基于预瞄距离的地下矿用铰接车路径跟踪预测控制
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摘要
矿用车辆无人驾驶是实现矿山无人化开采的关键技术,而路径跟踪控制是无人驾驶系统的核心技术之一.路径跟踪控制系统是多变量、多约束系统,采用传统方法在多约束条件下存在执行器饱和等问题.针对上述问题,本文引入模型预测控制方法,通过考虑车辆的姿态与位置之间的关系,以跟踪路径的横向偏差最小化和车辆的航向角偏差最小化为目标对预测控制的目标函数进行优化,以获得车辆速度和铰接角度的最优控制量,实现对多变量、多约束系统的求解.针对模型预测控制算法不能提前判断道路曲率突变而导致跟踪超调的问题,提出基于预瞄距离的控制方法,通过提前判断道路突变信息,提高车辆路径跟踪精确性和稳定性.使用Matlab/Adams仿真软件进行对比仿真试验,结果表明:使用模型预测跟踪控制器能够解决多变量、多约束系统控制问题,有效防止执行器饱和;而使用基于预瞄距离的模型预测跟踪控制器能够使车辆的横向位置偏差保持在±0. 04 m,航向角偏差保持在±1. 8°范围内,相较于改进前的控制器,其横向位置偏差减少了80. 9%,航向角偏差减少了59. 1%,证明改进后的控制器具有更好的横向稳定性和精确性.
Due to the narrow roadway and poor working environment,underground mines pose a threat to the safety of vehicle drivers. The realization of automatic driving of underground mine vehicles can improve mining automation and intelligence and ensure safety of workers,and it can significantly increase mining and exploitation efficiency. Automatic driving of underground mining vehicles requires the technologies of location,communication,navigation,and path following control. Automatic driving of mining vehicles is the ultimate approach of autonomous navigation and auto driving,while path following control system is one of the core technologies of the autopilot system. The path following control system is a multi-variable,multi-constraint system. There are optimization problems under multiple constraints as well as challenges such as actuator saturation during the control process. To solve the above problems,a model predictive control method was introduced in this paper. By considering the relationship between the position and situation of the vehicle,the objective function of the predictive control was optimized by minimizing the lateral deviation of the following path and the heading angle deviation of the vehicle. Therefore,the optimal controls of vehicle speed and articulation angle were obtained,and the problem of multi-variable and multi-constraint system was solved. For the tracking overshoot problem caused by the inability of determining sudden changes of road curvature in the model predictive control strategy,a control method based on preview distance was proposed; thus,the vehicle path following control accuracy and stability was improved through the advance judgment of road mutation information. Matlab/Adams simulation software was used to perform a comparison simulation test. The results show that the model predictive following controller is capable of solving the control problem in multi-variable,multi-constrained system and effectively prevent the actuator saturation. Moreover,the model predictive following control strategy based on the preview distance keeps the horizontal deviation of the vehicle within ± 0. 04 m and the heading angle deviation within ± 1. 8°. Compared with the controller before improvement,the lateral position deviation is reduced by 80. 9%,and the heading angle deviation is reduced by 59. 1%; this proves that the improved controller has better lateral stability and accuracy.
