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.

中文翻译：

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具有运动约束的不确定多代理网络中的鲁棒近最优协调

本文解决了具有运动约束的非线性不确定二阶多代理网络的鲁棒协调问题，包括速度饱和和避免碰撞。采用基于单评判神经网络的近似动态规划方法和未知动力学的精确估计来在线学习最优值函数和控制器。通过将避让惩罚纳入跟踪变量，构建新的价值函数，并设计合适的学习算法，同时实现了多智能体协调和避碰。我们表明，开发的基于反馈的协调策略保证了闭环动态稳定性的统一最终有界收敛，并且始终严格遵守所有基础运动约束。