Collective-Based Multiagent Coordination: A Case Study
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EURASIP Journal on Wireless Communications and Networking
Distributed faulty sensor detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A neuro-evolutionary approach to micro aerial vehicle control
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Probability collectives multi-agent systems: a study of robustness in search
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
A study of probability collectives multi-agent systems on optimization and robustness
Transactions on computational collective intelligence IV
Surveillance of unmanned aerial vehicles using probability collectives
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
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Expert Systems with Applications: An International Journal
Multi-objective probability collectives
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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The increasing complexity of aerospace systems demands new approaches for their design and control. Approaches are required to address the trend towards aerospace systems comprised of a large number of inherently distributed and highly nonlinear components with complex and sometimes competing interactions. This work introduces collectives to address these challenges. Although collectives have been used for distributed optimization problems in computer science, recent developments based upon Probability Collectives (PC) theory enhance their applicability to discrete, continuous, mixed, and constrained optimization problems. Further, they are naturally applied to distributed systems and those involving uncertainty, such as control in the presence of noise and disturbances. This work describes collectives theory and its implementation, including its connections to multi-agent systems, machine learning, statistics, and gradient-based optimization. To demonstrate the approach, two experiments were developed. These experiments built upon recent advances in actuator technology that resulted in small, simple flow control devices. Miniature-Trailing Edge Effectors (MiTE), consisting of a small, 1-5% chord, moveable surface mounted at the wing trailing edge, are used for the experiments. The high bandwidth, distributed placement, and good control authority make these ideal candidates for rigid and flexible mode control of flight vehicles. This is demonstrated in two experiments: flutter suppression of a flexible wing, and flight control of a remotely piloted aircraft. The first experiment successfully increased the flutter speed by over 25%. The second experiment included a novel distributed flight control system based upon the MiTEs that includes distributed sensing, logic, and actuation. Flight tests validated the control capability of the MiTEs and the associated flight control architecture. The collectives approach was used to design controllers for the distributed flight control system. These controllers increased the flight vehicle stability by 85% and alleviated gust loads by 78%, when compared with open loop. This work demonstrates the use of collectives for the range of optimization problems of interest in aerospace systems, provides the mathematical foundations and implementation details, and focuses on applications to nonlinear, robust, distributed control. The two successful experiments validate both the collectives approach and the use of MiTEs for the control of flight vehicles.