Pub Date : 2021-09-01DOI: 10.1142/s273748072150014x
Yanhong Luo, A. Bai, Huaguang Zhang
In this paper, a novel formation control strategy is proposed to address the target tracking and circumnavigating problem of multi-UAV formation. First, two sets of definitions, space angle definition and space vector definition, are presented in order to describe the flight state and construct the desired relative velocity. Then, the relative kinematic model between the UAV and the moving target is established. The distributed control law is constructed by using dynamic feedback linearization so as to realize the tracking and circumnavigating control with the desired velocity, circling radius and relative angular spacing. Next, the exponential stability of the closed-loop system is further guaranteed by properly choosing some corresponding parameters based on the Lyapunov method. Finally, the numerical simulation is carried out to verify the effectiveness of the proposed control method.
{"title":"Distributed Formation Control of UAVs for Circumnavigating a Moving Target in Three-Dimensional Space","authors":"Yanhong Luo, A. Bai, Huaguang Zhang","doi":"10.1142/s273748072150014x","DOIUrl":"https://doi.org/10.1142/s273748072150014x","url":null,"abstract":"In this paper, a novel formation control strategy is proposed to address the target tracking and circumnavigating problem of multi-UAV formation. First, two sets of definitions, space angle definition and space vector definition, are presented in order to describe the flight state and construct the desired relative velocity. Then, the relative kinematic model between the UAV and the moving target is established. The distributed control law is constructed by using dynamic feedback linearization so as to realize the tracking and circumnavigating control with the desired velocity, circling radius and relative angular spacing. Next, the exponential stability of the closed-loop system is further guaranteed by properly choosing some corresponding parameters based on the Lyapunov method. Finally, the numerical simulation is carried out to verify the effectiveness of the proposed control method.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77051439","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 : 2021-07-24DOI: 10.1142/S2737480721500114
Wei Dong, Jianan Wang, Chunyan Wang, Zhenqiang Qi, Z. Ding
In this paper, the optimal consensus control problem is investigated for heterogeneous linear multi-agent systems (MASs) with spanning tree condition based on game theory and reinforcement learning. First, the graphical minimax game algebraic Riccati equation (ARE) is derived by converting the consensus problem into a zero-sum game problem between each agent and its neighbors. The asymptotic stability and minimax validation of the closed-loop systems are proved theoretically. Then, a data-driven off-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without the information of the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation.
{"title":"Graphical Minimax Game and Off-Policy Reinforcement Learning for Heterogeneous MASs with Spanning Tree Condition","authors":"Wei Dong, Jianan Wang, Chunyan Wang, Zhenqiang Qi, Z. Ding","doi":"10.1142/S2737480721500114","DOIUrl":"https://doi.org/10.1142/S2737480721500114","url":null,"abstract":"In this paper, the optimal consensus control problem is investigated for heterogeneous linear multi-agent systems (MASs) with spanning tree condition based on game theory and reinforcement learning. First, the graphical minimax game algebraic Riccati equation (ARE) is derived by converting the consensus problem into a zero-sum game problem between each agent and its neighbors. The asymptotic stability and minimax validation of the closed-loop systems are proved theoretically. Then, a data-driven off-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without the information of the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89907989","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 : 2021-07-16DOI: 10.1142/s2737480721500096
K. Peng
In this paper, a nonlinear flight control law is designed for a hybrid unmanned aerial vehicle (UAV) to achieve the advanced flight performances with the autonomous mission management (AMM). The hybrid UAV is capable of hovering like quadrotors and maneuvering as fixed-wing aircraft. The main idea is to design the flight control laws in modules. Those modules are organized online by the autonomous mission management. Such online organization will improve the UAV autonomy. One of the challenges is to execute the transition flight between the rotary-wing and fixed-wing modes. The resulting closed-loop system with the designed flight control law is verified in simulation and the simulation results demonstrate that the resulting closed-loop system can successfully complete the designated flight missions including the transition flight between the rotary-wing and fixed-wing modes.
