Proceedings of the International Naval Engineering Conference & Exhibition (INEC)
Published October 3, 2018 | Version v1
Conference paper Open

Study on Intelligent Speed Control Algorithm for Diesel Engine

Creators

  • 1. Department of power and energy engineering, Harbin Engineering University, Harbin, 150001, China

Description

In this paper, two types of intelligent controllers are designed based on the RBF neural network algorithm and active disturbance rejection control (ADRC) technology to solve the problem that the dynamic speed is difficult to control for diesel engine. In order to verify the speed regulation performance of the intelligent control system a mean value modeling (MVM) of D6114 generation diesel engine was established for off-line simulation, and the above two intelligent algorithms were compared with PID. The results show that the ADRC has a relatively small overshoot and quick dynamic response for diesel engine speed control. Radial basis function (RBF) intelligent algorithm can real-timely optimize the control parameters and has good adaptability in speed control, the transient rate decreased by 1.6% and stable time is shortened by 1.46s compared with common PID algorithm. The control performance under condition of start-up, idle speed and mutation load is compared. The results show that RBF neural network controller has good learning and adaptive capabilities for speed control of diesel engine. It can balance the stability at different speed and output of large rack displacement in a short time when the load changes to reduce the influence of load change on the rotational speed. For ADRC controller, it maintains good effect when the nonlinearity in the system increases. Improvement of PID using TD has fast response at startup and under disturbances. With NLSEF and ESO, NLSEF can automatically adjust the output according to the speed deviation to reduce interference while ESO can correct the control amount to improve the control effect of load change.

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