To address the problems of the slow convergence and inefficiency in the existing adaptive PID controllers, we propose a new adaptive PID controller using the asynchronous advantage actor–critic (A3C) algorithm. Firstly, the controller can train the multiple agents of the actor–critic structures in parallel exploiting the multi-thread asynchronous learning characteristics of the A3C structure. Secondly, in order to achieve the best control effect, each agent uses a multilayer neural network to approach the strategy function and value function to search the best parameter-tuning strategy in continuous action space. The simulation results indicate that our proposed controller can achieve the fast convergence and strong adaptability compared with conventional controllers.

中文翻译：

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基于异步优势动子学习方法的自适应PID控制器的设计与应用

为了解决现有自适应PID控制器收敛速度慢和效率低下的问题，我们提出了一种使用异步优势行为者评论（A3C）算法的新型自适应PID控制器。首先，控制器可以利用A3C结构的多线程异步学习特性并行地训练行为者-批评结构的多个代理。其次，为了获得最佳的控制效果，每个代理使用多层神经网络来接近策略功能和值功能，以在连续动作空间中搜索最佳的参数调整策略。仿真结果表明，与常规控制器相比，本文提出的控制器可以实现快速收敛和较强的适应性。