Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming

Morgan, Daniel; Chung, Soon‐Jo; Hadaegh, Fred Y.

doi:10.2514/1.g000218

Cited by 300 publications

(216 citation statements)

References 42 publications

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“…But the process can be computationally expensive and stability guarantees are challenging. Alternately, the control design can be separated from optimal trajectory design by treating the optimized state trajectory for each robot, obtained from (7), as a desired trajectory for the tracking controller [25], [28], [87], [93], [94]. This approach has the benefit of setting up the control design problem in the traditional input-tracking or model reference setting with guaranteed closed-loop stability.…”

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

“…The third constraint ensures that the optimal trajectories begin at the actual initial states while ensuring safety and other state-dependent constraints. Since the cost function is optimized in real-time over a finite-time horizon, often times recomputed using the current states of the robots as the initial conditions, (7) can be viewed as model predictive control (MPC) [25], [84]- [87]. Another approach to multi-agent planning under uncertainty over a discretized state domain is to employ a decentralized partially observable Markov decision process (POMDP) [88], [89].…”

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

“…One must ensure that the resulting paths are kinematically or dynamically feasible for the robots to follow [91], [92]. Direct optimal control approaches [25], [85]- [87] cast the dynamics into equality constraints between the states in successive time steps for optimization (e.g., iterative linearization of dynamics in sequential convex programming [25], [87]). Alternatively, one can find a geometric path for each robot to reach its goal and then use these paths as inputs to a trajectory optimization step to make the paths dynamically feasible [92].…”

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

“…This decentralized formulation is sub-optimal compared to the centralized solution, but it is still complete. For example, simultaneous optimal assignment and trajectory planning computes an optimal terminal point constraint of (7) for shape reconfiguration control [25], [87].…”

Section: B Simultaneous Planning With Distributed Assignmentmentioning

confidence: 99%

“…The most intuitive techniques for avoiding collisions are speed adjustment [118] and sequentially replanning the trajectories [87] without changing the assignment in an optimal control framework (7). In particular, mixedinteger linear programming (MILP) has been successfully derived for optimal collision-free motions and applied to mobile robots, spacecraft, and UAVs [85], [86], [119].…”

Section: Collision Avoidance and Collision-free Motionsmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

Section: B Simultaneous Planning With Distributed Assignmentmentioning

confidence: 99%

Section: Collision Avoidance and Collision-free Motionsmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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State‐dependent indirect hp‐pseudospectral method for rigid spacecraft attitude optimal control problem

Liu,

2024

Asian Journal of Control

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The state‐dependent indirect ‐pseudospectral (SDIHP) method for rigid spacecraft attitude control is presented in this paper. The optimal controller is obtained by the SDIHP method through classifying the attitude optimal control problem as a nonlinear quadratic optimal control problem. In the proposed control scheme, successive state‐dependent coefficient (SDC) parameterization is initially applied to the spacecraft dynamics to derive an available linear system to decrease the calculation cost and speed. Then, the indirect ‐adaptive spectral discretization is developed to convert the resultant optimal control problem into a sequence of linear equations, which can improve the global computational efficiency and guarantee the solving precision. Hence, the optimal control can be efficiently solved using basic matrix operations under the necessary collocation points based on the ‐adaptive features. Considering the inertia uncertainties and external disturbances, a state feedback controller is generated by the receding horizon control (RHC) framework to improve the robustness. Simulation results are included to show the effectiveness and performance of the suggested optimal method.

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An introductory review of swarm technology for spacecraft on‐orbit servicing

Asri,

Zhu

2024

Int Journal of Mech Sys Dyn

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This review paper presents a comprehensive evaluation and forward‐looking perspective on the underexplored topic of servicing target objects using spacecraft swarms. Such targets can be known or unknown, cooperative or uncooperative, and pose significant challenges in modern space operations due to their inherent complexity and unpredictability. Successfully servicing space objects is vital for active debris removal and broader on‐orbit servicing tasks such as satellite maintenance, repair, refueling, orbital assembly, and construction. Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft. Given its advantages and benefits, this paper expands the discussion to encompass a swarm approach to the problem. This review covers various single‐spacecraft approaches and presents a critical examination of the existing, although limited, body of work dedicated to servicing orbital objects using multiple spacecraft. The focus is also broadened to include some influential studies concerning the characterization, capture, and manipulation of physical objects by general multiagent systems, a subject with significant parallels to the core interest of this manuscript. Furthermore, this article also delves into the realm of simultaneous localization and mapping, highlighting its application within close‐proximity operations in space, especially when dealing with unknown uncooperative targets. Special attention is paid to the benefits that this field can receive from distributed multiagent architectures. Finally, an exploration of the promising field of swarm robotics is presented, with an emphasis on its potential to revolutionize the servicing of orbital target objects. Concurrently, a survey of general research directly engaging swarms in the orbital context is conducted. This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.

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Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming

Cited by 300 publications

References 42 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

State‐dependent indirect hp‐pseudospectral method for rigid spacecraft attitude optimal control problem

An introductory review of swarm technology for spacecraft on‐orbit servicing

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