Due to the narrow roadway and poor working environment,underground mines pose a threat to the safety of vehicle drivers. The realization of automatic driving of underground mine vehicles can improve mining automation and intelligence and ensure safety of workers,and it can significantly increase mining and exploitation efficiency. Automatic driving of underground mining vehicles requires the technologies of location,communication,navigation,and path following control. Automatic driving of mining vehicles is the ultimate approach of autonomous navigation and auto driving,while path following control system is one of the core technologies of the autopilot system. The path following control system is a multi-variable,multi-constraint system. There are optimization problems under multiple constraints as well as challenges such as actuator saturation during the control process. To solve the above problems,a model predictive control method was introduced in this paper. By considering the relationship between the position and situation of the vehicle,the objective function of the predictive control was optimized by minimizing the lateral deviation of the following path and the heading angle deviation of the vehicle. Therefore,the optimal controls of vehicle speed and articulation angle were obtained,and the problem of multi-variable and multi-constraint system was solved. For the tracking overshoot problem caused by the inability of determining sudden changes of road curvature in the model predictive control strategy,a control method based on preview distance was proposed; thus,the vehicle path following control accuracy and stability was improved through the advance judgment of road mutation information. Matlab/Adams simulation software was used to perform a comparison simulation test. The results show that the model predictive following controller is capable of solving the control problem in multi-variable,multi-constrained system and effectively prevent the actuator saturation. Moreover,the model predictive following control strategy based on the preview distance keeps the horizontal deviation of the vehicle within ± 0. 04 m and the heading angle deviation within ± 1. 8°. Compared with the controller before improvement,the lateral position deviation is reduced by 80. 9%,and the heading angle deviation is reduced by 59. 1%; this proves that the improved controller has better lateral stability and accuracy.
引文
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[8] Kapania N R,Gerdes J C. Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling. Vehicle Syst Dyn,2015,53(12):1687
[9] Aslam J,Qin S Y,Alvi M A. Fuzzy sliding mode control algorithm for a four-wheel skid steer vehicle. J Mech Sci Technol,2014,28(8):3301
[10] Zhang L W,Xie W,Wang J C. Robust MPC for linear systems with structured time-varying uncertainties and saturating actuator.Asian J Control,2017,19(3):1197
[11] Di Cairano S,Bemporad A. Model predictive control tuning by controller matching. IEEE Trans Autom Control,2010,55(1):185
[12] Xi Y G,Li D W,Lin S. Model predictive control———status and challenges. Acta Autom Sin,2013,39(3):222(席裕庚,李德伟,林姝.模型预测控制———现状与挑战.自动化学报,2013,39(3):222)
[13] Yu R,Guo H Y,Sun Z P,et al. MPC-based regional path tracking controller design for autonomous ground vehicles//2015IEEE International Conference on Systems,Man,and Cybernetics(SMC). Hongkon,2015:2510
[14] Falcone P,Eric Tseng H,Borrelli F,et al. MPC-based yaw and lateral stabilisation via active front steering and braking. Vehicle Syst Dyn,2008,46(Suppl 1):611
[15] Nayl T,Nikolakopoulos G,Gustafsson T. A full error dynamics switching modeling and control scheme for an articulated vehicle.Int J Control Autom Syst,2015,13(5):1221
