiaolin Zhao, Shuang Tan, Jiankai Fan, Tingting Yu, Zhe Lin
The surface error of the workpiece, mechanical axis error, etc. will cause the defocus of the marking focal spot in the laser marking process and affect the marking accuracy. An important way to avoid defocusing is to introduce a focusing servo system to detect and compensate defocusing in real time. The critical angle method is selected to detect the focus, and the focusing servo system is established. According to the characteristics of linear and nonlinear coexistence of defocusing error curve, a focusing servo fuzzy controller is designed, which completes the linear control of defocusing error in linear area and the fuzzy control of defocusing error in nonlinear area. The focusing experiment results show that the focusing servo fuzzy control system can realize automatic focusing, and the focusing servo adaptive fuzzy control algorithm enhances the robustness of the control system and expands the dynamic range of focusing.
{"title":"Realization of Fuzzy Control in an Auto Focusing System","authors":"F. Liang, Xiaolin Zhao, Shuang Tan, Jiankai Fan, Tingting Yu, Zhe Lin","doi":"10.1109/ICARCE55724.2022.10046521","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046521","url":null,"abstract":"The surface error of the workpiece, mechanical axis error, etc. will cause the defocus of the marking focal spot in the laser marking process and affect the marking accuracy. An important way to avoid defocusing is to introduce a focusing servo system to detect and compensate defocusing in real time. The critical angle method is selected to detect the focus, and the focusing servo system is established. According to the characteristics of linear and nonlinear coexistence of defocusing error curve, a focusing servo fuzzy controller is designed, which completes the linear control of defocusing error in linear area and the fuzzy control of defocusing error in nonlinear area. The focusing experiment results show that the focusing servo fuzzy control system can realize automatic focusing, and the focusing servo adaptive fuzzy control algorithm enhances the robustness of the control system and expands the dynamic range of focusing.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132357992","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046652
Wenyang Liu, Weiping Hu, Deli Fu
In order to solve the problem of mode mixing and mode aliasing arising from speech decomposition, this paper proposes a speech signal decomposition method based on Variational Mode Decomposition (VMD): Variational Mode Decomposition-Frequency Shifting, VMD-FS). The method takes advantage of the VMD's good extraction of the fundamental frequency of the speech signal, sets specific carrier parameters to shift the frequency of the speech signal to lower frequency, and then applies specific parameters and iterative methods to the VMD to decompose the speech signal in order to obtain the true IMFs that make up the speech signal. Through the decomposition experiments of real speech signals, it is demonstrated that VMD-FS solves the phenomenon of mode mixing and mode aliasing issues arising from the decomposition of speech signals compared with Empirical Mode Decomposition (EMD) and the original VMD method. From the Mean Square Error (MSE) of the decomposition results of the above three methods, it can be proved that VMD-FS outperforms EMD and VMD methods
{"title":"Frequency Shifting-based Variational Mode Decomposition Method for Speech Signal Decomposition","authors":"Wenyang Liu, Weiping Hu, Deli Fu","doi":"10.1109/ICARCE55724.2022.10046652","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046652","url":null,"abstract":"In order to solve the problem of mode mixing and mode aliasing arising from speech decomposition, this paper proposes a speech signal decomposition method based on Variational Mode Decomposition (VMD): Variational Mode Decomposition-Frequency Shifting, VMD-FS). The method takes advantage of the VMD's good extraction of the fundamental frequency of the speech signal, sets specific carrier parameters to shift the frequency of the speech signal to lower frequency, and then applies specific parameters and iterative methods to the VMD to decompose the speech signal in order to obtain the true IMFs that make up the speech signal. Through the decomposition experiments of real speech signals, it is demonstrated that VMD-FS solves the phenomenon of mode mixing and mode aliasing issues arising from the decomposition of speech signals compared with Empirical Mode Decomposition (EMD) and the original VMD method. From the Mean Square Error (MSE) of the decomposition results of the above three methods, it can be proved that VMD-FS outperforms EMD and VMD methods","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122050094","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}
At present, the trend of improving the mobility of fast acquisition, long-term tracking and high-precision control has been formed in China. The continuous observation ability of non-cooperative targets and good imaging performance in motion are important trends in the development of optical remote sensing technology. Aiming at the high-precision observation and real-time tracking requirements of high orbit space satellites, this paper studies the high-precision relative navigation of space non cooperative targets. In order to solve the problem that the estimation accuracy of Unscented Kalman filter (UKF) decreases when the system visibility is low, a modified Unscented Kalman filter (MUKF) based on the visibility is proposed. This algorithm defines a system visibility characterization method based on the error gain matrix of the filtering process, and proposes the visibility scaling parameter based on this method, The filter gain covariance matrix is adjusted online, so that the algorithm can adjust the weight of state prediction and system observation online according to the observability of the current time. Numerical simulation shows that compared with UKF, the estimation accuracy of MUKF is improved by about 4 times, and MUKF stabilizes faster and has higher accuracy.
