磁悬浮纳米定位系统的数据驱动多目标控制器优化
Data-Driven Multi-Objective Controller Optimization for a Magnetically-Levitated Nanopositioning System
摘要：
利用传统的基于模型的控制系统设计方法实现的性能通常严重依赖于运动动力学的精确建模。然而，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具挑战性的任务;并且通常必要的实际近似通常会使自动化系统在非最佳条件下运行。在多轴磁悬浮纳米定位系统的情况下，该问题可能会大大加剧，其中完全浮动行为和多轴耦合使得运动动力学的非常准确的识别在很大程度上是不可能的。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涉及重复运动，这可能在非最佳条件下产生大量运动数据。这些运动数据基本上包含丰富的信息;因此，存在开发智能自动化系统的可能性，以从这些运动数据中学习并驱动系统以数据驱动的方式朝着最佳状态运行。沿着这条线，然后，本文提出了一种数据驱动的控制器优化方法，从过去的非最优运动数据学习，以迭代地提高运动控制性能。具体而言，提出了一种新的数据驱动的多目标优化方法，能够自动估计的梯度和海森纯粹的基础上测量的运动数据;多目标代价函数适当地设计，以考虑到平滑和准确的轨迹跟踪。实验结果表明，该方法的有效性，而清楚地表明，我们的方法的实际吸引力相关的复杂机器人系统没有准确的模型。
英文摘要：
The performance achieved with traditional model-based control system design approaches typically relies heavily upon 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 render the automation system to operate in a non-optimal condition. This problem can be greatly aggravated in the case of a multi-axis magnetically-levitated nanopositioning system where the fully floating behavior and multi-axis 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 non-optimal 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 towards optimality in a data-driven manner. Along this line then, this paper proposes a data-driven controller optimization approach that learns from the past non-optimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multi-objective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multi-objective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. 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.
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