Yamaha Hatsudoki Kabushiki Kaisha

Yamaha Motor Company Ltd

Shigeki Hashimoto

Masashi Yamaguchi

Sergei V Ulyanov

A reduced control system suitable for control of an engine as a nonlinear plant is described. The reduced control system is configured to use a reduced sensor set for controlling the plant without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.

System for intelligent control of an engine based on soft computing

A fuel injection control system of an engine includes: (a) an intake air quantity estimation unit for estimating the quantity of intake air; (b) an intake fuel quantity estimation unit for estimating the quantity of intake fuel; (c) an estimated air-fuel ratio calculation unit for calculating an estimated air-fuel ratio; (d) a target air-fuel ratio setting unit for setting a target air-fuel ratio; (e) a feedback control unit for providing a fuel injection signal to the engine, which fuel injection signal is outputted also to the intake fuel quantity estimation unit as one of the predetermined signals; and (f) an actual air-fuel ratio deviation estimation unit for estimating a deviation of an actual air-fuel ratio or a factor correlated thereto from a predetermined level, which unit is programmed to output a deviation signal based on predetermined signals. In the above, at least one unit selected from the intake air quantity estimation unit, the intake fuel estimation unit, and the target air-fuel ratio setting unit is provided with a learning function which is programmed to modify output from the at least one unit based on the deviation signal used as teacher data to minimize the deviation. The system requires a reduced number of sensors.

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Shizuoka, Japan

Shigeki Hashimoto