{"title":"Modified Unscented Kalman Filter for Relative Navition of Space Target Acquisition, Tracking and Control","authors":"Jianbing Kang, Ai Zhang, Zhao-Ru Shi, Yuanming Miao, X. Zhao, Chao Zhang","doi":"10.1109/ICARCE55724.2022.10046586","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046586","url":null,"abstract":"At present, the trend of improving the mobility of fast acquisition, long-term tracking and high-precision control has been formed in China. The continuous observation ability of non-cooperative targets and good imaging performance in motion are important trends in the development of optical remote sensing technology. Aiming at the high-precision observation and real-time tracking requirements of high orbit space satellites, this paper studies the high-precision relative navigation of space non cooperative targets. In order to solve the problem that the estimation accuracy of Unscented Kalman filter (UKF) decreases when the system visibility is low, a modified Unscented Kalman filter (MUKF) based on the visibility is proposed. This algorithm defines a system visibility characterization method based on the error gain matrix of the filtering process, and proposes the visibility scaling parameter based on this method, The filter gain covariance matrix is adjusted online, so that the algorithm can adjust the weight of state prediction and system observation online according to the observability of the current time. Numerical simulation shows that compared with UKF, the estimation accuracy of MUKF is improved by about 4 times, and MUKF stabilizes faster and has higher accuracy.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116936276","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}
Autonomous navigation of unmanned aerial vehicle (UAV) is one of the fundamental yet completely solved problems in automatic control. In this paper, an option-based hierarchical reinforcement learning approach is proposed for UAV autonomous navigation. Specifically, the proposed method consists of a high-level and two low-level model, where the high level behavior selection model learns a stable and reliable behavior selection strategy automatically, while the low-level obstacle avoidance model and target-driven control model implement two behavior strategies, obstacle avoidance and target approach, respectively, thus avoiding the dependence on manually designed control rules. Furthermore, the proposed model is pre-trained on large public dataset, allowing the model to converge quickly in various complex unstructured flight environments. Extensive experiments show that the proposed method indicates an overall advantage in various evaluation metrics, which indicating that the proposed method has a strong generalization capability in autonomous navigation task of UAV.
{"title":"Autonomous Navigation of UAV in Dynamic Unstructured Environments via Hierarchical Reinforcement Learning","authors":"Kai-chang Kou, Gang Yang, Wenqi Zhang, Chenyi Wang, Yuan Yao, Xingshe Zhou","doi":"10.1109/ICARCE55724.2022.10046655","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046655","url":null,"abstract":"Autonomous navigation of unmanned aerial vehicle (UAV) is one of the fundamental yet completely solved problems in automatic control. In this paper, an option-based hierarchical reinforcement learning approach is proposed for UAV autonomous navigation. Specifically, the proposed method consists of a high-level and two low-level model, where the high level behavior selection model learns a stable and reliable behavior selection strategy automatically, while the low-level obstacle avoidance model and target-driven control model implement two behavior strategies, obstacle avoidance and target approach, respectively, thus avoiding the dependence on manually designed control rules. Furthermore, the proposed model is pre-trained on large public dataset, allowing the model to converge quickly in various complex unstructured flight environments. Extensive experiments show that the proposed method indicates an overall advantage in various evaluation metrics, which indicating that the proposed method has a strong generalization capability in autonomous navigation task of UAV.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129183302","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046571
Liwu Tan, Xianzhe Yao, Yuan Long, Zhizheng Zhang, Zhiwei Li, Yan Li
In the safety monitoring of hydrological monitoring stations, the target detection algorithm can be used to avoid unnecessary trouble caused by artificial supervision through intelligent monitoring. However, the accuracy of target recognition and time - delay is always a contradiction in security monitoring. SSD target detection algorithm is the latest target recognition algorithm after Faster RCNN and YOLOv1 algorithm, combining the advantages of both. The algorithm is faster than the fast RCNN algorithm and has higher accuracy than the YOLOv1 algorithm. In this paper, a PSO intelligent algorithm based on hyperlight fast generic face detection and 1mb network convergence is proposed, called PSO-1MB. The device is deployed in or near the hydrologic monitoring station on the edge node server for calculation and processing. In this paper, the test whether the staff wear a hard hat as an example, using the Pytorch environmental framework, experiment simulation. Experimental results show that this model and algorithm can detect helmet features more accurately and quickly, and can better meet the engineering requirements.
