Path Tracking for Different Wheelbase Vehicles

Abstract

Abstract:

Abstract： In order to improve the path tracking robustness for different wheelbase vehicles, this paper proposed a path tracking algorithm for parallel parking based on sliding mode active disturbance rejection control (SMADRC). The linear sliding mode control was used in the algorithm to design the error feedback in ADRC law, which improved the structure of ADRC. The algorithm can be applied to different wheelbase vehicles. The linear active disturbance rejection controller can observe and estimate the external disturbance and uncertainty caused by different wheelbase vehicles. With the assumption of operating conditions such as a low speed and small slip angle, a nonlinear kinematic model with kinematic constraints was used for the design of the controller. Simulation results show that the path tracking algorithm based on sliding mode active disturbance rejection control can observe and compensate the uncertainty, which can guarantee different wheelbase vehicles to track the ideal parking trajectory very well.

Key words: path tracking, sliding mode active disturbance rejection control, parallel parking, uncertainty

CLC Number:

U 471.15

ZHAO Youqun1,WANG Jian1,ZANG Liguo1,LI Bo1,WU Yue2. Path Tracking for Different Wheelbase Vehicles[J]. Journal of Shanghai Jiaotong University, 2015, 49(04): 481-486.

References

［1］Huang S J, LIN G Y. Parallel autoparking of a model vehicle using a selforganizing fuzzy controller ［J］. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2010, 224(8): 9971012.

［2］Mukherjee R, Anderson A. A surface integral approach to the motion planning of nonholonomic systems ［J］. Journal of Dynamic Systems, Measurement, and Control, 1994, 116(3): 315325.

［3］Laumont J P, Jacobs P E, Taix M, et al. A motion planner for nonholonomic mobile robots ［J］. IEEE Transactions on Robotics and Automation, 1994, 10(5): 577593.

［4］Jiang K, Seneviratne L D. A sensor guided autonomous parking system for nonholonomic mobile robots ［J］. Proceedings in IEEE International Conference on Robotics and Automation, 1999, 1:311316.

［5］吴冰，钱立军，虞明，等. 基于RBF神经网络的自动泊车路径规划［J］. 合肥工业大学学报：自然科学版，2012, 35(4): 459463.

WU Bing, QIAN Lijun, YU Ming, et al. Path planning of automatic parallel parking based on RBF neural network［J］. Journal of Hefei University of Technology, 2012, 35(4):459463.

［6］Fraichard T, Scheuer A. From reeds and shepp’s to continuouscurvature paths ［J］. IEEE Transactions on Robotics, 2004, 20(6): 10251035.

［7］Wang Y J, Cartmell M P. Autonomous vehicle parallel parking design using function fitting approaches ［J］. Robotica, 1998,16(2):159170.

［8］GómezBravo F, Cuesta F, Ollero A, et al. Continuous curvature path generation based on β spline curves for parking manoeuvres ［J］. Robotics and Autonomous Systems, 2008, 56(4): 360372.

［9］Zhao Y N, Collins E G. Robust automatic parallel parking in tight spaces via fuzzy logic ［J］. Robotics and Autonomous Systems, 2005, 51(2):111127.

［10］Ollero A, GarcíaCerezo A, Martínez J L, et al. Fuzzy tracking methods for mobile robots ［J］. Applications of Fuzzy Logic: Towards High Machine Intelligence Quotient Systems, 1997, 9:347364.

［11］Demirli K, Khoshnejad M. Autonomous parallel parking of a carlike mobile robot by a neuro fuzzy sensor based controller ［J］. Fuzzy Sets and Systems, 2009, 160(19):28762891.

［12］Lian K Y, Chiu C S, Chiang T S. Parallel parking a carlike robot using fuzzy gain scheduling ［C］∥Proceedings of the 1999 IEEE International Conference on Control Applications. Hawai, USA:IEEE,1999: 16861691.

［13］Gao Z Q. Scaling and bandwidthparameterization based controller tuning［C］∥Proceedings of the American Control Conference. Denver, Colorado, USA: IEEE, 2003:49894996.

［14］韩京清.从PID技术到自抗扰控制技术［J］. 控制工程，2002, 9(3):1318.

