In practical engineering, the communication networks of industrial systems are complex, and system models are generally unavailable. To overcome the requirement of mathematical models, several artificial-intelligence-based algorithms in multi-access edge computing are introduced for the performance optimization control of a benchmark microgrid in this paper. First, a neural-network-based identification scheme is proposed to combine with the online adaptive dynamic programming learning method, which avoids the requirement of system models. However, the identification errors are not taken into consideration. Next, to realize the model-free purpose without using the identification schemes, an online dual-network-based action-dependent heuristic dynamic programming method and a critic-only Q-learning approach are presented. Finally, the optimal control strategy is applied to a benchmark microgrid system to demonstrate the effectiveness of performance optimization.

在实际工程中，工业系统的通信网络很复杂，并且系统模型通常不可用。为了克服数学模型的要求，本文介绍了几种基于人工智能的多访问边缘计算算法，用于基准微电网的性能优化控制。首先，提出一种基于神经网络的识别方案，结合在线自适应动态规划学习方法，避免了系统模型的需求。但是，没有考虑识别错误。接下来，为了在不使用识别方案的情况下实现无模型的目的，提出了一种基于在线双网络的基于动作的启发式动态规划方法和仅批评者的Q学习方法。最后，