Abstract
Over the last decades, diesel engines gained more and more popularity. The trend started in 1997 with the introduction of common rail injection and after the Kyoto protocols set new targets for overall CO2 emissions. As the diesel engine emits less CO2 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.
In 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. NNs can approximate any nonlinear function closely, however, the effectiveness of a general multilayer NN is limited in problems that require online learning. 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 first time to 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.