HAN Jingqing. From PID technique to active disturbances rejection control technique［J］. Control Engineering of China, 2002, 9(3):1318.

［15］陈增强，孙明玮，杨瑞光. 线性自抗扰控制器的稳定性研究［J］.自动化学报，2013，39(5)：574580.

CHEN Zengqiang, SUN Mingwei, YANG Ruiguang. Research on the stability of linear active disturbance rejection control ［J］. Acta Automatica Sinica, 2013, 39(5): 574580.

[1]	WU Jing, LIU Xuanyu, LI Xiang, QI Xiaoyan, LI Chengjun, ZHANG Zhong. Impact Mechanism Analysis of Carbon Price Uncertainty on Power System Dispatch [J]. Journal of Shanghai Jiao Tong University, 2025, 59(3): 354-364.
[2]	LI Jianlin, ZHANG Zedong, LIANG Ce, ZENG Fei. Multi-Objective Robustness of Integrated Energy System Considering Source-Load Uncertainty [J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 175-185.
[3]	TANG Jianguo1, 2, MAO Jinghang1, 2, LIU Mingyue1, 2. Design and Hierarchical Sliding Mode Path Tracking of Deep-Sea Lander with Rudder [J]. Ocean Engineering Equipment and Technology, 2025, 12(1): 106-118.
[4]	MI Yang, FU Qixin, ZHAO Haihui, MA Siyuan, WANG Yufei. Robust Optimal Scheduling of Micro Energy Grid Considering Multi-Interval Uncertainty Set of Source-Load and Integrated Demand Response [J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1323-1333.
[5]	YANG Sen, GUO Ning, ZHANG Shouming. Robust Optimal Scheduling of Agricultural Microgrid Combined with Irrigation System Under Uncertainty Conditions [J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1432-1442.
[6]	TIAN Shuxin, HAN Xue, FU Yang, SU Xiangjing, LI Zhenkun. Two-Stage Robust Expansion Planning of Transmission Network Considering Uncertainty of Offshore Wind Power [J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1400-1409.
[7]	LIU Yuesheng (刘月笙), HE Ning^∗(贺宁), HE Lile (贺利乐), ZHANG Yiwen (张译文), XI Kun (习坤), ZHANG Mengrui (张梦芮). Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1028-1036.
[8]	ZHANG Weihua, WANG Donghui, ZHOU Zhitan, LI Yiqing. Research on Digital Prototype Technology of Solid Rocket Divert and Attitude Control Motor [J]. Air & Space Defense, 2024, 7(5): 18-27.
[9]	LIU Huahua, WANG Qing. Multi-Aircraft Target Assignment Method Based on Reinforcement Learning [J]. Air & Space Defense, 2024, 7(5): 65-72.
[10]	ZHANG Xianwen, YIN Gaowen, SHEN Feifan, HUANG Sheng, WEI Juan. Bidding Strategies for Energy Storage Participation in Electricity Market Considering Uncertainty of Wind Power and Carbon Trading [J]. Journal of Shanghai Jiao Tong University, 2024, 58(12): 1868-1880.
[11]	WANG Xianglei, LIU Xiuquan, LIU Zhaowei, CHANG Yuanjiang, CHEN Guoming. Intelligent Control for Deepwater Drilling Riser Disconnection and Recoil Considering System Uncertainty [J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1698-1706.
[12]	WEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun. Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network [J]. Journal of Shanghai Jiao Tong University, 2023, 57(S1): 37-45.
[13]	CHEN Yi, WANG Han, XU Xiaoyuan, HU Youlin, YAN Zheng, ZENG Dan, FENG Kai. A Two-Stage Distributionally Robust Economic Dispatch Model Under the Coordination of Inter-Provincial and Intra-Provincial Bi-Level Market [J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1114-1125.
[14]	WANG Jing, XING Haijun, WANG Huaxin, PENG Sijia. Optimal Scheduling of Integrated Energy System Considering Integration of Electric Vehicles and Load Aggregators [J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 814-823.
[15]	HU Bo, CHENG Xin, SHAO Changzheng, HUANG Wei, SUN Yue, XIE Kaigui. Optimal Dispatch of Integrated Energy System Based on Flexibility of Thermal Load [J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 803-813.