Sliding mode periodic adaptive learning control method for medium-speed maglev trains
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摘要: 为了提高中速磁悬浮列车的运行控制性能，考虑到中速磁悬浮列车沿固定线路往返运行时的周期特性，提出了一种基于滑模周期自适应学习的中速磁悬浮列车运行控制算法，对应的滑模周期自适应控制器由等效比例积分微分(PID)和速度前馈控制部分、磁阻力与空气阻力补偿部分以及坡道阻力滑模周期自适应补偿部分组成；采用粒子群优化算法辨识了运行控制器的参数，采用滑模周期自适应学习控制器学习上一周期内的列车运行信息，实时估计并补偿列车运行过程中的坡道阻力，消除坡道阻力对列车运行性能的影响；利用全长5 076 m的中速磁悬浮列车半实物仿真试验线进行了数值仿真，并将设计的滑模周期自适应控制器与PID控制器进行仿真对比。仿真结果显示：滑模周期自适应控制器和PID控制器作用下的列车最大位置跟踪误差分别为0.004和0.007 m，最大速度跟踪误差分别为0.007和0.036 m·s-1；经过4个迭代周期后，滑模周期自适应控制器已准确地估计了给定的坡道阻力；在控制系统受到扰动的情况下，滑模周期自适应控制器与PID控制器作用下的位置与速度跟踪曲线均有波动，相比于PID控制器，滑模周期自适应控制器控制下的跟踪曲线波动更小。可见，相比于传统的PID控制算法，提出的滑模周期自适应学习控制方法可以提高中速磁悬浮列车的运行控制性能。Abstract: In order to improve the operation control performance of the medium-speed maglev train, the periodic characteristic of the train running along a fixed line was considered, and an operation control algorithm based on the sliding mode periodic adaptive learning control (SMPALC) method for the train was proposed. The corresponding sliding mode periodic adaptive controller was composed of the equivalent proportional-integral-differential (PID) and speed feedforward control part, the magnetic resistance and air resistance compensation part, and the ramp resistance sliding mode periodic adaptive compensation part. The operation controller parameters were tuned through the particle swarm optimization (PSO) algorithm. The sliding mode periodic adaptive controller was used to learn the train operation information in the previous period, the ramp resistance during train operation in real time was estimated and compensated, and the influence of ramp resistance on the train operation performance was eliminated. The semi-physical simulation test line of a medium-speed maglev train with a total route length of 5 076 m was numerically simulated, and the designed sliding mode periodic adaptive controller was compared with the PID controller through the simulation. Simulation results show that the maximum position tracking errors under the sliding mode periodic adaptive controller and PID controller are 0.004 and 0.007 m, respectively, and the maximum speed tracking errors are 0.007 and 0.036 m·s-1, respectively. After four iterative periods, the sliding mode periodic adaptive controller has accurately estimated the given ramp resistance. When the control system is disturbed, the position and speed tracking curves under the sliding mode periodic adaptive controller and PID controller both fluctuate. However, compared with those under the PID controller, the tracking curves under the sliding mode periodic adaptive controller have a smaller fluctuation. Therefore, the proposed SMPALC method can improve the operation control performance of medium-speed maglev trains.
