Mechatronic systems are commonly used in the industry, where fast and accurate motion performance is always required to guarantee manufacturing precision and efficiency. Nevertheless, the system model and parameters are difficult to be obtained accurately. Moreover, the high-order modes, strong coupling in the multi-axis systems, or unmodeled frictions will bring uncertain dynamics to the system. To overcome the above-mentioned issues and enhance the motion performance, this paper introduces a novel intelligent and totally model-free control method for mechatronic systems with unknown dynamics. In detail, a 2-degree-of-freedom (DOF) architecture is designed, which organically merges a generalized super-twisting algorithm with a unique iterative learning law. The controller solely utilizes the input-output data collected in iterations such that it works without any knowledge of the system parameters. The rigorous proof of convergence ability is given and a case study on flexture-joint dual-drive H-gantry stage is shown to validate the effectiveness of the proposed method.
翻译：机械系统通常用于该行业,需要快速和准确的运动性能,以保证制造精确和效率。然而,系统模型和参数很难准确获得。此外,高顺序模式、多轴系统中的强大组合或非模型摩擦将给系统带来不确定的动态。为了克服上述问题和加强运动性能,本文件为动力不明的机械系统引入了一种新的智能和完全无模型的控制方法。详细而言,设计了一个2度自由结构,将通用的超自动算法与独特的迭接学习法有机地结合。控制器仅利用在迭接法中收集的输入输出数据,从而在对系统参数一无所知的情况下运作。提供了严格的趋同能力证明,并进行了关于软化-联合双驱动H轨迹阶段的案例研究,以验证拟议方法的有效性。
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