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Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines

机译:内燃机中归一化空气对燃料比的模糊模型预测控制

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In this paper, a fuzzy model predictive controller is developed to reduce the emission pollutants in spark ignition internal combustion engines. The path to this control goal is regulating the amount of normalized air-to-fuel ratio in the engine. In order to generate the simulation data, mean value engine model is simulated. To approximate the nonlinear and fast time-varying dynamics of the engine, a modified fuzzy relational model is trained offline in batch mode. For training, gradient descent back propagation algorithm along with evolutionary asexual reproduction optimization algorithm is used. Nonlinear structure of the fuzzy model of the engine imposes nonlinear optimization to produce control signals. Hence, gradient descent algorithm is used to generate online control signals. The effectiveness and robustness of the controller are evaluated through simulations.
机译:在本文中,开发了模糊模型预测控制器,以减少火花点火内燃机中的排放污染物。 该控制目标的路径是调节发动机中的归一化空气到燃料比的量。 为了生成模拟数据,模拟平均值引擎模型。 为了近似发动机的非线性和快速时变动力学,在批处理模式下训练了修改的模糊关系模型。 为了训练,使用梯度下降回到传播算法以及进化的无性再现优化算法。 发动机模糊模型的非线性结构施加非线性优化以产生控制信号。 因此,使用梯度下降算法来生成在线控制信号。 控制器的有效性和稳健性通过模拟来评估。

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