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图 4 PID与SMPALC控制器速度跟踪曲线
Figure 4. Speed tracking curves with PID and SMPALC controllers
图 5 PID与SMPALC控制器速度跟踪误差
Figure 5. Speed tracking errors with PID and SMPALC controllers
图 6 PID与SMPALC控制器位置跟踪误差
Figure 6. Position tracking errors with PID and SMPALC controllers
图 7 SMPALC控制器估计的坡道阻力与实际的坡道阻力
Figure 7. Estimated ramp resistances with SMPALC controller and actual ramp resistances
图 8 SMPALC控制器坡道阻力估计误差
Figure 8. Ramp resistance estimation errors with SMPALC controller
图 9 噪声干扰下PID与SMPALC控制器位置跟踪误差
Figure 9. Position tracking errors with PID and SMPALC controllers under noise interference
图 10 噪声干扰下PID与SMPALC控制器速度跟踪误差
Figure 10. Speed tracking errors with PID and SMPALC controllers under noise interference
图 11 SMPALC控制器滑模面变化曲线
Figure 11. Variation curve of sliding mode surface with SMPALC controller
图 12 SMPALC控制器滑模面导数变化曲线
Figure 12. Variation curve of derivative of sliding mode surface with SMPALC controller
表 1 中速磁悬浮列车基本参数
Table 1. Basic parameters of medium-speed maglev train
参数 数值 列车编组数 2 单节车厢满载总质量/kg 23 000 列车质量/kg 46 000 最大速度/(km·h-1) 194.4 最大加速度/(m·s-2) 0.9 
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表 2 试验线基本参数
Table 2. Basic parameters of test line
参数 数值 线路长度/m 5 076 坡道长度/m 500 坡道坡度千分数系数/‰ 15 弯道长度/m 400 弯道曲线半径/m 2 000
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表 3 PSO算法参数
Table 3. Parameters of PSO algorithm
参数 数值 群体规模 20 惯性权重ω 0.5 加速常数c1 2 加速常数c2 2 迭代次数 60
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表 4 SMPALC控制器参数
Table 4. Parameters of SMPALC controller
参数 数值 ε1 0.000 1 ε2 380.1 Ki 8.13×109 K 3.459×10-5
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[1] LEE H W, KIM K C, LEE J. Review of maglev train technologies[J]. IEEE Transactions on Magnetics, 2006, 42(7): 1917-1925. doi: 10.1109/TMAG.2006.875842 [2] 熊嘉阳, 邓自刚. 高速磁悬浮轨道交通研究进展[J]. 交通运输工程学报, 2021, 21(1): 177-198. doi: 10.19818/j.cnki.1671-1637.2021.01.008XIONG Jia-yang, DENG Zi-gang. Research progress of high-speed maglev transit[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 177-198. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.01.008 [3] 马卫华, 罗世辉, 张敏, 等. 中低速磁悬浮车辆研究综述[J]. 交通运输工程学报, 2021, 21(1): 199-216. doi: 10.19818/j.cnki.1671-1637.2021.01.009MA Wei-hua, LUO Shi-hui, ZHANG Min, et al. Research review on medium and low speed maglev vehicle[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 199-216. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.01.009 [4] ZHU Li, HE Ying, YU F R, et al. Communication-based train control system performance optimization using deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 10705-10717. doi: 10.1109/TVT.2017.2724060 [5] 余进, 何正友, 钱清泉, 等. 列车运行过程的自适应模糊控制[J]. 铁道学报, 2010, 32(4): 44-49. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201004009.htmYU Jin, HE Zheng-you, QIAN Qing-quan, et al. Adaptive fuzzy control of train operation[J]. Journal of the China Railway Society, 2010, 32(4): 44-49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201004009.htm [6] YU Qiong-xia, HOU Zhong-sheng, XU Jian-xin. D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains[J]. IEEE Transactions on Control Systems Technology, 2018, 26(2): 652-663. doi: 10.1109/TCST.2017.2692730 [7] 何之煜, 杨志杰, 吕旌阳. 基于自适应模糊滑模的列车精确停车制动控制算法[J]. 中国铁道科学, 2019, 40(2): 122-129. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201902017.htmHE Zhi-yu, YANG Zhi-jie, LYU Jing-yang. Braking control algorithm for accurate train stopping based on adaptive fuzzy sliding mode [J]. China Railway Science, 2019, 40(2): 122-129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201902017.htm [8] 杨艳飞, 崔科, 吕新军. 列车自动驾驶系统的滑模PID组合控制[J]. 铁道学报, 2014, 36(6): 61-67. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201406014.htmYANG Yan-fei, CUI Ke, LYU Xin-jun. Combined sliding mode and PID control of automatic train operation system[J]. Journal of the China Railway Society, 2014, 36(6): 61-67. