[论文解读] General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison.

Johannes Günther, Elias Reichensdörfer+2 more|arXiv (Cornell University)|May 30, 2019

Fault Detection and Control Systems被引用 1

一句话总结

本文提出通用动态神经网络（GDNN），通过自适应、可解释的学习方法增强PID控制器，以应对复杂控制系统的需求。通过将循环神经网络与有界输入有界输出（BIBO）稳定性分析相结合，GDNN-PID控制器在16项基准任务中的15项中优于标准PID和基于模型的PID控制，同时保持了可解释性与稳定性保证。

ABSTRACT

Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet most 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 offers a way to extend PID controllers beyond their linear 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 control systems and highlight how they can be a scalable and interpretable option for control systems. To do so, we provide an extensive study using four benchmark systems that represent the most common control engineering benchmarks. All control benchmarks 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, thus making them understandable. This combination of rigorous evaluation paired with better interpretability is an important step towards the acceptance of neural-network-based control approaches. It is furthermore an important step towards interpretable and safely applied artificial intelligence.

研究动机与目标

• 解决传统线性PID控制器在控制复杂非线性过程时的局限性。
• 克服基于神经网络的控制系统中性能与可解释性之间的权衡。
• 开发一种可扩展且稳定的PID控制器神经扩展方法，同时保持控制器的透明性。
• 在包含噪声和扰动的多样化、真实控制场景中评估所提方法。

提出的方法

• 将通用动态神经网络（GDNN）作为标准PID控制架构的动态、循环扩展进行实现。
• 训练GDNN以根据系统反馈和过程动态实时自适应调整PID参数。
• 应用有界输入有界输出（BIBO）稳定性分析，以验证所学控制器参数的稳定性。
• 通过形式化稳定性准则分析神经网络的参数输出，实现可解释性。
• 使用四项标准控制工程基准测试在不同条件下评估性能，包括噪声和扰动。
• 在所有配置下将GDNN-PID与标准PID和基于模型的控制策略进行比较。

实验结果

研究问题

• RQ1 与标准PID和基于模型的控制器相比，基于GDNN的PID控制器是否能在复杂非线性系统上实现更优的控制性能？
• RQ2 GDNN-PID控制器在存在噪声和外部扰动的情况下，其稳定性和鲁棒性在多大程度上得以保持？
• RQ3 BIBO稳定性分析是否可有效用于解释和验证神经网络在控制背景下生成的参数？
• RQ4 GDNN-PID方法在多样化控制系统基准测试中的可扩展性和泛化能力如何？

主要发现

• 在16项基准任务中的15项中，GDNN-PID控制器优于标准PID控制，表现出显著的性能提升。
• 在16项任务中的13项中，GDNN-PID控制器超越了基于模型的控制策略，表明其具备强大的泛化能力和适应性。
• BIBO稳定性分析的整合实现了对控制器参数的正式验证，增强了可解释性与可信度。
• 该方法在存在噪声和扰动的条件下仍保持了稳健性能，证实其在真实场景中的抗干扰能力。
• 所提方法成功平衡了高性能与可解释性，解决了神经控制在实际部署中面临的关键障碍。
• 结果表明，GDNN-PID是一种可扩展且可靠的现代控制系统替代方案，适用于同时需要自适应性与安全保证的场景。