A Physics-Guided Data-Driven Feedforward Tracking Controller for Systems With Unmodeled Dynamics—Applied to 3D Printing
A Physics-Guided Data-Driven Feedforward Tracking Controller for Systems With Unmodeled Dynamics—Applied to 3D Printing
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DOI：
10.1109/access.2023.3244194
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发表时间：
2022-06
期刊：
影响因子：
3.9
通讯作者：
Cheng-Hao Chou;Molong Duan;C. Okwudire
中科院分区：
文献类型：
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作者：
Cheng-Hao Chou;Molong Duan;C. Okwudire
针对未建模的线性或非线性动力学系统，提出了一种混合（即物理引导数据驱动）前馈跟踪控制器。所提出的控制器基于滤波基函数（FBF）方法，因此称为混合FBF控制器。它将系统的前馈控制输入表述为一组基函数的线性组合，这些基函数的系数被选择以最小化跟踪误差。为了预测系统响应从而预测跟踪误差，基函数使用两个线性模型的组合进行滤波。第一个模型是基于物理的，在控制器执行期间保持不变，而第二个模型是数据驱动的，在控制器执行期间不断更新。为了确保其实用性和安全学习，所提出的混合FBF控制器具有处理数据采集延迟的能力，并且由于其固有的数据驱动反馈回路而具有检测即将发生的不稳定性的能力。通过对具有未建模的线性和非线性动力学的3D打印机进行振动补偿，验证了混合FBF控制器的有效性。与不包含数据驱动模型的标准FBF控制器相比，该混合FBF控制器在高速打印实验中显著提高了3D打印机的跟踪精度和打印质量。此外，混合FBF控制器的能力，以检测，从而潜在地避免，即将发生的不稳定，通过从实验中收集的数据离线证明。
A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The proposed controller is based on the filtered basis functions (FBF) approach, and hence called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. To predict the system response and thereby the tracking errors, the basis functions are filtered using a combination of two linear models. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the abilities to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. The effectiveness of the hybrid FBF controller is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer and the print quality are both significantly improved in experiments involving high-speed printing, compared to standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect, and hence to potentially avoid, impending instability is demonstrated offline using data collected online from experiments.