论文标题
基于神经网络的有限最佳协调，用于异质性不确定的非线性多机构系统
Neural Network-based Constrained Optimal Coordination for Heterogeneous Uncertain Nonlinear Multi-agent Systems
论文作者
论文摘要
在本文中，我们研究了一类由未知非线性和外部干扰的高阶动力学描述的一类非均质非线性多机构系统的最佳最佳协调问题。每个代理都有一个私人目标函数和对其输出的稳态约束。我们通过内部模型和神经网络的组合为每个代理开发一个复合分布式控制器。事实证明，所有代理输出都可以达到汇总目标函数的约束最小点，而有限的残余误差无关未知的非线性和外部干扰。最终给出了两个例子以证明算法的有效性。
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.