{"title":"Design of Image Recognition Monitoring System of Hydrological Monitoring Station Based on Edge Intelligence","authors":"Liwu Tan, Xianzhe Yao, Yuan Long, Zhizheng Zhang, Zhiwei Li, Yan Li","doi":"10.1109/ICARCE55724.2022.10046571","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046571","url":null,"abstract":"In the safety monitoring of hydrological monitoring stations, the target detection algorithm can be used to avoid unnecessary trouble caused by artificial supervision through intelligent monitoring. However, the accuracy of target recognition and time - delay is always a contradiction in security monitoring. SSD target detection algorithm is the latest target recognition algorithm after Faster RCNN and YOLOv1 algorithm, combining the advantages of both. The algorithm is faster than the fast RCNN algorithm and has higher accuracy than the YOLOv1 algorithm. In this paper, a PSO intelligent algorithm based on hyperlight fast generic face detection and 1mb network convergence is proposed, called PSO-1MB. The device is deployed in or near the hydrologic monitoring station on the edge node server for calculation and processing. In this paper, the test whether the staff wear a hard hat as an example, using the Pytorch environmental framework, experiment simulation. Experimental results show that this model and algorithm can detect helmet features more accurately and quickly, and can better meet the engineering requirements.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134111156","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046626
Xisheng Zhu, Dan Zhang, Wenhao Ai, Weidong Fu, D. Zuo
In order to reduce the system performance loss and restore the normal operation for the downtime disturbance event on the monocrystalline SiC substrate production line, a prediction model of the performance of the SiC wafer substrate production line is established based on the G/G/m/b queuing model. A production line simulation model is built in the Witness simulation software to verify the feasibility of the prediction model. A production line performance optimization model under downtime disturbance with the buffer capacity and processing preparation time as optimization parameters is established combining with the concept of time window. And the management strategy is obtained by the optimization solution of the model using genetic algorithm. The feasibility of the proposed method is verified based on the improved coupled map lattices production network propagation model. The results of the simulation validation in Witness software show that the proposed method improves the production performance of the production line under equipment downtime.
{"title":"Management Strategies for Monocrystalline SiC Substrate Production Lines under Downtime Disturbances","authors":"Xisheng Zhu, Dan Zhang, Wenhao Ai, Weidong Fu, D. Zuo","doi":"10.1109/ICARCE55724.2022.10046626","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046626","url":null,"abstract":"In order to reduce the system performance loss and restore the normal operation for the downtime disturbance event on the monocrystalline SiC substrate production line, a prediction model of the performance of the SiC wafer substrate production line is established based on the G/G/m/b queuing model. A production line simulation model is built in the Witness simulation software to verify the feasibility of the prediction model. A production line performance optimization model under downtime disturbance with the buffer capacity and processing preparation time as optimization parameters is established combining with the concept of time window. And the management strategy is obtained by the optimization solution of the model using genetic algorithm. The feasibility of the proposed method is verified based on the improved coupled map lattices production network propagation model. The results of the simulation validation in Witness software show that the proposed method improves the production performance of the production line under equipment downtime.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375914","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046603
Shangce Gao, Lei Zuo, Shitong Bao
Unmanned air vehicle (UAV) reconnaissance task allocation is important in a total military combat system. The typical Genetic Algorithm (GA) is a common effective means to deal with the UAV task allocation problem. But when face with a large number of targets, the initial population has a huge influence on the performance of GA algorithms, which leads to instability on the solution accuracy. To overcome this limitation of heuristics algorithms, we propose a new algorithm combing reinforcement learning (RL) and the GA algorithms, named GA-RL. The RL is used to fast provide an initial population for GA, and then the GA algorithms further optimize this initial population to get the solution. Finally, the numerical simulation tests show that this algorithm can hugely improve the solving accuracy, especially in large tasks allocation problems.
