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Distributed Formation Control Using Artificial Potentials and Neural Network for Constrained Multiagent Systems
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DOI：10.1109/TCST.2018.2884226.png)
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Abstract
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In this brief, we focus on the study of formation tracking problem for a class of multiagent systems with nonlinear dynamics and external disturbances in the presence of relative distance constraints. A novel distributed formation control strategy is proposed based on an integration of radial basis function neural network (NN) with artificial potential field method. The relative distance constraints between arbitrary adjacent agents can be ensured by the artificial potential function. Based on the NN approximation property, it has been proposed to neutralize the nonlinear dynamics in agents. To account for the negative influence of the approximation error and external disturbances, a robustness term is employed. Finally, based on algebraic graph theory, matrix theory, and Barbalat's lemma, some sufficient conditions are established to accomplish the asymptotical stability of the systems for a given communication graph. The study is with application to tethered space net robot. The simulation results are performed to illustrate the performance of the proposed strategy.
Keywords:
Manganese
Artificial neural networks
Collision avoidance
Multi-agent systems
Aerospace electronics
Robots
Orbits
Artificial potential function
formation tracking control
neural network (NN)
relative distance constraint
tethered space net robot (TSNR)
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