基于喷射增材制造的学习控制策略
A Learn-and-Control Strategy for Jet-Based Additive Manufacturing
摘要：
在本文中，我们开发了一个基于物理指导的递归神经网络（RNN）模型的预测几何控制框架，用于基于喷射的增材制造（AM）。由于其物理上可解释的架构，该模型的参数是通过使用少量层的输入输出数据通过反向传播训练网络来获得的。此外，我们证明，该模型可以双重表示，使层液滴输入模式（每一层）的一部分，现在成为网络参数，通过反向传播学习。该方法适用于前馈预测控制，其中网络参数从先前数据离线学习，并且合成所有待打印层的控制输入图案。预测控制器的稳定性的充分条件。此外，我们设计了一种算法，有效地实现反馈预测控制，其中网络参数和输入模式（滚动时域）在线学习，没有增加提前期计算。前馈控制方案的实验表明，提高RMS参考跟踪误差超过30%的最先进的状态。我们还实验证明，过程的不确定性补偿的在线学习和反馈控制。
英文摘要：
In this paper, we develop a predictive geometry control framework for jet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model's parameters are obtained by training the network through back propagation using input-output data from a small number of layers. Moreover, we demonstrate that the model can be dually expressed such that the layer droplet input pattern for (each layer of) the part to be fabricated now becomes the network parameter to be learned by back-propagation. This approach is applied for feedforward predictive control in which the network parameters are learned offline from previous data and the control input pattern for all layers to be printed is synthesized. Sufficient conditions for the predictive controller's stability are then shown. Furthermore, we design an algorithm for efficiently implementing feedback predictive control in which the network parameters and input patterns (for the receding horizon) are learned online with no added lead time for computation. The feedforward control scheme is shown experimentally to improve the RMS reference tracking error by more than 30% over the state of the art. We also experimentally demonstrate that process uncertainties are compensated by the online learning and feedback control.
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