{"title":"UAV Reconnaissance Task Allocation with Reinforcement Learning and Genetic Algorithm","authors":"Shangce Gao, Lei Zuo, Shitong Bao","doi":"10.1109/ICARCE55724.2022.10046603","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046603","url":null,"abstract":"Unmanned air vehicle (UAV) reconnaissance task allocation is important in a total military combat system. The typical Genetic Algorithm (GA) is a common effective means to deal with the UAV task allocation problem. But when face with a large number of targets, the initial population has a huge influence on the performance of GA algorithms, which leads to instability on the solution accuracy. To overcome this limitation of heuristics algorithms, we propose a new algorithm combing reinforcement learning (RL) and the GA algorithms, named GA-RL. The RL is used to fast provide an initial population for GA, and then the GA algorithms further optimize this initial population to get the solution. Finally, the numerical simulation tests show that this algorithm can hugely improve the solving accuracy, especially in large tasks allocation problems.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132615835","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046617
X. Zhao
This paper taking the course of Java programming as an example, an adaptive learning question bank system based on personalized recommendation is realized by using collaborative filtering recommendation algorithm. Personalized test exercises that are suitable for students' own curriculum learning status can help students improve their learning efficiency and effectively promote the improvement and progress of students' academic achievements. At the same time, the application of this system can provide a reference basis for other adaptive learning systems based on personalized recommendation.
{"title":"Research on Application of Personalized Recommendation Technology in Adaptive Learning System Based on Java Programming","authors":"X. Zhao","doi":"10.1109/ICARCE55724.2022.10046617","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046617","url":null,"abstract":"This paper taking the course of Java programming as an example, an adaptive learning question bank system based on personalized recommendation is realized by using collaborative filtering recommendation algorithm. Personalized test exercises that are suitable for students' own curriculum learning status can help students improve their learning efficiency and effectively promote the improvement and progress of students' academic achievements. At the same time, the application of this system can provide a reference basis for other adaptive learning systems based on personalized recommendation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123590540","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046578
Yongliang Zhang, Lingcong Nie, Ting Yu, F. Lu, Jin-Quan Huang
In this paper, a mathematical model of tandem turbine-based combined cycle (TBCC) engine is studied based on the component-level concept, and then the mode transition is focused on with the controller design. The aerodynamic thermodynamic equations are drawn out in the establishment of engine component-level model, and Newton-Raphson method is applied to solve the common operation equations. In addition, the modal transition process is simulated and analyzed, the mode transition operating point of the TBCC engine is determined in the flight trajectory. Thus, the combined engine modal transition control quantity adjustment plan is formulated. Finally, a multi-variable controller based on neural network estimation and inverse control is designed and verified in the TBCC simulation.
{"title":"Design Method of Mode Transition Control Law for TBCC Engine","authors":"Yongliang Zhang, Lingcong Nie, Ting Yu, F. Lu, Jin-Quan Huang","doi":"10.1109/ICARCE55724.2022.10046578","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046578","url":null,"abstract":"In this paper, a mathematical model of tandem turbine-based combined cycle (TBCC) engine is studied based on the component-level concept, and then the mode transition is focused on with the controller design. The aerodynamic thermodynamic equations are drawn out in the establishment of engine component-level model, and Newton-Raphson method is applied to solve the common operation equations. In addition, the modal transition process is simulated and analyzed, the mode transition operating point of the TBCC engine is determined in the flight trajectory. Thus, the combined engine modal transition control quantity adjustment plan is formulated. Finally, a multi-variable controller based on neural network estimation and inverse control is designed and verified in the TBCC simulation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"125 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121189328","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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