摘要
针对柴油机调速系统存在的非线性和时变性,以及传统PID控制效果不理想等特点,研究了船用柴油机转速控制优化问题。利用BP神经网络的自学习能力和自适应能力,对PID控制器的参数进行在线整定。基于BP-PID控制算法与MC9S12XEP100单片机设计了执行器位置控制器软硬件,结果表明:相对于传统PID,BP-PID具有良好的控制效果,抗干扰性和适应性更好,但同时存在运算量较大的问题。在此基础上将柴油机作为控制对象,利用dSPACE半实物仿真平台与MicroAutoBox进行算法验证,结果表明:该算法满足柴油机转速控制性能指标要求,负载突变时转速超调量小于5%,能有效降低负载变化对转速的影响,可以用于实际柴油机转速控制系统研究开发。
Owing to the non-linear and time-variantion characteristics, marine diesel engine speed control system with traditional PID controller is unsatisfied. A speed controller with self-learning and adap- tive ability was designed based on Back-Propagation （BP） Neural Network to realize on-line optimization of the PID controller parameters. Using PB-PID algorithm and MCgS12XEP100 MCU, the controller was experimentally evaluated in a marine diesel engine rack position actuator. Results demonstrated that the controller possesses better adaptability and anti-jamming capability compared to the tradition PID and PB- PID,but there is also the problem of bigger calculation losd. The algorithm is verified by hardware-in-loop simulation of dSPACE and MicroAutoBox to be able to control the overshoot less than 5 % while load varying suddenly,meeting the diesel engine speed control requirements. The algorithm can be used to research and development of diesel engine speed control system.
出处
《内燃机工程》
EI
CAS
CSCD
北大核心
2013年第4期42-47,共6页
Chinese Internal Combustion Engine Engineering
基金
国家自然科学基金项目(50909024)
中央高校基本科研业务费专项资金资助项目(HEUCF130307)
关键词
内燃机
船用柴油机
BP神经网络
转速控制
位置执行器
IC engine
marine diesel engine
BP neural network
speed control
rack position actuator