[16] Gong J W,Xu W,Jiang Y,et al. Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking.J Beijing Inst Technol,2015,24(4):441
[17] Polotski V,Hemami A. Control of articulated vehicle for mining applications:modeling and laboratory experiments//Proceedings of the 1997 IEEE International Conference on Control Applications. Hartford,1997:318
[18] Ridley P,Corke P. Load haul dump vehicle kinematics and control. J Dyn Syst Meas Control,2003,125(1):54
[19] Wang J M,Steiber J,Surampudi B. Autonomous ground vehicle control system for high-speed and safe operation//Proceedings of the American Control Conference(2008). Seattle,2008:218
[20] Zhao X,Yang J,Zhang W M,et al. Sliding mode control algorithm for path tracking of articulated dump truck. Trans Chin Soc Agric Eng,2015,31(10):198(赵翾,杨珏,张文明,等.农用轮式铰接车辆滑模轨迹跟踪控制算法.农业工程学报,2015,31(10):198)
[21] Li S B,Wang J Q,Li K Q. Stabilization of linear predictive control systems with softening constraints. J Tsinghua Univ Sci Technol,2010,50(11):1848(李升波,王建强,李克强.软约束线性模型预测控制系统的稳定性方法.清华大学学报(自然科学版),2010,50(11):1848)
[22] Dou F Q. Research on Path Tracking and Obstacles Avoidance for Autonomous Underground Mining Articulated Vehicles[Dissertation]. Beijing:University of Science and Technology Beijing,2018(窦凤谦.地下矿用铰接车路径跟踪与智能避障控制研究[学位论文].北京:北京科技大学,2018)
[23] Liu R,Duan J M. A path tracking algorithm of intelligent vehicle by preview strategy//Proceedings of the 32nd Chinese Control Conference. Xi'an,2013:5630
[24] Bruschetta M,Cenedese C,Beghi A,et al. A motion cueing algorithm with look-ahead and driver characterization:application to vertical car dynamics. IEEE Trans Human-Mach Syst,2018,48(1):6
[25] Chan W W,Li J,Wang T B,et al. Preview control for road following of vision guided intelligent vehicle. Chin J Mech Eng,2008,44(10):277(陈无畏,李进,王檀彬,等.视觉导航智能车辆的路径跟踪预瞄控制.机械工程学报,2008,44(10):277)
[26] Lin F,Ni L Q,Zhao Y Q,et al. Path following control of intelligent vehicles considering lateral stability. J S China Univ Technol Nat Sci Ed,2018,46(1):78(林棻,倪兰青,赵又群,等.考虑横向稳定性的智能车辆路径跟踪控制.华南理工大学学报(自然科学版),2018,46(1):78)
[27] Nayl T,Nikolakopoulos G,Gustafsson T. Path following for an articulated vehicle based on switching model predictive control under varying speeds and slip angles//Proceedings of 2012IEEE 17th International Conference on Emerging Technologies&Factory Automation(ETFA 2012). Krakow,2012:1
[28] Jin L S,Gao L L,Xie X Y,et al. Fuzzy-optimal control of fourwheel independent steering vehicles. J Southwest Jiaotong Univ,2016,51(6):1064(金立生,高琳琳,谢宪毅,等.四轮独立转向车辆稳定性的模糊最优控制方法.西南交通大学学报,2016,51(6):1064)
[2] Zhan K,Gu H S,Zhou J W,et al. Remote controlling and precision positioning technologies of underground remote-controlled scrapers. Nonferrous Met,2009,61(1):107(战凯,顾洪枢,周俊武,等.地下遥控铲运机遥控技术和精确定位技术研究.有色金属,2009,61(1):107)
[3] Dong J J,Zhang W M,Shi B Q. General of underground mining articulated dump truck and market in China. Coal Mine Machinery,2007,28(12):1(董建军,张文明,石博强.地下矿用铰接式自卸汽车的概况及中国市场分析.煤矿机械,2007,28(12):1)
[4] Yakub F,Mori Y. Comparative study of autonomous path-following vehicle control via model predictive control and linear quadratic control. Proc Inst Mech Eng Part D J Automobile Eng,2015,229(12):1695
[5] Liu C,Zou Z J,Li T S. Path following of underactuated surface vessels with fin roll reduction based on neural network and hierarchical sliding mode technique. Neural Comput Appl,2015,26(7):1525
[6] Abatari H T,Tafti A D. Using a fuzzy PID controller for the path following of a car-like mobile robot//2013 First RSI/ISM International Conference on Robotics and Mechatronics. Tehran,2013:189
[7] Kritayakirana K,Gerdes J C. Using the centre of percussion to design a steering controller for an autonomous race car. Vehicle Syst Dyn,2012,50(Suppl 1):33
