基于卡尔曼滤波器的智能车过弯时间优化控制算法

中国机械工程 ›› 2015, Vol. 26 ›› Issue (19): 2693-2697.

基于卡尔曼滤波器的智能车过弯时间优化控制算法

孙涛1,2;徐正进1,2;尤霖1,2;黄序1;郑松林1,2;张振东1,2;孙跃东1

1.上海理工大学，上海，200093
2.机械工业汽车底盘机械零部件强度与可靠性评价重点实验室，上海，200093

出版日期:2015-10-10 发布日期:2015-10-10
基金资助:
上海市科研创新项目(12ZZ145)

Cornering-time Optimal Algorithm for Intelligent Scaled Vehicle Based on Kalman Filter

Sun Tao1,2;Xu Zhengjin1,2;You Lin1,2;Huang Xu1;Zheng Songlin1,2;Zhang Zhendong1,2;Sun Yuedong1

1.University of Shanghai for Science and Technology，Shanghai，200093
2.Machinery Industry Key Laboratory for Mechanical Strength & Reliability Evaluation of Auto Chassis Components，Shanghai，200093

Online:2015-10-10 Published:2015-10-10

摘要/Abstract

摘要：

对最小过弯时间算法的研究是赛车单圈行驶时间优化的关键问题之一。在最优控制理论的框架下，设计了一种基于Kalman滤波器的过弯时间优化控制算法。运用Kalman滤波算法估计了车辆行驶状态，通过控制算法调节轮胎转角实现了过弯轨迹的优化。最后，根据相似理论Buckingham Pi定理，运用飞思卡尔智能模型车进行试验。结果表明，所设计的优化控制算法可有效缩短车辆过弯时间，验证了优化算法的有效性。

关键词: 过弯时间, 卡尔曼滤波, 动态相似, 最优控制

Abstract:

Vehicle optimal cornering-time is one of the most significant aspects in the field of lap time optimization. An optimal cornering-time algorithm was proposed herein based on the Kalman filter and optimal control theory. The Kalman algorithm was used to estimate the motion state of the vehicle，and the optimal trajectories were computed by the optimal control algorithm. A scaled vehicle with Freescale controller was developed subsequently according to the Buckingham Pi theorem based on dynamics similarity to validate the effectiveness of the control algorithm. The experimental results indicate that the scaled vehicle reduces the vehicle cornering-time effectively without sacrificing the vehicle stability.

Key words: cornering time, Kalman filtering, dynamic similarity, optimal control

中图分类号:

TP391.9

孙涛, 徐正进, 尤霖, 黄序, 郑松林, 张振东, 孙跃东. 基于卡尔曼滤波器的智能车过弯时间优化控制算法[J]. 中国机械工程, 2015, 26(19): 2693-2697.

Sun Tao, Xu Zhengjin, You Lin, Huang Xu, Zheng Songlin, Zhang Zhendong, . Cornering-time Optimal Algorithm for Intelligent Scaled Vehicle Based on Kalman Filter[J]. China Mechanical Engineering, 2015, 26(19): 2693-2697.

