论文标题
PID设计的基于模型的增强学习方法
A Model-Based Reinforcement Learning Approach for PID Design
论文作者
论文摘要
比例综合衍生（PID）控制器由于其直接实施而广泛用于各种工业过程控制应用程序。但是，在实践中微调PID参数以实现稳健的性能可能是一项挑战。本文提出了一个基于模型的增强学习（RL）框架来设计PID控制器，利用学习控制概率推断（PILCO）方法和Kullback-Leibler Divergence（KLD）。由于PID控制器的控制结构比网络基础函数更容易解释，因此PILCO给出的最佳策略被转换为一组不足的机械系统的强大的PID调谐参数。提出的方法是一般的，可以与几种基于模型和无模型的算法融合。通过在干扰和系统参数不确定性下对基准推车系统的模拟研究，证明了设计的PID控制器的性能。
Proportional-integral-derivative (PID) controller is widely used across various industrial process control applications because of its straightforward implementation. However, it can be challenging to fine-tune the PID parameters in practice to achieve robust performance. The paper proposes a model-based reinforcement learning (RL) framework to design PID controllers leveraging the probabilistic inference for learning control (PILCO) method and Kullback-Leibler divergence (KLD). Since PID controllers have a much more interpretable control structure than a network basis function, an optimal policy given by PILCO is transformed into a set of robust PID tuning parameters for underactuated mechanical systems. The presented method is general and can blend with several model-based and model-free algorithms. The performance of the devised PID controllers is demonstrated with simulation studies for a benchmark cart-pole system under disturbances and system parameter uncertainties.
