Multiscale-control of the low-temperature combustion process GCAI

用于低温燃烧过程GCAI的闭环控制的现有技术方法基于循环到循环反馈控制。然而，这些方法仅允许在非常有限的发动机映射中稳定操作。通过基于周期到周期的控制，只有在相同时间尺度上发生的动态效应和干扰才能被控制。决定低温燃烧的稳定性和排放特性并在内部循环时间水平上运行的相关物理化学过程无法控制。出于这个原因，TP1研究了考虑较小时间尺度的多尺度控制算法。预期在成功控制这些关键时间尺度后，运营图将显著扩大，同时提高效率并减少污染物排放。作为第一个资助期的一部分，开发并实施了多尺度控制方法的具体概念。在TP 1中，针对负荷和速度瞬态运行场景，解决了多尺度控制的面向控制的扩展。为了明确考虑发动机转速，打算扩展当前模型，以包括转速变化的影响。为此，在发动机台架上与TP3合作进行了新的试验，并在分析效果的基础上对模型进行了扩展。此外，还将研究将研究单位开发的反应动力学纳入缩短时间的模型，以改善预测和相关的控制质量。在这方面，特别是模型降阶技术将被审查。为了解决显着的过程不确定性，考虑进一步的干扰形式ofphysically动机的干扰模型将被研究。此外，将研究鲁棒控制方法。虽然TP 1侧重于相应的系统特定的问题制定，特别是对不确定性的充分描述，开发的算法将在TP 2中扩展，以满足高计算需求，尽管增加操作所产生的鲁棒approach.Finally，开发的控制算法验证发动机试验台上与TP 3。决定性的标准是覆盖的运行图，在该图中，可以实现稳定的运行，同时考虑瞬态负载和速度曲线以及减少排放和提高效率的潜力。

State-of-the-art approaches for closed-loop control of the low temperature combustion process GCAI are based on cycle-to-cycle feedback control. However, these approaches allow only a stable operation in a very limited engine map. With cycle-to-cycle based control, only the dynamic effects and disturbances that occur on the same time scale can be controlled. The relevant physicochemical processes that determine the stability and emission characteristics of low temperature combustion and run on an inner-cyclic time-level, cannot be controlled. For this reason TP1 investigates multiscale control algorithms that account the smaller time scales. It is expected that with successful control on these critical time scales, the operating map will be significantly expanded while improving efficiency and reducing pollutant emissions. As part of the first funding period, a concrete concept for the multi-scale control approach was developed and implemented.In TP1, the control-oriented expansion of the multi-scale control with regard to load- and speedtransient operating scenarios is addressed. In order to take the engine speed explicitly into account, it is intended to expand the current models to include the effects of changing speed. For this purpose, new experiments are carried out on the engine test bench in cooperation with TP3 and the models are extended based on the analysed effects. In addition, the integration of the reaction kinetics, which was developed in the research unit, into the reduced-time models to improve prediction and the associated control quality will be investigated. In this regard, particularly model order reduction techniques will be examined.To tackle the significant process uncertainty, consideration of further disturbances in form ofphysically motivated disturbance models will be studied. In addition, robust control approaches will be investigated. While TP1 focuses on the corresponding system-specific problem formulation, in particular on the adequate description of the uncertainty, the developed algorithms will be expanded in TP2 to meet the high computational demands despite the increasing operations resulting from the robust approach.Finally, the developed control algorithms are validated on the engine test bed together with TP3. Decisive criteria are the covered operating map, in which stable operation can be realized while taking into account transient load and speed profiles as well as the potential for reducing emissions and increasing efficiency.