Neuro-Adaptive Distributed Control With Prescribed Performance for the Synchronization of Unknown Nonlinear Networked Systems
提出一种神经自适应分布式协同跟踪控制方法，通过预设性能函数将约束误差转化为无约束问题，使完全未知动力学的多智能体系统在强连通网络下跟踪领导者轨迹，并保证误差和神经网络权值的一致最终有界。
This paper proposes a neuro-adaptive distributive cooperative tracking control with prescribed performance function (PPF) for highly nonlinear multiagent systems. PPF allows error tracking from a predefined large set to be trapped into a predefined small set. The key idea is to transform the constrained system into unconstrained one through transformation of the output error. Agents' dynamics are assumed to be completely unknown, and the controller is developed for strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness of the transformed error and the adaptive neural network weights. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous networked system with time varying uncertain parameters and external disturbances.