Intelligent Formation Control of Nonlinear Multi-Agent Systems

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The main goal of this proposal is to establish a long-lasting research program in intelligent and robust formation control of nonlinear multi-agent systems and their swarms. Recent results in formation control of multi-agent systems mostly assume simple agents' models (point mass, single and double integrators), ideal communication links between the agents, and ideal, unconstrained on-board sensors. While those results are important from a theoretical standpoint, their use is limited in practical multi-agent applications.******The proposed research program aims to bridge that gap and to study intelligent control of multi-agents in rigid graph formations. Moreover, multi-agents will include nonlinear models with various nonlinearities, and non-ideal sensor and communication models. The proposed research aims to develop a new framework for rigid graph formation control of mobile agents in 3D space that is based on intelligent control tools such as Recurrent Neural Networks (RNNs), which showed great promise in classical nonlinear control. The developed controllers will have real-time learning capabilities, will be able to accommodate for the systems' nonlinearities and sensors' constraints, and will have adjustable structure to account for rapidly changing formations of agents in the field. Since multi-agents are inherently distributed systems, the proposed control algorithms will be distributed in nature, while aiming to achieve a global objective of the swarm.******The novelty in this research program is threefold: (1) A Neural Network (NN) formation control of nonlinear agents whose movements are constrained with graph rigidity and where both the control algorithms and the NN structure are novel. The NN structure is adaptable in real-time based on number of adjacent agents in the field and the overall network resembles a deep learning NN with many layers from different agents. Adaptation of the intelligent controller will be based on local indices of performance that consider graph rigidity, energy constraints, and agent's nonlinear dynamics. (2) A fault-tolerant multi-agent formations that are robust on individual agent failures and where agents exploit the localized swarm topology in order to monitor and detect faults of individual agents. (3) A new heterogeneous multi-robot test bed that will enable experimentation with multi-agent formation control at various levels including static and mobile sensors, ground-based agents, and micro-aerial vehicles. ******The research program will combine two emerging areas of engineering and computer science, i.e. multi-agent systems and deep learning NNs. As a result, future multi-agent formations will improve robustness to failures of individual agents, will have built-in intelligence to adapt to changing dynamics (of individual agents and the swarm), and will be able to handle various nonlinearities and communication links imperfections.***********