Complicated first principles modelling and controller synthesis can be prohibitively slow and expensive for high-mix, low-volume products such as hydraulic excavators. Instead, in a data-driven approach, recorded trajectories from the real system can be used to train local model networks (LMNs), for which feedforward controllers are derived via feedback linearization. However, previous works required LMNs without zero dynamics for feedback linearization, which restricts the model structure and thus modelling capacity of LMNs. In this paper, we overcome this restriction by providing a criterion for when feedback linearization of LMNs with zero dynamics yields a valid controller. As a criterion we propose the bounded-input bounded-output stability of the resulting controller. In two additional contributions, we extend this approach to consider measured disturbance signals and multiple inputs and outputs. We illustrate the effectiveness of our contributions in a hydraulic excavator control application with hardware experiments. To this end, we train LMNs from recorded, noisy data and derive feedforward controllers used as part of a leveling assistance system on the excavator. In our experiments, incorporating disturbance signals and multiple inputs and outputs enhances tracking performance of the learned controller. A video of our experiments is available at https://youtu.be/lrrWBx2ASaE.

翻译：对于液压挖掘机这类多品种、小批量的产品而言，基于第一性原理的复杂建模与控制器综合方法往往因耗时过长、成本过高而难以实施。作为替代方案，可采用数据驱动的方法，利用真实系统记录的轨迹训练局部模型网络，并通过反馈线性化推导出相应的前馈控制器。然而，以往的研究要求用于反馈线性化的局部模型网络不具备零动态特性，这限制了模型结构，从而制约了局部模型网络的建模能力。本文通过提出一个判据，克服了这一限制，该判据用于判断对具有零动态的局部模型网络进行反馈线性化是否能够产生有效的控制器。我们提出以所得控制器的有界输入有界输出稳定性作为该判据。在另外两项贡献中，我们将此方法扩展到考虑测量的扰动信号以及多输入多输出的情况。我们通过液压挖掘机控制应用的硬件实验，展示了所提方法的有效性。为此，我们利用记录的含噪声数据训练局部模型网络，并推导出用作挖掘机调平辅助系统一部分的前馈控制器。实验结果表明，引入扰动信号以及多输入多输出处理能够提升所学控制器的跟踪性能。实验视频可在 https://youtu.be/lrrWBx2ASaE 查看。