{"title":"Autonomous Mission Management Based Nonlinear Flight Control Design for a Class of Hybrid Unmanned Aerial Vehicles","authors":"K. Peng","doi":"10.1142/s2737480721500096","DOIUrl":"https://doi.org/10.1142/s2737480721500096","url":null,"abstract":"In this paper, a nonlinear flight control law is designed for a hybrid unmanned aerial vehicle (UAV) to achieve the advanced flight performances with the autonomous mission management (AMM). The hybrid UAV is capable of hovering like quadrotors and maneuvering as fixed-wing aircraft. The main idea is to design the flight control laws in modules. Those modules are organized online by the autonomous mission management. Such online organization will improve the UAV autonomy. One of the challenges is to execute the transition flight between the rotary-wing and fixed-wing modes. The resulting closed-loop system with the designed flight control law is verified in simulation and the simulation results demonstrate that the resulting closed-loop system can successfully complete the designated flight missions including the transition flight between the rotary-wing and fixed-wing modes.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75182645","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 : 2021-07-16DOI: 10.1142/s2737480721500084
Rafael Rodrigues da Silva, Vince Kurtz, Hai Lin
In safety-critical systems, it is desirable to automatically synthesize controllers for complex tasks with guaranteed safety and correctness. Although much progress has been made through controller synthesis from temporal logic specifications, existing approaches generally require conservative assumptions and do not scale well with system dimensionality. We propose a scalable, provably complete algorithm that synthesizes continuous trajectories for hybrid systems to satisfy temporal logic specifications. Specifically, we harness highly efficient Boolean satisfiability (SAT) and Linear Programming (LP) solvers to find trajectories that satisfy non-convex Signal Temporal Logic (STL) specifications for a class of high dimensional hybrid systems. The proposed design algorithms are proven sound and complete, and are validated in simulation experiments.
{"title":"Symbolic Control of Hybrid Systems from Signal Temporal Logic Specifications","authors":"Rafael Rodrigues da Silva, Vince Kurtz, Hai Lin","doi":"10.1142/s2737480721500084","DOIUrl":"https://doi.org/10.1142/s2737480721500084","url":null,"abstract":"In safety-critical systems, it is desirable to automatically synthesize controllers for complex tasks with guaranteed safety and correctness. Although much progress has been made through controller synthesis from temporal logic specifications, existing approaches generally require conservative assumptions and do not scale well with system dimensionality. We propose a scalable, provably complete algorithm that synthesizes continuous trajectories for hybrid systems to satisfy temporal logic specifications. Specifically, we harness highly efficient Boolean satisfiability (SAT) and Linear Programming (LP) solvers to find trajectories that satisfy non-convex Signal Temporal Logic (STL) specifications for a class of high dimensional hybrid systems. The proposed design algorithms are proven sound and complete, and are validated in simulation experiments.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84582881","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 : 2021-07-16DOI: 10.1142/s2737480721500102
Lei Liu, Qianwei He, Bo Wang, Wenzhe Fu, Zhongtao Cheng, Wang Yongji
This paper proposes an improved Generalized Quasi-Spectral Model Predictive Static Programming (GS-MPSP) algorithm for the ascent trajectory optimization for hypersonic vehicles in a complex flight environment. The proposed method guarantees the satisfaction of constraints related to the state and control vector while retaining its high computational efficiency. The spectral representation technique is used to describe the control variables, which reduces the number of decision variables and makes the control input smooth enough. Through Taylor expansion, the constraints are transformed into an inequality containing only decision variables, such that it can be added into GS-MPSP framework. By Gauss quadrature collocation method, only a few collocation points are needed to solve the sensitivity matrix, which greatly accelerates the calculation. Subsequently, the analytical expression is obtained by combining the static optimization with the penalty function method. Finally, the simulation results demonstrate that the proposed improved GS-MPSP algorithm can achieve both high computational efficiency and high terminal precision under the constraints.
{"title":"Ascent Trajectory Optimization for Air-Breathing Hypersonic Vehicles Based on IGS-MPSP","authors":"Lei Liu, Qianwei He, Bo Wang, Wenzhe Fu, Zhongtao Cheng, Wang Yongji","doi":"10.1142/s2737480721500102","DOIUrl":"https://doi.org/10.1142/s2737480721500102","url":null,"abstract":"This paper proposes an improved Generalized Quasi-Spectral Model Predictive Static Programming (GS-MPSP) algorithm for the ascent trajectory optimization for hypersonic vehicles in a complex flight environment. The proposed method guarantees the satisfaction of constraints related to the state and control vector while retaining its high computational efficiency. The spectral representation technique is used to describe the control variables, which reduces the number of decision variables and makes the control input smooth enough. Through Taylor expansion, the constraints are transformed into an inequality containing only decision variables, such that it can be added into GS-MPSP framework. By Gauss quadrature collocation method, only a few collocation points are needed to solve the sensitivity matrix, which greatly accelerates the calculation. Subsequently, the analytical expression is obtained by combining the static optimization with the penalty function method. Finally, the simulation results demonstrate that the proposed improved GS-MPSP algorithm can achieve both high computational efficiency and high terminal precision under the constraints.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72669419","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 : 2021-07-16DOI: 10.1142/s2737480721500060
Yu Sheng, G. Tao
This paper presents an adaptive controller design framework with input compensation for quadrotor systems, which deals with different system operating conditions with a uniform update law for the controller parameters. The motivation of the work is to handle the situation that existing adaptive control schemes are either restricted to the system equilibrium as the hover condition or unable to deal with the diverse system uncertainties which cause system interactor matrix and high-frequency gain matrix to change. An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems. To deal with the uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the gain parameters and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. It is ensured that all closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system parameter uncertainties and the uncertain offsets at non-equilibrium operating conditions. The proposed scheme expands the capacity of adaptive control for quadrotors to operate at multiple operating conditions in the presence of system uncertainties. Simulation results of a quadrotor with the proposed adaptive control scheme are presented to show the desired system performance.
