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Cyclic RGF regulation using adaptive IMC approach and statistical feedback criterion ?

机译：使用自适应IMC方法和统计反馈标准的循环RGF调节 ？

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Residual gas fraction (RGF) significantly affects fuel efficiency and emission of combustion engines, and usually be regulated by managing variable valve timing (VVT) system. Cyclic RGF regulation is challenging due to nonlinear parameter-varying dynamics as well as stochastic property of controlled objects. This paper addresses this issue by designing an adaptive internal model controller (AIMC) combined with statistical feedback criterion. Firstly, closed-loop RGF regulation problem based on VVT control is formulated. Secondly, AIMC is adopted to online identify plant dynamics and its inverse model. Note that the statistical criterion is creatively introduced in the feedback channel of AIMC to handle the stochastic property of RGF. Finally, experiments performed on an engine test bench show that the proposed controller not only possesses advantages of AIMC but also alleviates stochastic noise in feedback channel.
机译：残余气体分数（RGF）会显着影响燃油效率和内燃机的排放，通常通过管理可变气门正时（VVT）系统进行调节。由于非线性参数变化动力学以及受控对象的随机特性，循环RGF调节具有挑战性。本文通过设计一种结合统计反馈准则的自适应内部模型控制器（AIMC）来解决此问题。首先，提出了基于VVT控制的闭环RGF调节问题。其次，采用AIMC在线识别植物动力学及其逆模型。注意，在AIMC的反馈通道中创造性地引入了统计标准，以处理RGF的随机性。最后，在发动机试验台上进行的实验表明，所提出的控制器不仅具有AIMC的优势，而且还减轻了反馈通道中的随机噪声。

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