Research on the Speed of Diesel Engine Based on Improved BP Neural Network Controller

Abstract:

Marine diesel engine is of characteristics of non-linear and time-invariant, so it is difficult to be controlled with traditional PID controller. An adaptive controller based on back-propagation (BP) neural networks was put forwarded for marine diesel engine speed control system, where two neural networks are proposed to control the position loop and speed loop. The adaptive controller was improved was improved via introducing relative error in target evaluation function of the BP neural network, and obtain sensitivity function of diesel engine output with respect to its input using a differential equation. The controller has self-learning and adaptive capacity. It can also optimize the PID controller parameters online. The controller was experimentally evaluated on rack position actuator of marine diesel engine simulated based on a diesel hardware-in-loop system of dSPACE. Finally, tests on a diesel engine demonstrated that the controller can satisfy the transient and steady demands of speed regulation system.

• Periodical: Applied Mechanics and Materials (Volume 281)
• Pages: 105-111
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.281.105
• Online since: January 2013
• Authors: Yong Shi, Lian Yu Zhang, Jun Sun, Hong Guang Zhang
• Keywords: Adaptive Controller, BP Neural Network (BPNN), Marine Diesel Engine, Rack Position Actuator, Speed Control

References

[1] LI Yuan chun, ZHOU Xiao, GAO Wei．Control of Rack Position Actuator and Electronic Governing System in Vehicle Diesel Engine. Transactions of Csice(2002), 20, 1, pp.84-88.

[2] Jinzhu Peng, Rickey Dubay. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics. ISA Transactions (2011), 50, p.588–598.

DOI: 10.1016/j.isatra.2011.06.005

[3] CHEN Yu-tao, ZENG Fan-ming, CHEN Lin-gen. Study on Gain Scheduling Self-Adaptive Control Strategy for Marine CODAD Propulsion Plant. Chinese Internal Combustion Engine Engineering(2012). 3, pp.68-71.

[4] Yong Zhou, Qing-jiang Zhang, Qi-xun Zhou. Research on Self-Adapting PID Control of Digital Governor Proceedings of 2010 International Conference on Circuit and Signal Processing & 2010 Second IITA International Joint Conference on Artificial Intelligence. Volume 2. ( 2010), 12, pp.157-161.

[5] Chanchal Dey, Rajani K. Mudi. An improved auto-tuning scheme for PID controllers. ISA Transactions, Volume 48(2009), 10, pp.396-409.

DOI: 10.1016/j.isatra.2009.07.002

[6] Metin Demirtas. Off-line tuning of a PI speed controller for a permanent magnet brushless DC motor using DSP. Energy Conversion and Management, In Press, Corrected Proof, (2010).

DOI: 10.1016/j.enconman.2010.06.067

[7] Jamuna Venkatesan, S. Rama Reddy. Neural network controlled energy saver for induction motor drive. Journal of industrial technology, Volume 26(2010), 1.

[8] Selami Beyhan, Musa Alcı. Stable modeling based control methods using a new RBF network. ISA Transactions, In Press, Corrected Proof, (2010).

DOI: 10.1016/j.isatra.2010.04.005

[9] Song Bailing．Rapid Control Prototyping Research of the BP Neural Network Speed Governing Controller for Diesel．Small Internal Combustion Engine and Motorcycle(2009), 38, 2, pp.50-53.

[10] TANG Ji-ren, LEI Yu-yong, NIE Guang-wei. Application of PID Neural Network in Control of Diesel Engine Speed . Coal Mine Machinery(2010), 1. pp.201-204.

[11] Sun Bin, Zeng Fanming, Li Yanfei. Research on Feed Forward Compensation Control Based on FNN for Diesel Engine of Ship Power Plant. Journal of Wuhan University of Technology (2011), 3, pp.567-570.

[12] SUN Yu, ZHANG Jian. Application of Neural Mode Theory to Speed Control of Diesel Engine. Journal of Liaoning University of Technology(2011), 4, pp.30-34.