Parallel multi-level learning and optimization algorithms for control of cyclic processes on embedded systems

这个子项目的目标是使用创新的算法思想来实时控制快速、周期性的过程。与亚琛和苏黎世的PCCI和GCAI引擎的实际操作相关的挑战尤其需要新的算法开发和数字上有利的问题公式，这些公式超出了第一个资助期所取得的结果。现实操作的相关特点，首先是对计算时间的要求更严格。更高的转速（超过3000转/分钟）导致准备的最大计算时间显着缩短，并且-具有相同的执行器延迟-特别是对于反馈阶段（< 1毫秒）。此外，对于实际电机运行不可或缺的内循环控制需要在更小的时间尺度上进行并行计算。其次，实际运行具有时变负荷特征。对于GCAI过程，基于优化的参考发生器应防止控制器在瞬态运行时收敛性受损。可行的周期参考的生成需要自己定制的算法，并且应该与控制和估计并行运行。第三，研究表明GCAI过程的随机特性在不同的发动机特征图上是不同的。特别是在迟发点火情况下，目前设计的控制系统不能充分抑制系统的随机行为。为了保证鲁棒性满足系统限制，需要开发基于优化控制的鲁棒变体。由于鲁棒问题的表述更具有挑战性，因此必须开发创新的算法和问题表述，以满足鲁棒MPC的实时性要求。

The goal of this subproject is to use innovative algorithmic ideas to enable the control of fast, cyclical processes in real time. The challenges associated with the real operation of the PCCI and GCAI engines in Aachen and Zurich in particular require new algorithmic developments and numerically favourable problem formulations that go beyond the results achieved in the first funding period. Relevant features of the realistic operation are, first of all, even tougher requirements on computation times. The higher rotational speeds (over 3000 rpm) lead to significantly shorter maximum computation times for the preparation and - with the same actuator delay - especially for the feedback phase (< 1 ms). Furthermore, the inner-cyclic control which is indispensable for real motor operation requires parallel calculations on an even smaller time scale.Secondly, real operation is characterized by time-dependent load profiles. For the GCAI process, an optimization-based reference generator should prevent controller convergence from being impaired in transient operation. The generation of feasible periodic references requires its own tailored algorithms and should run in parallel to control and estimation.Thirdly, it has been shown that the stochastic properties of the GCAI process vary across the engine characteristic map. Especially in case of late ignitions, the stochastic system behavior cannot be suppressed sufficiently with the control system designed so far. In order to guarantee a robust satisfaction of the system limitations, a robust variant of the optimization-based control needs to be developed. Since robust problem formulations are more challenging to solve, innovative algorithms and problem formulations have to be developed to meet the real-time requirements also with the robust MPC.