Data-driven linear quadratic tracking based temperature control of a big area additive manufacturing system

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Eleni Zavrakli, Andrew Parnell, Andrew Dickson, Subhrakanti Dey
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Abstract

Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control for the temperatures in the extruder of a Material Extrusion AM system, specifically a Big Area Additive Manufacturing (BAAM) system. Previous approaches for temperature control in AM either require the knowledge of exact model parameters, or involve discretisation of the state and action spaces to employ traditional data-driven control techniques. On the other hand, modern algorithms that can handle continuous state and action space problems require a large number of hyperparameter tuning to ensure good performance. In this work, we circumvent the above limitations by making use of a state space temperature model while focusing on both model-based and data-driven methods. We adopt the Linear Quadratic Tracking (LQT) framework and utilise the quadratic structure of the value function in the model-based analytical solution to produce a data-driven approximation formula for the optimal controller. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a BAAM system and perform an in-depth comparison of the performance of these methods. We find that we can learn an effective controller using solely simulated input–output process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the often intricate and complicated process model. We believe this result is an important step towards achieving autonomous intelligent manufacturing.

Abstract Image

基于数据驱动线性二次跟踪的大面积增材制造系统温度控制
设计高效的闭环控制算法是增材制造(AM)的一个关键问题,因为增材制造过程的各个方面都需要持续监控和调节,其中温度是一个特别重要的因素。在此,我们研究了材料挤出 AM 系统(特别是大面积增材制造 (BAAM) 系统)挤出机温度的闭环控制。以往的 AM 温度控制方法要么需要了解精确的模型参数,要么需要对状态和动作空间进行离散化,以采用传统的数据驱动控制技术。另一方面,能够处理连续状态和动作空间问题的现代算法需要进行大量的超参数调整,以确保良好的性能。在这项工作中,我们通过使用状态空间温度模型来规避上述限制,同时关注基于模型和数据驱动的方法。我们采用线性二次跟踪(LQT)框架,并利用基于模型的分析解决方案中值函数的二次结构,为最优控制器生成数据驱动的近似公式。我们使用 BAAM 系统挤出机温度演变模拟器演示了这些方法,并对这些方法的性能进行了深入比较。我们发现,仅使用模拟输入输出过程数据就能学习到有效的控制器。与基于模型的控制器相比,我们的方法性能相当,因此减少了对往往错综复杂的工艺模型的大量参数进行估计的需要。我们相信,这一成果是实现自主智能制造的重要一步。
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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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