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基于分数阶PID理论的汽车线控转向的主动控制 被引量:6

Active control of steering-by-wire of vehicle based on fractional-order PID theory
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摘要 为实现更好的车辆稳定性,提出一种基于分数阶比例积分微分(PID)理论的线控转向(SBW)主动控制方法。建立了SBW系统Simulink动力学模型,设计了定横摆角速度增益的理想变传动比曲线;基于横摆角速度反馈的状态跟踪校正控制策略,提出该算法;利用Oustaloup滤波器和粒子群优化算法,进行了有理化和参数整定处理。在高速双移线和鱼钩工况下,进行了离线Carsim/Simulink联合仿真分析。结果表明:相比于传统PID控制和模糊控制,该控制方法的横摆角速度降低了4.7%和9%,侧向加速度降低了6.3%和7.7%,质心侧偏角降低了9.8%和19.5%;因而,该控制器具有较好的SBW主动控制性能。 An active control method of steering-by-wire(SBW) was proposed based on a fractional-order proportional-integral-differential(PID) theory to have a better vehicle running stability. The authors stablished a Simulink dynamic model of the SBW system, and designed a control strategy of state-tracking-correction with yaw-rate-feedback using an ideal variable transmission ratio curve with constant yaw rate gain. Some rationalization and parameter tuning were processed out by using an Oustaloup filter and a particle swarm optimization algorithm. The automobile SBW with a fractional-order PID controller was analyzed by an off-line simulation using a Carsim/Simulink joint simulation under the conditions of high-speed double-shift line and the conditions of fish hook. The results show that the fractional order PID controller reduces the yaw angular velocity by 4.7% and 9%, reduces the lateral acceleration by 6.3% and 7.7%, and reduces the sideslip angle of center of mass by 9.8% and 19.5%, compared with the traditional PID control and with the fuzzy control. Therefore, the controller has better SBW active control performance.
作者 赵树恩 刘秋杨 ZHAO Shuen;LIU Qiuyang(School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
出处 《汽车安全与节能学报》 CAS CSCD 2019年第2期161-168,共8页 Journal of Automotive Safety and Energy
基金 国家重点研发计划项目(2016YFB0100905) 重庆市基础研究与前沿探索项目(cstc2018jcyjAX0422)
关键词 汽车行驶稳定性 主动控制 线控转向(SBW) 变传动比 比例积分微分(PID) 分数阶PID 粒子群算法 vehicle driving stability active-control steer-by-wire(SBW) variable ratio proportion integration differentiation(PID) fractional-order PID particle swarm optimization
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