参考文献

[1]Milliken W F, Milliken D L. Race Car Vehicle Dynamics[M]. Warrendale：Society of Automotive Engineers, 1995.
[2]Brayshaw D L, Harrison M F. A Quasi Steady State Approach to Race Car Lap Simulation in Order to Understand the Effects of Racing Line and Centre of Gravity Location[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2005, 219(6): 725-739.
[3]Vlkl T, Muehlmeier M, Winner H. Extended Steady State Lap Time Simulation for Analyzing Transient Vehicle Behavior[J].Int. J. Passeng. Cars-Mech. Syst.,2013,6(1):283-292.
[4]Kelly D P. Lap Time Simulation with Transient Vehicle and Tyre Dynamics[D]. Cranfield：Cranfield University, 2008.
[5]Velenis E, Tsiotras P. Minimum Time vs Maximum Exit Velocity Path Optimization during Cornering[C]//2005 IEEE International Symposium on Industrial Electronics. Dubrovnik, Croatia: IEEE, 2005: 355-360.
[6]Timings J P, Cole D J. Minimum Manoeuvre Time of a Nonlinear Vehicle at Constant Forward Speed Using Convex Optimization[C]//Proceedings of the 10th International Symposium on Advanced Vehicle Control. Loughborough, UK, 2010:21-26.
[7]Rucco A, Notarstefano G, Hauser J. Computing Minimum Lap-time Trajectories for a Single-track Car with Load Transfer[C]//2012 IEEE 51st Annual Conference on Decision and Control. Maui, Hawaii, USA: IEEE, 2012: 6321-6326.
[8]Velenis E, Tsiotras P. Optimal Velocity Profile Generation for Given Acceleration Limits: Theoretical Analysis[C]//American Control Conference. Portland, OR, USA, 2005:1478-1483.
[9]Velenis E, Tsiotras P. OptimalVelocity Profile Generation for Given Acceleration Limits; the Half-car Model Case[C]//2005 IEEE International Symposium on Industrial Electronics. Dubrovnik, Croatia: IEEE, 2005: 361-366.
[10]丁海涛, 郭孔辉, 李飞, 等. 基于加速度反馈的任意道路和车速跟随控制驾驶员模型[J]. 机械工程学报, 2010, 46(10): 116-120.
Ding Haitao, Guo Konghui, Li Fei, et al. Arbitrary Path and Speed Following Driver Model Based on Vehicle Acceleration Feedback[J]. Journal of Mechanical Engineering, 2010, 46(10):116-120.

[1]	沈运1;谢荣理2;赵艳娜3;付庄1;王尧1;张俊2;费健2. 基于改进阻抗控制的自适应曲面乳腺超声扫描方法[J]. 中国机械工程, 2021, 32(02): 195-203.
[2]	李阳1,2,3；刘子明1,2,4；陈庆盈1,2,3 . 考虑打滑干扰的解耦式主动脚轮全向移动机器人跟踪控制[J]. 中国机械工程, 2020, 31(18): 2247-2253.
[3]	丁镇涛, 邓涛, 李志飞, 尹燕莉. 基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究[J]. 中国机械工程, 2020, 31(15): 1823-1830.
[4]	臧政1;霍炜1;王玉海2;李兴坤3;郑旭光3;李圆圆4. 重型商用车辆质量估计算法研究[J]. 中国机械工程, 2020, 31(11): 1360-1367.
[5]	刘征宇1，2;黎盼春1;朱诚诚1;尤勇1. 基于组合模型的锂电池参数辨识和电池荷电状态在线联合估计[J]. 中国机械工程, 2020, 31(10): 1162-1168.
[6]	李伟1;刘伟嵬1;邓业林2. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(03): 321-327,343.
[7]	徐亚旋;刘梁;常思勤;胡茂杨. 电磁驱动配气机构的发动机分缸工作一致性控制[J]. 中国机械工程, 2019, 30(21): 2546-2553.
[8]	陈志勇;张婷婷. 柔性基、柔性关节空间机械臂的运动混合控制[J]. 中国机械工程, 2019, 30(12): 1466-1473.
[9]	熊璐1,2;付志强2;李增良2;章仁燮1. 无人车的线控转向系统故障诊断[J]. 中国机械工程, 2017, 28(22): 2689-2694,2700.
[10]	汪, 魏民祥, 赵万忠, 张凤娇, 严明月. 基于递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的车辆状态估计[J]. 中国机械工程, 2017, 28(06): 750-755.
[11]	肖献强, 殷延杰, 王家恩. 基于个体特性的驾驶行为操纵模式建模方法[J]. 中国机械工程, 2016, 27(19): 2681-2686,2692.
[12]	史中权, , 叶文华. 基于音圈电机作动器的主轴振动LQG控制研究[J]. 中国机械工程, 2016, 27(10): 1308-1314.
[13]	张鹏, 王文格, 付霞, 聂挺. 基于闭环自适应辨识的速度环PI参数自整定[J]. 中国机械工程, 2016, 27(05): 639-645.
[14]	朱文超, 许德章. 混沌野草扩展Kalman滤波器辨识六维力传感器下E膜模型参数[J]. 中国机械工程, 2015, 26(7): 878-886.
[15]	许世景1，2;吴志新1，3. 基于PMP的HEV全局最优能量管理策略研究[J]. 中国机械工程, 2014, 25(1): 138-141.