[8] Kapania N R,Gerdes J C. Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling. Vehicle Syst Dyn,2015,53(12):1687
[9] Aslam J,Qin S Y,Alvi M A. Fuzzy sliding mode control algorithm for a four-wheel skid steer vehicle. J Mech Sci Technol,2014,28(8):3301
[10] Zhang L W,Xie W,Wang J C. Robust MPC for linear systems with structured time-varying uncertainties and saturating actuator.Asian J Control,2017,19(3):1197
[11] Di Cairano S,Bemporad A. Model predictive control tuning by controller matching. IEEE Trans Autom Control,2010,55(1):185
[12] Xi Y G,Li D W,Lin S. Model predictive control———status and challenges. Acta Autom Sin,2013,39(3):222(席裕庚,李德伟,林姝.模型预测控制———现状与挑战.自动化学报,2013,39(3):222)
[13] Yu R,Guo H Y,Sun Z P,et al. MPC-based regional path tracking controller design for autonomous ground vehicles//2015IEEE International Conference on Systems,Man,and Cybernetics(SMC). Hongkon,2015:2510
[14] Falcone P,Eric Tseng H,Borrelli F,et al. MPC-based yaw and lateral stabilisation via active front steering and braking. Vehicle Syst Dyn,2008,46(Suppl 1):611
[15] Nayl T,Nikolakopoulos G,Gustafsson T. A full error dynamics switching modeling and control scheme for an articulated vehicle.Int J Control Autom Syst,2015,13(5):1221
[16] Gong J W,Xu W,Jiang Y,et al. Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking.J Beijing Inst Technol,2015,24(4):441
[17] Polotski V,Hemami A. Control of articulated vehicle for mining applications:modeling and laboratory experiments//Proceedings of the 1997 IEEE International Conference on Control Applications. Hartford,1997:318
[18] Ridley P,Corke P. Load haul dump vehicle kinematics and control. J Dyn Syst Meas Control,2003,125(1):54
[19] Wang J M,Steiber J,Surampudi B. Autonomous ground vehicle control system for high-speed and safe operation//Proceedings of the American Control Conference(2008). Seattle,2008:218
[20] Zhao X,Yang J,Zhang W M,et al. Sliding mode control algorithm for path tracking of articulated dump truck. Trans Chin Soc Agric Eng,2015,31(10):198(赵翾,杨珏,张文明,等.农用轮式铰接车辆滑模轨迹跟踪控制算法.农业工程学报,2015,31(10):198)
[21] Li S B,Wang J Q,Li K Q. Stabilization of linear predictive control systems with softening constraints. J Tsinghua Univ Sci Technol,2010,50(11):1848(李升波,王建强,李克强.软约束线性模型预测控制系统的稳定性方法.清华大学学报(自然科学版),2010,50(11):1848)
[22] Dou F Q. Research on Path Tracking and Obstacles Avoidance for Autonomous Underground Mining Articulated Vehicles[Dissertation]. Beijing:University of Science and Technology Beijing,2018(窦凤谦.地下矿用铰接车路径跟踪与智能避障控制研究[学位论文].北京:北京科技大学,2018)
[23] Liu R,Duan J M. A path tracking algorithm of intelligent vehicle by preview strategy//Proceedings of the 32nd Chinese Control Conference. Xi'an,2013:5630
[24] Bruschetta M,Cenedese C,Beghi A,et al. A motion cueing algorithm with look-ahead and driver characterization:application to vertical car dynamics. IEEE Trans Human-Mach Syst,2018,48(1):6
[25] Chan W W,Li J,Wang T B,et al. Preview control for road following of vision guided intelligent vehicle. Chin J Mech Eng,2008,44(10):277(陈无畏,李进,王檀彬,等.视觉导航智能车辆的路径跟踪预瞄控制.机械工程学报,2008,44(10):277)
[26] Lin F,Ni L Q,Zhao Y Q,et al. Path following control of intelligent vehicles considering lateral stability. J S China Univ Technol Nat Sci Ed,2018,46(1):78(林棻,倪兰青,赵又群,等.考虑横向稳定性的智能车辆路径跟踪控制.华南理工大学学报(自然科学版),2018,46(1):78)
[27] Nayl T,Nikolakopoulos G,Gustafsson T. Path following for an articulated vehicle based on switching model predictive control under varying speeds and slip angles//Proceedings of 2012IEEE 17th International Conference on Emerging Technologies&Factory Automation(ETFA 2012). Krakow,2012:1
[28] Jin L S,Gao L L,Xie X Y,et al. Fuzzy-optimal control of fourwheel independent steering vehicles. J Southwest Jiaotong Univ,2016,51(6):1064(金立生,高琳琳,谢宪毅,等.四轮独立转向车辆稳定性的模糊最优控制方法.西南交通大学学报,2016,51(6):1064)