论文标题
非线性多代理系统的神经自适应形成控制和目标跟踪与时间延迟
Neuro-Adaptive Formation Control and Target Tracking for Nonlinear Multi-Agent Systems with Time-Delay
论文作者
论文摘要
本文提出了一个基于自适应的神经网络的后台控制器，该控制器使用刚性图理论来解决基于距离的形成控制问题，并针对具有有限的时间段和干扰的非线性多机构系统的目标跟踪。径向基函数神经网络（RBFNN）用于克服并补偿系统动力学中未知的非线性和干扰。通过使用基于特定Lyapunov功能和Young的不等式设计的适当控制信号来缓解代理的状态时间延迟的影响。自适应神经网络（NN）权重调整定律是使用此Lyapunov函数得出的。引入了归一化刚度矩阵的奇异值的上限，并基于Lyapunov稳定性理论严格证明了形成距离误差的均匀最终界限（UUB）。最后，通过非线性多代理系统的仿真结果验证了所提出方法的性能和有效性。提供了基于距离的方法与现有基于位移的方法之间的比较，以评估建议方法的性能。
This paper proposes an adaptive neural network-based backstepping controller that uses rigid graph theory to address the distance-based formation control problem and target tracking for nonlinear multi-agent systems with bounded time-delay and disturbance. The radial basis function neural network (RBFNN) is used to overcome and compensate for the unknown nonlinearity and disturbance in the system dynamics. The effect of the state time-delay of the agents is alleviated by using an appropriate control signal that is designed based on specific Lyapunov function and Young's inequality. The adaptive neural network (NN) weights tuning law is derived using this Lyapunov function. An upper bound for the singular value of the normalized rigidity matrix is introduced, and uniform ultimate boundedness (UUB) of the formation distance error is rigorously proven based on the Lyapunov stability theory. Finally, the performance and effectiveness of the proposed method are validated through the simulation results on nonlinear multi-agent systems. Comparisons between the proposed distance-based method and an existing, displacement-based method are provided to evaluate the performance of the suggested method.
