Multiple Spacecraft Formation and Station-Keeping Control in Presence of Static Attitude Constraint via Decentralized Virtual Structure Approach

Abstract In this paper, we use a dynamic programming formulation to address a class of multi-agent task assignment problems that arise in the study of fuel optimal control of multiple agents. The fuel optimal multi-agent control is highly relevant to multiple spacecraft formation reconfiguration, an area of intense current research activity. Based on the recurrence relation derived from the celebrated principle of optimality, we develop an algorithm with a distributed computational architecture for the global optimal task assignment. In addition, we propose a communication protocol to facilitate decentralized decision making among agents. Illustrative studies are included to demonstrate the efficacy of the proposed multi-agent optimal task assignment algorithm.

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Loosely Formation-Displaced Geostationary Orbit Optimization with Complex Hybrid Sail Propulsion

Complexity ◽

10.1155/2018/3576187 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Yuan Liu ◽

Yong Hao ◽

Weiwei Liu

Keyword(s):

Propulsion System ◽

Geostationary Orbit ◽

Relative Distance ◽

Station Keeping ◽

Direct Optimization ◽

Distance Constraints ◽

Hybrid Propulsion ◽

Spacecraft Formation ◽

Complex Hybrid ◽

Propellant Consumption

To explore the performance of hybrid sail and overcome the congestion of geostationary orbit, this work proposes a method intended to optimize the trajectories of the spacecraft formation and extend the concept of displaced geostationary orbit by loosening the relative distance and introducing a station-keeping box. The multispacecraft formation is a typical complex system with nonlinear dynamics, and the hybrid propulsion system introduces additional complexity. To solve this problem, suboptimal trajectories with constant relative distance constraints are first found with inverse methods, which were referred to as ideal displaced geostationary orbits. Then, the suboptimal trajectories are used as a first guess for a direct optimization algorithm based on Gauss pseudospectral algorithm, which loosens the relative distance constraints and allows the spacecraft to be placed anywhere inside the station-keeping box. The optimization results show that the loosely formation and station-keeping box can create more flexible trajectories and achieve higher efficiency of the hybrid sail propulsion system, which can save about 40% propellant consumption.

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Unscented kalman filter for multiple spacecraft formation flying

Proceedings of the 2003 American Control Conference, 2003. ◽

10.1109/acc.2003.1239848 ◽

2004 ◽

Cited By ~ 1

Author(s):

Lingji Chen ◽

S. Seereeram ◽

R.K. Mehra

Keyword(s):

Kalman Filter ◽

Formation Flying ◽

Unscented Kalman Filter ◽

Spacecraft Formation ◽

Spacecraft Formation Flying ◽

Multiple Spacecraft

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Relative Navigation Based on UKF for Multiple Spacecraft Formation Flying

AIAA Guidance, Navigation, and Control Conference and Exhibit ◽

10.2514/6.2004-5137 ◽

2004 ◽

Cited By ~ 3

Author(s):

Dong Sun ◽

F. Zhou ◽

Z. Jun

Keyword(s):

Formation Flying ◽

Relative Navigation ◽

Spacecraft Formation ◽

Spacecraft Formation Flying ◽

Multiple Spacecraft

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A Memory/Immunology-Based Control Approach with Applications to Multiple Spacecraft Formation Flying

Mathematical Problems in Engineering ◽

10.1155/2013/456410 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Liguo Weng ◽

Min Xia ◽

Kai Hu ◽

Zhuhan Qiao

Keyword(s):

Formation Control ◽

Outer Space ◽

Human Memory ◽

Dynamic Information ◽

Control Approach ◽

Spacecraft Formation ◽

Spacecraft Formation Flying ◽

Gravity Forces ◽

Multiple Spacecraft ◽

Current Behavior

This paper addresses the problem of formation control for multiple spacecrafts in Planetary Orbital Environment (POE). Due to the presence of diverse interferences and uncertainties in the outer space, such as the changing spacecraft mass, unavailable space parameters, and varying gravity forces, traditional control methods encounter great difficulties in this area. A new control approach inspired by human memory and immune system is proposed, and this approach is shown to be capable of learning from past control experience and current behavior to improve its performance. It demands much less system dynamic information as compared with traditional controls. Both theoretic analysis and computer simulation verify its effectiveness.

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