Diesel Engine Combustion Control Based on Cerebellar Model Articulation Controller (CMAC) in Feedback Error Learning

摘要

The trend started in 1997 with the introduction of common rail injection and after some protocols set new targets for overall CO_2 emissions. As the diesel engine emits less CO_2 than its gasoline counterpart, it kept conquering more and more market shares. Conventional diesel engine control design is mainly based on the maps techniques which required too much time, money and human resources under the number of experiments under various environmental conditions. This makes increasing system complexity. In our authors group, we have proposed that the control structure has the feedback error learning, two-degree-of-freedom controller configuration, with advanced neural networks (NNs) as the feedforward controller along the model-based control method. On the other hand, a cerebellar model articulation controller (CMAC) is a non-fully connected perceptron like associative memory network with overlapping receptive fields, which is used to resolve problems that involve rapid growth and the learning difficulty. Then CMACs have the advantages of good generalization capability, fast learning ability, and simple computation. To our best knowledge, this is new introduce the cerebellar model articulation controller (CMAC) for the control diesel engine combustion control. The effectiveness of the proposed method will be confirmed through numerical simulations based on the Tokyo University diesel engine model with triple fuel injections.