A Framework for Autonomous Cooperative Optimal Assignment and Control of Multi-Agent Systems
Abstract
Space has always been a domain requiring a high degree of autonomy. The challenges presented by the required autonomy have made it difficult to accomplish complex tasks and operations with short response windows. With the growing use of multiagent systems to enhance old and demonstrate new capabilities in the aerial domain, the need for development in multi-agent operations on orbit and in proximity operations has never been greater. A decentralized, cooperative multi-agent optimal control framework is presented to offer a solution to the assignment and control problems associated with performing multi-agent tasks in a proximity operations environment. However, the framework developed may be applied to a variety domains such as air, space, and sea. The solution presented takes advantage of a second price auction assignment algorithm to optimally task each satellite, while model predictive control is implemented to control the agents optimally while adhering to safety and mission constraints. The solution is compared to a direct orthogonal collocation method, and a study on tuning parameters is included. Results demonstrate the proposed technique allows the user to optimize control beyond phase horizons with Model Predictive Control and achieve a formation rendezvous with three tuning parameters. This better approximates phase transition in collocation techniques compared to traditional multi-phase MPC.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 25, 2021
- Accession Number
- AD1136280
Entities
People
- Devin E. Saunders
Organizations
- Air Force Institute of Technology