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• W4226049115 abstract "This article addresses the robust coordination problem for nonlinear uncertain second-order multiagent networks with motion constraints, including velocity saturation and collision avoidance. A single-critic neural network-based approximate dynamic programming approach and exact estimation of unknown dynamics are employed to learn online the optimal value function and controller. By incorporating avoidance penalties into tracking variable, constructing a novel value function, and designing of suitable learning algorithms, multiagent coordination and collision avoidance are achieved simultaneously. We show that the developed feedback-based coordination strategy guarantees uniformly ultimately bounded convergence of the closed-loop dynamical stability and all underlying motion constraints are always strictly obeyed. The effectiveness of the proposed collision-free coordination law is finally illustrated using numerical simulations." @default.
• W4226049115 created "2022-05-05" @default.
• W4226049115 creator A5045067652 @default.
• W4226049115 creator A5045364347 @default.
• W4226049115 date "2023-05-01" @default.
• W4226049115 modified "2023-09-26" @default.
• W4226049115 title "Robust Near-Optimal Coordination in Uncertain Multiagent Networks With Motion Constraints" @default.
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• W4226049115 doi "https://doi.org/10.1109/tcyb.2021.3125318" @default.
• W4226049115 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34793315" @default.
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