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，使快速，周期性的过程控制在真实的时间。与亚琛和苏黎世的PCCI和GCAI发动机的真实的运行相关的挑战尤其需要新的算法开发和数值上有利的问题公式，这些公式超出了第一个资助期所取得的结果。现实操作的相关特征首先是对计算时间的更严格要求。更高的旋转速度（超过3000 rpm）可显著缩短准备的最大计算时间，并且在相同的致动器延迟下，尤其是反馈阶段（< 1 ms）。此外，真实的电机运行所必需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，真实的运行的特点是负载曲线随时间变化。对于GCAI过程，基于优化的参考发生器应防止控制器收敛在瞬态操作中受损。可行的周期性参考的产生需要其自己的定制算法，并应并行运行的控制和estimation.Third，它已被证明，GCAI过程的随机特性在整个发动机特性图。特别是在点火延迟的情况下，迄今为止设计的控制系统不能充分抑制系统的随机行为。为了保证系统限制的鲁棒性满足，需要开发基于优化的控制的鲁棒变体。由于鲁棒问题公式更具有挑战性的解决，创新的算法和问题公式必须开发，以满足实时要求，也与鲁棒MPC。

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.