In this paper, a cooperative adaptive control of leader-following uncertain nonlinear multiagent systems is proposed. The communication network is weighted undirected graph with fixed topology. The uncertain nonlinear model for each agent is a higher-order integrator with unknown nonlinear functions, unknown disturbances and unknown input actuators. Meanwhile, the gains of input actuators are unknown nonlinear functions with unknown sign. Two most common behaviors of input actuators in practical applications are hysteresis and dead-zone. In this paper, backlash-like hysteresis and dead-zone are used to model the input actuators. Using universal approximation theorem proved for neural networks, the unknown nonlinear functions are tackled. The unknown weights of neural networks are derived by proposing appropriate adaptive laws. To cope with modeling errors and disturbances an adaptive robust structure is proposed. Considering Lyapunov synthesis approach not only all the adaptive laws are derived but also it is proved that the closed-loop network is cooperatively semi-globally uniformly ultimately bounded (CSUUB). In order to investigate the effectiveness of the proposed method, it is applied to agents modeled with highly nonlinear mathematical equations and inverted pendulums. Simulation results demonstrate the effectiveness and applicability of the proposed method in dealing with both numerical and practical multi-agent systems.

中文翻译：

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不确定的具有滞后和死区的多代理系统之后的领导者协同神经自适应控制

本文提出了一种不确定变量非线性多主体系统的跟随跟随协同自适应控制方法。通信网络是具有固定拓扑的加权无向图。每个代理的不确定非线性模型是具有未知非线性函数，未知干扰和未知输入执行器的高阶积分器。同时，输入执行器的增益是符号未知的非线性函数。实际应用中，输入执行器的两种最常见的行为是磁滞和死区。在本文中，使用类似反冲的磁滞和死区来模拟输入执行器。使用针对神经网络证明的通用逼近定理，可以解决未知的非线性函数。神经网络的未知权重是通过提出适当的自适应定律得出的。为了应对建模误差和干扰，提出了一种自适应鲁棒结构。考虑到李雅普诺夫综合方法，不仅推导了所有自适应律，而且证明了闭环网络是合作半全局一致最终有界的（CSUUB）。为了研究该方法的有效性，将其应用于具有高度非线性数学方程和倒立摆的智能体。仿真结果证明了该方法在数值和实际多智能体系统中的有效性和适用性。考虑到李雅普诺夫综合方法，不仅推导了所有自适应律，而且证明了闭环网络是合作半全局一致最终有界的（CSUUB）。为了研究该方法的有效性，将其应用于具有高度非线性数学方程和倒立摆的智能体。仿真结果证明了该方法在数值和实际多智能体系统中的有效性和适用性。考虑到李雅普诺夫综合方法，不仅推导了所有自适应律，而且证明了闭环网络是合作半全局一致最终有界的（CSUUB）。为了研究该方法的有效性，将其应用于具有高度非线性数学方程和倒立摆的智能体。仿真结果证明了该方法在数值和实际多智能体系统中的有效性和适用性。