The performance achieved with traditional model-based control system design approaches typically relies heavily on accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often renders the automation system to operate in a nonoptimal condition. This problem can be greatly aggravated in the case of a multiaxis magnetically levitated (maglev) nanopositioning system where the fully floating behavior and multiaxis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under nonoptimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate toward optimality in a data-driven manner. Along this line then, this article proposes a data-driven model-free controller optimization approach that learns from the past nonoptimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multiobjective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In this article, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

中文翻译：

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磁悬浮纳米定位系统的数据驱动多目标控制器优化

传统的基于模型的控制系统设计方法所实现的性能通常严重依赖于运动动力学的精确建模。但是，对当今日益复杂的系统的真实动态进行建模可能是一项极富挑战性的任务。通常通常需要的逼近近似值使自动化系统无法在最佳状态下运行。在多轴磁悬浮（磁悬浮）纳米定位系统的情况下，这个问题会大大恶化，在该系统中，完全浮动的行为和多轴耦合使得极其不可能准确地识别运动动力学。另一方面，在许多相关的工业自动化应用中，例如磁悬浮系统的扫描过程中，涉及重复运动，这些运动可能在非最佳条件下生成大量运动数据。这些运动数据实质上包含丰富的信息。因此，存在开发一种智能自动化系统以从这些运动数据中学习并驱动该系统以数据驱动方式朝着最优方向运行的可能性。沿着这条思路，本文提出了一种数据驱动的无模型控制器优化方法，该方法从过去的非最佳运动数据中学习，以迭代地提高运动控制性能。具体来说，提出了一种新颖的数据驱动的多目标优化方法，该方法能够完全基于所测量的运动数据自动估计梯度和Hessian。多目标成本函数经过适当设计，要考虑到平滑和准确的轨迹跟踪。在本文中，然后在磁悬浮纳米定位系统上进行了实验，以证明所提出方法的有效性，结果相当清楚地表明了我们的方法对相关复杂机器人系统的方法的实际吸引力，而没有精确的模型可用。