In this paper, the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive distributed optimization algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.

本文研究了一类一般非线性无模型多智能体系统的分布式优化问题。每个代理的动态模型是未知的，只有输入/输出数据可用。采用无模型自适应控制方法，将原始未知非线性系统等价转换为动态线性化模型。开发了事件触发的共识方案来保证所有代理的输出的共识误差是收敛的。然后，利用分布式梯度下降法，提出了一种新颖的事件触发的无模型自适应分布式优化算法。建立足够的条件来确保所讨论的系统的共识和最优性。最后，提供仿真结果来验证所提出方法的有效性。