{"title":"System Characterization and Adaptive Tracking Control of Quadrotors under Multiple Operating Conditions","authors":"Yu Sheng, G. Tao","doi":"10.1142/s2737480721500060","DOIUrl":"https://doi.org/10.1142/s2737480721500060","url":null,"abstract":"This paper presents an adaptive controller design framework with input compensation for quadrotor systems, which deals with different system operating conditions with a uniform update law for the controller parameters. The motivation of the work is to handle the situation that existing adaptive control schemes are either restricted to the system equilibrium as the hover condition or unable to deal with the diverse system uncertainties which cause system interactor matrix and high-frequency gain matrix to change. An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems. To deal with the uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the gain parameters and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. It is ensured that all closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system parameter uncertainties and the uncertain offsets at non-equilibrium operating conditions. The proposed scheme expands the capacity of adaptive control for quadrotors to operate at multiple operating conditions in the presence of system uncertainties. Simulation results of a quadrotor with the proposed adaptive control scheme are presented to show the desired system performance.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88456840","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 : 2021-06-01DOI: 10.1142/s2737480721500072
Xinyi Wang, Lele Xi, Yizhou Chen, Shupeng Lai, F. Lin, Ben M. Chen
Challenges in motion planning for multiple quadrotors in complex environments lie in overall flight efficiency and the avoidance of obstacles, deadlock, and collisions among themselves. In this paper, we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption. A model predictive control (MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning. First, the motion primitives of each quadrotor are formulated as the boundary state constrained primitives (BSCPs) which are constructed with jerk limited trajectory (JLT) generation method, a boundary value problem (BVP) solver, to obtain time-optimal trajectories. They are then approximated with a neural network (NN), pre-trained using this solver to reduce the computational burden. The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee flight safety without deadlock. Finally, the reference trajectories are generated using the same BVP solver. Our simulation and experimental results demonstrate the superior performance of the proposed method.
{"title":"Decentralized MPC-Based Trajectory Generation for Multiple Quadrotors in Cluttered Environments","authors":"Xinyi Wang, Lele Xi, Yizhou Chen, Shupeng Lai, F. Lin, Ben M. Chen","doi":"10.1142/s2737480721500072","DOIUrl":"https://doi.org/10.1142/s2737480721500072","url":null,"abstract":"Challenges in motion planning for multiple quadrotors in complex environments lie in overall flight efficiency and the avoidance of obstacles, deadlock, and collisions among themselves. In this paper, we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption. A model predictive control (MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning. First, the motion primitives of each quadrotor are formulated as the boundary state constrained primitives (BSCPs) which are constructed with jerk limited trajectory (JLT) generation method, a boundary value problem (BVP) solver, to obtain time-optimal trajectories. They are then approximated with a neural network (NN), pre-trained using this solver to reduce the computational burden. The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee flight safety without deadlock. Finally, the reference trajectories are generated using the same BVP solver. Our simulation and experimental results demonstrate the superior performance of the proposed method.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78433375","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}
In this paper, a new learning algorithm named geometric learning algorithm is proposed to solve the UAV’s track planning problem. Actually, based on the environment modeling, the optimal path planning problem is to find an optimal route. The Geometric learning algorithm is essentially an reinforcement learning algorithm. It can not only fully use the distance information to calculate the track based on the geometric distance information but can also fuse dangerous information in a complex environment, which solves the problem of track planning from a practical and theoretical point of view. Based on the two-dimensional successful planning of a single drone, the algorithm is extended to the path planning and decision making of single drone three-dimensional planning. And from a practical and theoretical point of view, the path planning problem has been well solved.
