Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.
Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada.
PLoS One. 2020 Dec 10;15(12):e0243320. doi: 10.1371/journal.pone.0243320. eCollection 2020.
Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning techniques offer a way to extend PID controllers beyond their linear control capabilities by using neural networks. However, such an extension comes at the cost of losing stability guarantees and controller interpretability. In this paper, we examine the utility of extending PID controllers with recurrent neural networks--namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of complex control systems and highlight how they can be a scalable and interpretable option for modern control systems. To do so, we provide an extensive study using four benchmark systems that represent the most common control engineering benchmarks. All control environments are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second contribution, we address the lack of interpretability that prevents neural networks from being used in real-world control processes. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, making them understandable for engineers. This combination of rigorous evaluation paired with better interpretability is an important step towards the acceptance of neural-network-based control approaches for real-world systems. It is furthermore an important step towards interpretable and safely applied artificial intelligence.
现代自动化系统在很大程度上依赖于闭环控制，其中控制器通过基于观测的动作与被控过程进行交互。这些系统越来越复杂，但大多数部署的控制器都是线性比例积分微分 (PID) 控制器。PID 控制器在线性和近线性系统上表现良好，但它们的简单性与可靠控制复杂过程所需的鲁棒性不一致。现代机器学习技术提供了一种通过使用神经网络将 PID 控制器扩展到其线性控制能力之外的方法。然而，这种扩展是以失去稳定性保证和控制器可解释性为代价的。在本文中，我们研究了使用递归神经网络——即广义动态神经网络 (GDNN)——扩展 PID 控制器的效用；我们表明，GDNN（神经）PID 控制器在一系列复杂控制系统上表现良好，并强调了它们如何成为现代控制系统的可扩展和可解释的选择。为此，我们使用四个代表最常见控制工程基准的基准系统进行了广泛的研究。所有控制环境都在有噪声和无噪声以及有干扰和无干扰的情况下进行了评估。神经 PID 控制器在 16 项任务中的 15 项中表现优于标准 PID 控制，在 16 项任务中的 13 项中表现优于基于模型的控制。作为第二项贡献，我们解决了神经网络无法在实际控制过程中使用的可解释性问题。我们使用有界输入有界输出稳定性分析来评估神经网络建议的参数，使它们对工程师来说是可理解的。这种严格评估与更好的可解释性的结合是接受基于神经网络的控制方法用于实际系统的重要步骤。此外，这也是实现可解释和安全应用人工智能的重要步骤。
