基于神经网络的异构不确定非线性多智能体系统约束最优协调
Neural Network-based Constrained Optimal Coordination for Heterogeneous Uncertain Nonlinear Multi-agent Systems
摘要：
本文研究了一类由高阶动力学描述的非线性多智能体系统的约束最优协调问题。每个智能体都有一个私有的目标函数和一个关于其输出的稳态约束。通过内模和神经网络的结合，我们为每个智能体开发了一个复合分布式控制器。所有的代理输出被证明达到约束最小点的聚合目标函数有界的残差，而不考虑未知的非线性和外部干扰。最后给出了两个算例，验证了算法的有效性.
英文摘要：
In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a steady-state constraint about its output. We develop a composite distributed controller for each agent by a combination of internal model and neural network. All agent outputs are proven to reach the constrained minimal point of the aggregate objective function with bounded residual errors irrespective of the unknown nonlinearities and external disturbances. Two examples are finally given to demonstrate the effectiveness of the algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
