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基于NCD/自适应模糊PID的汽车EPS系统控制特性研究 被引量:4

Study of Control Performance Based on NCD/Fuzzy Self-adaptive PID Controller for Automotive EPS system
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摘要 为提高汽车的转向性能,在分析电动助力转向(EPS)系统结构及动力学特性基础上,构建了系统动力学模型和仿真模型;根据助力原理设计了助力特性曲线,运用非线性控制设计(Nonlinear Control Design,NCD Blockset)对自适应模糊PID控制器初始参数进行了优化,并通过自适应模糊PID控制策略对电动机目标电流进行闭环跟踪控制仿真;通过对比仿真结果中的目标电流响应速度、横摆角速度等转向性能参数表明:该控制策略能够提高电动转向控制系统响应性、跟踪性,提高了汽车的转向性能和操作稳定性。 In order to improve the vehicles steering performance, the Electrical Power Steering (EPS) System simulation is modeling and simulated through analyzing the structure and dynamics performance of EPS system. The assisting characteristics curve is design according to the assisting principle. An integrated optimization for the Fuzzy Self adaptive PID Controller parameters based on NCD blockset was brought forward. PID Fuzzy control strategy was adopted for the closed loop tracking control of objective current in the motor. By com parison the stimulation outputs on steering characteristics such as the response speed of the target current, yaw rate and so on, the simulation result shows that: the designed controller makes the EPS system has a better performance in the trace, it can improve the steering character istics and stability on vehicle.
出处 《计算机测量与控制》 北大核心 2013年第11期2963-2965,3007,共4页 Computer Measurement &Control
基金 汽车运输安全保障技术交通行业重点实验室开放基金(CHD2011SY007) 陕西省教育厅科技项目(12JK0693)
关键词 电动助力转向 模糊PID控制 仿真 NCD优化 electric power steering PID Fuzzy control simulation NCD optimization
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  • 1王元聪,李伟光.汽车电动助力式转向系统(EPS)控制策略研究[J].交通与计算机,2005,23(6):75-78. 被引量:13
  • 2谢刚,殷国富,尹显明,李双跃.基于PID参数模糊自整定控制的电动助力转向系统跟随性研究[J].机床与液压,2007,35(7):15-18. 被引量:8
  • 3Chen X,Yang T B,Chen X Q. A Generic Mode- BasedAdvanced-Control of Electric Power _ Assisted Steering Systems [ J ].IEEETransaction on Control Systems Technology,2008, 16 (6):1298 - 1299.
  • 4刘金琨.先进PID控制MATLAB仿真[M].3版.北京:电子工业出版社,2014.
  • 5Astrom K J, Hagglund T. PID Controller: Theory, Design, and Tunning, 2nd Edition [ M ]. Research Triangle Park. North Carolina: Instrument Society of America, 1995.
  • 6Guo Q z, Kang D L, Yu X D,et al. Binary-coded extremal optimization for the design of PID controllers [ J ]. Neuro- computing, 2014,138 ( 22 ) : 180 - 188.
  • 7Leandro d S C, Marcelo W P. A tuning strategy for muhiva- riable PI and PID controllers using differential evolution combined with chaotic Zaslavskii map [ J ]. Expert Systems with Applications, 2011,38 ( 11 ) : 13694 - 13701.
  • 8A K Qin, V L Huang, P N Suganthan. Differential Evolu- tion Algorithm With Strategy Adaptation for Global Numeri- cal Optimization [ J ]. IEEE Transactions on Evolutionary Computation,2009,13 : 398 - 417.
  • 9K V Price, R M Storn, J A Lampinen. Differential evolution: a practical approach to global optimization[ D]. Berlin :Nat- ural Computing Series, Springer,2005.
  • 10R Storn, K Price. Differential evolution a simple and effi- cient heuristic for global optimization over continuous spaces [ J]. J. Global Optimization, 1997,11:341 - 359.

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