用物理神经网络自动生成可自适应调参的PID控制器。
Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
- 基于PINN自动微分计算梯度，实现增益在线优化。
- 在时域与频域均验证了控制稳定性与响应性能。
- 适合需处理非线性系统的工业控制场景。
本文提出一种基于自适应增益优化原理的数据驱动PID控制器设计方法，利用为预测建模生成的物理信息神经网络（PINNs）。该方法通过PINN的自动微分获取PID增益优化的梯度，采用基于跟踪误差和控制输入的代价函数实现模型预测控制。通过优化基于PINNs的PID增益，实现了保证系统稳定性的自适应增益调节，并能有效应对系统非线性。所提方法构建了一个将动态控制系统PINNs模型系统集成至闭环控制的框架，可直接应用于PID控制设计。一系列数值实验从时域与频域控制角度验证了该方法的有效性。
原文摘要 · Abstract (English)
This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed control design method utilizes gradients of the PID gain optimization, achieved through the automatic differentiation of PINNs, to apply model predictive control using a cost function based on tracking error and control inputs. By optimizing PINNs-based PID gains, the method achieves adaptive gain tuning that ensures stability while accounting for system nonlinearities. The proposed method features a systematic framework for integrating PINNs-based models of dynamical control systems into closed-loop control systems, enabling direct application to PID control design. A series of numerical experiments is conducted to demonstrate the effectiveness of the proposed method from the control perspectives based on both time and frequency domains.
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