Abstract Reactivity controlled compression ignition (RCCI) is a promising combustion concept which uses two fuels to combine high thermal efficiencies and low engine-out NOx and soot emissions. The combustion concept relies on controlled auto-ignition and is sensitive for changing injection pressure, fuel quality, etc. Consequently, modeling and control of this complex combustion concept is not straightforward. In this work, Gaussian process regression is used to arrive at a data-driven model for a gasoline-diesel RCCI engine. This data-driven model is employed in a robust optimization approach that uses a nested particle swarm optimization. The designed (feedforward) control inputs maximize the efficiency of the RCCI engine while satisfying safety and emissions constraints under various disturbed conditions. In the simulation study, robust performance is obtained, and the robust efficiency is very similar to the efficiency under nominal condition.

中文翻译：

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使用嵌套惩罚粒子群的 RCCI 引擎的鲁棒约束优化

摘要 反应性控制压缩点火 (RCCI) 是一种很有前途的燃烧概念，它使用两种燃料来结合高热效率和低发动机 NOx 和碳烟排放。燃烧概念依赖于受控自动点火，并且对改变喷射压力、燃料质量等很敏感。因此，这种复杂燃烧概念的建模和控制并不简单。在这项工作中，高斯过程回归用于为汽油-柴油 RCCI 发动机建立数据驱动模型。该数据驱动模型用于使用嵌套粒子群优化的稳健优化方法。设计的（前馈）控制输入最大限度地提高了 RCCI 发动机的效率，同时满足各种干扰条件下的安全和排放限制。在模拟研究中，