跟踪目标路径的横向位移偏差小于0.2 m; 所提方法有效减小了驾驶机器人车辆转向性能下降造成的影响。
Abstract
To weaken the impact of steering performance degradation during long-term automatic driving
a dynamic steering torque compensation method for driving robot vehicle based on multi-innovation is proposed. The vehicle dynamic model and driving robot vehicle dynamic model are constructed
then an off-line self-learning model for the steering performance of driving robot vehicle is established
which takes the path curvature and the vehicle speed as input and the steering wheel angle as output. And a controlled autoregressive on-line identification model is established
which takes the angular velocity of steering wheel
angular acceleration and wheel angle as input and the driving torque of steering manipulator as output
and the parameters are identified with the forgetting factor multi-innovation least square method
the scalar innovation is extended to vector innovation in the iterative calculation to improve the identification accuracy of steering performance parameters of driving rob
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references
CHEN G, ZHANG W G. Digital prototyping design of electromagnetic unmanned robot applied to automotive test [J]. Robotics and Computer-Integrated Manufacturing, 2015, 32: 54-64.
DUCHOANˇU F, HUBINSKY P, HANZEL J, et al. Intelligent vehicles as the robotic applications [J]. Procedia Engineering, 2012, 48: 105-114.
ALT B, HERMANN E, SVARICEK F. Second order sliding modes control for rope winch based automotive driver robot [J]. International Journal of Vehicle Design, 2013, 62: 147.
CHEN G, ZHANG W G. Hierarchical coordinated control method for unmanned robot applied to automotive test [J]. IEEE Transactions on Industrial Electronics, 2016, 63(2): 1039-1051.
GURNEY K R, ROMERO-LANKAO P, SETO K C, et al. Climate change: track urban emissions on a human scale [J]. Nature, 2015, 525(7568): 179-181.
CHEN G, ZHANG W G. Design of prototype simulation system for driving performance of electromagnetic unmanned robot applied to automotive test [J]. Industrial Robot: An International Journal, 2015, 42(1): 74-82.
LU Wei, CHEN Hao, WANG Ling, et al. Motion analysis of tractor robot driver’s gear shift mechanical arm [J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(1): 37-44.
GUO Yingshi, JIANG Zhengmin, BAI Yan, et al. Investigation of humanoid level of path tracking methods based on autonomous vehicles [J]. China Journal of Highway and Transport, 2018, 31(8): 189-196.
CHOI J, YI K, SUH J, et al. Coordinated control of motor-driven power steering torque overlay and differential braking for emergency driving support [J]. IEEE Transactions on Vehicular Technology, 2014, 63(2): 566-579.
CHEN Gang, ZHANG Weigong, GONG Zongyang, et al. A vehicle performance self learning method applied to robot driver [J]. China Mechanical Engineering, 2010, 21(4): 491-495.
CHEN G, CHEN S B, LANGARI R, et al. Driver-behavior-based adaptive steering robust nonlinear control of unmanned driving robotic vehicle with modeling uncertainties and disturbance observer [J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 8183-8190.
XIONG Lu, FU Zhiqiang, BAI Manfei, et al. A vehicle speed adaptive control method considering acceleration demand [J]. Journal of Xi’an Jiaotong University, 2019, 53(1): 62-69.
DING F. Several multi-innovation identification methods [J]. Digital Signal Processing, 2010, 20(4): 1027-1039.
WAN L J, DING F. Decomposition- and gradient-based iterative identification algorithms for multivariable systems using the multi-innovation theory [J]. Circuits, Systems, and Signal Processing, 2019, 38(7): 2971-2991.
SUO Jianglei, HU Zhijian, LIU Yukai, et al. Power system state space identification based on multi-innovation coupling least square algorithm [J]. Electric Power Automation Equipment, 2015, 35(7): 65-73.
WEI Zhinong, YUAN Kangkang, CHENG Lexiang, et al. Parameter identification of lithium-ion battery based on multi-innovation least squares algorithm [J]. Automation of Electric Power Systems, 2019, 43(15): 139-145.
DING F, LIU P X, LIU G J. Multiinnovation least-squares identification for system modeling [J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, 2010, 40(3): 767-778.