In the article a method which is able to provide the required performance level of a system is proposed. Its principle is to combine the results of conventional control methods with those of methods based on nonconventional, for example, machine-learning-based ones. In more detail, it designs a robust linear parameter varying (LPV) control in a predefined form, whose output is equivalent to the output of a machine-learning-based control inside a predefined operational range. Outside of the operation range the output of the machine-learning-based control is overridden, while the intervention with the performance level is guaranteed. The efficiency of the proposed method is illustrated through an example on the semiactive suspension control design. The nonlinearities in the dynamics of the magneto-rheological damper are considered through a nonlinear parameter varying (NLPV) model. It designs an NLPV model-based LPV control, which is combined with a neural network to achieve preview capability.

中文翻译：

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通过稳健的线性参数变化框架确保具有非常规控制系统的半主动悬架的性能要求

在这篇文章中，提出了一种能够提供系统所需性能水平的方法。其原理是将传统控制方法的结果与基于非常规方法（例如基于机器学习的方法）的结果相结合。更详细地说，它以预定义的形式设计了一个鲁棒的线性参数变化 (LPV) 控制，其输出等效于预定义操作范围内基于机器学习的控制的输出。在操作范围之外，基于机器学习的控制的输出被覆盖，同时保证性能水平的干预。通过半主动悬架控制设计的示例说明了所提出方法的效率。通过非线性参数变化 (NLPV) 模型考虑了磁流变阻尼器动力学中的非线性。它设计了基于 NLPV 模型的 LPV 控制，结合神经网络实现预览功能。