Advanced Composites Manufacturing and Repair Using Integrated Distributed Actuation and Dynamic Network Control

由于其高刚性和轻质，碳纤维增强聚合物等复合材料正在汽车、航空航天和海运部门以及大型风力涡轮机等可再生能源系统中得到越来越多的使用。在许多复合材料的制造中，一个重要的步骤是高温固化，这涉及到在一个称为高压罐的密封压力容器中，通过精确的温度序列循环零件。这个项目的重点是将这些已完成的单个部件连接起来，形成一个完整的产品。虽然机械紧固件或低温粘合剂的性能不如高温复合粘合剂，但组装的系统太大，不能作为一个单元放置在高压灭菌器中。该项目考虑使用分布在整个接头上的嵌入式加热器来执行必要的热循环。为了不损害结构的完整性，这些加热器使用了与复合材料结构其余部分相同的碳纤维材料。在初步测试中，这种方法生产的接头在质量上与高压灭菌部件相当；该项目解决的挑战是在大型复杂结构(如飞机机翼)上实现这样的结果。这将通过重新利用最初为动态系统网络(如移动机器人)开发的先进控制技术来实现。同样的方法可用于修复损坏或疲劳的复合系统，以经济和可持续的方式延长昂贵基础设施的使用寿命。如果没有训练有素的劳动力能够使用，最先进的技术也是毫无用处的。该项目将为本科生和研究生提供先进制造和控制方面的培训和教育，其成果将用于推广和招募高中生。计划建立一个由多个分布式加热器组成的网络，该网络可以解决热损失的变化，例如，由于子结构不均匀，并使粘合剂在接合线上温度均匀，以实现粘合剂的一致固化。由于材料特性(例如，复合材料厚度)和边界条件(例如，由于子结构的存在)的不同，网络中每个分布式加热器系统的热动力学(输入电压到局部温度)的不同引起了控制挑战。目前用于网络多智能体系统的迭代方法大多适用于同质智能体动力学。对于异构型、网络化的多智能体系统，当不同智能体的动态行为都是一般的线性系统时，证明迭代学习的收敛是很有挑战性的。设计了一种新的基于逆的迭代控制，它可以校正每个智能体的动态，从而通过网络获得期望的输出。这项研究的智力价值是开发条件，量化可接受的建模不确定性，以确保在存在异质性的情况下迭代方法的收敛。因此，这项研究将促进网络多智能体系统迭代学习理论的发展及其在制造等重大技术问题上的适用性。

Because of their high stiffness and light weight, composites such as carbon-fiber reinforced polymers are finding increased use in the automotive, aerospace, and marine transportation sectors and in renewable energy systems such as large wind turbines. An important step in the manufacture of many composites is high-temperature curing, which involves cycling the part through a precise temperature sequence in a sealed pressure vessel called an autoclave. This project focuses on the joining of these finished individual parts to form a complete product. While mechanical fasteners or low-temperature adhesives have inferior performance to high-temperature composite bonds, the assembled system is far too large to place in an autoclave as a unit. This project considers the use of embedded heaters distributed throughout the joint to execute the necessary thermal cycle. So as not to compromise the integrity of the structure, these heaters are made using the same carbon-fiber material as the rest of the composite structure. In preliminary tests this approach has produced joints that equal autoclaved parts in quality; the challenge addressed by this project is achieving such a result over a large, complex structure, such as an airplane wing. This will be done by repurposing advanced control techniques originally developed for networks of dynamic systems, such as mobile robots. The same approach may be used to repair damaged or fatigued composite systems, economically and sustainably extending the useful life of expensive infrastructure. The most advanced technology is useless without a trained workforce capable of employing it. This project will provide training and education to undergraduate and graduate students in advanced manufacturing and controls, and the results will be used in outreach and recruitment to high-school students.A network of multiple distributed heaters that can account for variations in heat loss, e.g., due to uneven sub-structures, and enable uniform temperatures at the bondline for consistent curing of the adhesive are planned. The control challenge arises from differences in the thermal dynamics of each of the distributed heater system (input voltage to local temperature) in the network due to differences in material properties (e.g., thickness of composites) and boundary conditions (e.g., due to the presence of substructures). Current iterative approaches for networked multi-agent systems are mostly applicable to homogenous agent dynamics. It is challenging to prove convergence of iterative learning for heterogeneous, networked multi-agent systems when the dynamics of the different agents are general linear systems. A novel inversion-based iterative control is planned that can correct for the dynamics of each agent and, thereby, can achieve the desired output specified through the network. The intellectual merit of the research is to develop conditions that quantify the acceptable modeling uncertainty for ensuring convergence of iterative approaches in the presence of heterogeneity. Thus, the research will advance the state-of-the-art in iterative-learning theory for networked multi-agent systems and its applicability to important technological problems such as manufacturing.