Alejandro-Sanjines Ulbio, Maisincho-Jivaja Anthony, Asanza Victor, Lorente-Leyva Leandro L, Peluffo-Ordóñez Diego H
Escuela Superior Politécnica del Litoral, Guayaquil 090903, Ecuador.
SDAS Research Group, Ben Guerir 43150, Morocco.
Biomimetics (Basel). 2023 Sep 19;8(5):434. doi: 10.3390/biomimetics8050434.
Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor-critic agent, where its objective is to optimize the actor's policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent's learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.
自动化工业过程需要一个控制器来获得与用户指示的参考信号相似的输出信号。有诸如PID之类的控制器，如果系统不改变其初始条件，这些控制器是有效的。然而，如果情况并非如此，则必须对控制器进行重新调整，这会影响生产时间。在这项工作中，基于强化学习的人工智能算法为直流电动机调速系统开发了一种自适应PID控制器。该算法使用了一个演员-评论家智能体，其目标是优化演员的策略并训练评论家以获得奖励。这将在无需了解系统的情况下生成适当的增益。使用了带有双延迟的深度确定性策略梯度（DDPG TD3），其网络由300个神经元组成用于智能体的学习。最后，使用成本函数将获得的控制器的性能与经典控制的控制器进行比较。
