Coordination Funds

基于循环的控制是目前正在研究的控制低温燃烧的主要方法。然而，这种控制只允许LTC在非常有限的操作范围内稳定。在基于周期的控制中，只能控制周期积分的系统动态和扰动变量。在循环时间尺度上发生的化学物理过程与LTC的稳定性和排放生成有关。它们不会受到基于周期的控制的影响。因此，研究单位正在研究多尺度控制，以便能够考虑到较小的周期内时间尺度。预计可以提高稳定性，显著扩大运行范围，提高效率，减少污染物排放。多尺度控制是解决这一问题的一种创新和新颖的方法。在研究单元中，对LTC过程PCCI和GCAI进行了研究。提出了一种由周期控制和周期内控制器相结合的控制器体系结构。为了处理复杂的非线性多变量系统动力学问题，基于过程模型的优化方法被发展和应用。为此，基于迭代学习的非线性模型预测控制(IL-NMPC)的数值方法被用来作为循环过程的基础。具体地，对于这些过程，对可能的操纵和控制变量以及这些变量在不同时间尺度上的分配进行了分析和评估。此外，还对优化任务的合适配方进行了研究。建立了GCAI过程和PCCI过程的控制器内部模型。通过在单缸试验台上的实验，建立了物理模型，并对过程进行了定量描述。此外，重点介绍了在优化的试验室(如反应堆和火焰)上使用替代燃料所获得的燃烧过程的动力学描述。此外，还考虑了离子电流作为一种新的传感器概念在控制系统中的集成。所开发的描述被转换成可有效地用于实时控制的结构。此外，为此目的将创建物理激励的灰箱模型。在物理和系统理论观测的基础上，将建立扰动变量模型。最后，所得到的控制算法将在发动机试验台上进行验证。评价标准是燃烧稳定性、可实现稳定运行和瞬时负荷分布的可覆盖运行范围以及减排和提高效率的潜力。

Cycle-based control is the main research approach currently under investigation for controlling low-temperature combustion (LTC). This control, however, only allows the stabilization of the LTC in a very limited operation range. With a cycle-based control, only cycle-integral system dynamics and disturbance variables can be controlled. The chemical-physical processes, which occur on in-cycle time scales, are relevant for the stability and emission generation of the LTC. They cannot be influenced by a cycle based control. Consequently, the research unit is investigating multi-scale control in order to be able to take the smaller, in-cycle time scales into account. It is expected that the stability can be improved, the operation range can be extended significantly, the efficiency can be increased and the pollutant emissions can be reduced. Multi-scale control is an innovative and novel approach to solve this problem. In the research unit the LTC processes PCCI and GCAI are studied. A controller architecture consisting of a combination of cycle-to-cycle control and an in-cycle controller is developed. To account for the complex nonlinear multivariable system dynamics, optimization-based methods based on models of the process are developed and applied. For this purpose, numerical methods for iterative learning nonlinear model-based predictive control (IL-NMPC) developed for cyclic processes are used as a basis. Specifically for the processes, an analysis and evaluation of possible manipulated and controlled variables as well as the allocation of these variables to the different time scales is done. In addition, suitable formulations of the optimization task are also investigated. The controller-internal models are developed for both the GCAI and the PCCI process. Derived from experiments on singlecylinder test benches, physical models and quantitative descriptions of the processes are created. Furthermore, kinetic descriptions of the combustion processes, which are obtained by using surrogate fuels on optimized test laboratories such as reactors and flames, are emphasized. Furthermore, the integration of ion current as a novel sensor concept in the control system is considered. The developed descriptions are transferred into a structure that can be effectively used for realtime control. Furthermore, physically motivated grey box models are to be created for this purpose. Based on physical and system theoretical observations, models for disturbance variables will be developed. Finally, the resulting control algorithms will be validated on engine test benches. Evaluation criteria are the combustion stability, the coverable operation range in which stable operation and transient load profiles can be realized, and the potential for emission reduction and efficiency increase.