{"title":"A Path Planning for One UAV Based on Geometric Algorithm","authors":"Haochen Li, Sentang Wu, Pengzhi Xie, Zekui Qin, Baochang Zhang","doi":"10.1109/GNCC42960.2018.9019122","DOIUrl":"https://doi.org/10.1109/GNCC42960.2018.9019122","url":null,"abstract":"In this paper, a new learning algorithm named geometric learning algorithm is proposed to solve the UAV’s track planning problem. Actually, based on the environment modeling, the optimal path planning problem is to find an optimal route. The Geometric learning algorithm is essentially an reinforcement learning algorithm. It can not only fully use the distance information to calculate the track based on the geometric distance information but can also fuse dangerous information in a complex environment, which solves the problem of track planning from a practical and theoretical point of view. Based on the two-dimensional successful planning of a single drone, the algorithm is extended to the path planning and decision making of single drone three-dimensional planning. And from a practical and theoretical point of view, the path planning problem has been well solved.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"6 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72974915","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 : 2018-08-01DOI: 10.1109/GNCC42960.2018.9018757
Shanshan Cheng, Dawei Li, N. Li, Honglun Wang, Jianfa Wu, Zikang Su, Menghua Zhang
Aiming at the 3D standoff target tracking problem with six degree-of-freedom (6-DOF) unmanned aerial vehicle (UAV) nonlinear dynamic model in windy environment, this paper designs an integrated tracking guidance and control system based on the 3D Lyapunov Guidance Vector Field (LGVF) and the linear active disturbance rejection control (LADRC). The designed system is composed of the angular and angle loops based on LADRC, the flight path loop combined with the inverse dynamic resolving approach and LADRC, and the LGVF guidance law. It enables UAV to track the maneuvering target in the standoff manner and converge to the desired trajectory. Simulations verify the disturbance rejection ability and the tracking ability of the system for tracking the maneuvering target.
{"title":"Integrated Guidance and Control for UAV Standoff Target Tracking","authors":"Shanshan Cheng, Dawei Li, N. Li, Honglun Wang, Jianfa Wu, Zikang Su, Menghua Zhang","doi":"10.1109/GNCC42960.2018.9018757","DOIUrl":"https://doi.org/10.1109/GNCC42960.2018.9018757","url":null,"abstract":"Aiming at the 3D standoff target tracking problem with six degree-of-freedom (6-DOF) unmanned aerial vehicle (UAV) nonlinear dynamic model in windy environment, this paper designs an integrated tracking guidance and control system based on the 3D Lyapunov Guidance Vector Field (LGVF) and the linear active disturbance rejection control (LADRC). The designed system is composed of the angular and angle loops based on LADRC, the flight path loop combined with the inverse dynamic resolving approach and LADRC, and the LGVF guidance law. It enables UAV to track the maneuvering target in the standoff manner and converge to the desired trajectory. Simulations verify the disturbance rejection ability and the tracking ability of the system for tracking the maneuvering target.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"355 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72998364","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 : 2018-08-01DOI: 10.1109/GNCC42960.2018.9019014
Yuanxu Wang, J. Xiong, Yang Zhang, Y. Zhang
With the continuous development of spaceflight industry in recent years, the number of on-orbit satellites is rapidly increasing, the scale of the Satellite Control Center (SCC) is becoming larger and larger, and the complexity and difficulty of operating and maintenance of which is becoming more and more. Therefor the improvement of the reliability, recovery and the efficiency of resource utilization has become an important issue faced by the development of SCC system. As a resource management technology of computer system, cloud technology has a great advantage in improving resource utilization, reducing management costs and enhancing security, etc. Meanwhile these solutions of cloud technology service mode provide a good technical approach and ideas for new requirements of TT&C (track, telemetry and control). This paper summarizes the current application status of cloud technology in SCC system, puts forward the development ideas, and qualitative and quantitative analyzes the key technical indicators of the application of cloud technology in SCC system, which has certain reference significance for the subsequent development of SCC system.
{"title":"The Development Ideas and Experimental Verification on Cloud Technology of Satellite Control Center System","authors":"Yuanxu Wang, J. Xiong, Yang Zhang, Y. Zhang","doi":"10.1109/GNCC42960.2018.9019014","DOIUrl":"https://doi.org/10.1109/GNCC42960.2018.9019014","url":null,"abstract":"With the continuous development of spaceflight industry in recent years, the number of on-orbit satellites is rapidly increasing, the scale of the Satellite Control Center (SCC) is becoming larger and larger, and the complexity and difficulty of operating and maintenance of which is becoming more and more. Therefor the improvement of the reliability, recovery and the efficiency of resource utilization has become an important issue faced by the development of SCC system. As a resource management technology of computer system, cloud technology has a great advantage in improving resource utilization, reducing management costs and enhancing security, etc. Meanwhile these solutions of cloud technology service mode provide a good technical approach and ideas for new requirements of TT&C (track, telemetry and control). This paper summarizes the current application status of cloud technology in SCC system, puts forward the development ideas, and qualitative and quantitative analyzes the key technical indicators of the application of cloud technology in SCC system, which has certain reference significance for the subsequent development of SCC system.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75364911","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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