Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model. Physics-guided neural networks (PGNN), where a known physical model cooperates in parallel with a neural network, were recently proposed as a method to achieve high accuracy of the identified inverse dynamics. However, the flexible nature of neural networks can create overparameterization when employed in parallel with a physical model, which results in a parameter drift during training. This drift may result in parameters of the physical model not corresponding to their physical values, which increases vulnerability of the PGNN to operating conditions not present in the training data. To address this problem, this paper proposes a regularization method via identified physical parameters, in combination with an optimized training initialization that improves training convergence. The regularized PGNN framework is validated on a real-life industrial linear motor, where it delivers better tracking accuracy and extrapolation.
翻译:物理制导神经网络(PGNN)是已知物理模型与神经网络平行合作的一个已知物理模型,最近有人提议将这种网络作为一种方法,以实现所查明的反向动态的高度精确性;然而,神经网络的灵活性性质在与物理模型同时使用时可能造成超分数,从而导致培训期间的参数漂移。这种漂移可能导致物理模型参数的参数与其物理值不相称,从而增加PGNN在培训数据中不存在的操作条件下的脆弱性。为解决这一问题,本文件提议采用一种正规化方法,通过确定物理参数,结合优化的培训初始化,提高培训的趋同性。常规化PGNNN框架在现实工业线性发动机上得到验证,它提供更好的跟踪准确性和外推法。