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201406014.htm [9] 武妍, 施鸿宝. 基于神经网络的地铁列车运行过程的集成型智能控制[J]. 铁道学报, 2000, 22(3): 10-15. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB200003003.htmWU Yan, SHI Hong-bao. Automatic subway train operation control based on neural network and other intelligent control methods[J]. Journal of the China Railway Society, 2000, 22(3): 10-15. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB200003003.htm [10] 李中奇, 许健. 基于改进模糊PID-Smith控制器的高速动车组停车方法[J]. 交通运输工程学报, 2020, 20(4): 145-154. doi: 10.19818/j.cnki.1671-1637.2020.04.011LI Zhong-qi, XU Jian. High-speed EMU parking method based on improved fuzzy PID-Smith controller[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 145-154. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.04.011 [11] 董海荣, 高冰, 宁滨, 等. 基于模糊PID软切换控制的列车自动驾驶系统调速制动[J]. 控制与决策, 2010, 25(5): 794-796, 800. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201005033.htmDONG Hai-rong, GAO Bing, NING Bin, et al. Fuzzy-PID soft switching speed control of automatic train operation system[J]. Control and Decision, 2010, 25(5): 794-796, 800. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201005033.htm [12] 朱进权, 葛琼璇, 张波, 等. 考虑悬浮系统影响的高速磁悬浮列车牵引控制策略[J]. 电工技术学报, 2022, 37(12): 3087-3096. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS202212016.htmZHU Jin-quan, GE Qiong-xuan, ZHANG Bo, et al. Traction control strategy of high-speed maglev considering the influence of suspension system[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3087-3096. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS202212016.htm [13] 纪后继, 刘耀宗, 谢新立. 电机中置式中速磁悬浮列车单悬浮架动力学建模及特性研究[J]. 铁道学报, 2020, 42(9): 33-38. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202009005.htmJI Hou-ji, LIU Yao-zong, XIE Xin-li. Dynamics modeling and analysis of bogie of medium-speed maglev trains with mid-mounted linear motor[J]. Journal of the China Railway Society, 2020, 42(9): 33-38. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202009005.htm [14] NI Fei, MU Si-yuan, KANG Jin-song, et al. Robust controller design for maglev suspension systems based on improved suspension force model[J]. IEEE Transactions on Transportation Electrification, 2021, 7(3): 1765-1779. [15] JEONG J H, HA C W, LIM J, et al. Analysis and control of electromagnetic coupling effect of levitation and guidance systems for semi-high-speed maglev train considering current direction[J]. IEEE Transactions on Magnetics, 2017, 53(6): 1-4. [16] SUN You-gang, XU Jun-qi, QIANG Hai-yan, et al. Adaptive neural-fuzzy robust position control scheme for maglev train systems with experimental verification[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8589-8599. [17] ZHANG Jin-hui, CHEN Duan-duan, SHEN Gang-hui, et al. Disturbance observer based adaptive fuzzy sliding mode control: a dynamic sliding surface approach[J]. Automatica, 2021, 129: 109606. [18] INCREMONA G P, RUBAGOTTI M, FERRARA A. Sliding mode control of constrained nonlinear systems[J]. IEEE Transactions on Automatic Control, 2017, 62(6): 2965-2972. [19] WANG Xi, ZHU Li, WANG Hong-wei, et al. Robust distributed cruise control of multiple high-speed trains based on disturbance observer[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 267-279. [20] WANG Xi, LI Shu-kai, SU Shuai, et al. Robust fuzzy predictive control for automatic train regulation in high-frequency metro lines[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(6): 1295-1308. [21] 李德仓, 孟建军, 胥如迅, 等. 强风下高速列车滑膜自适应鲁棒H∞控制方法[J]. 铁道学报, 2018, 40(7): 67-73. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201807012.htmLI De-cang, MENG Jian-jun, XU Ru-xun, et al. Sliding mode adaptive robust H∞ control method for high-speed train under strong wind conditions[J]. Journal of the China Railway Society, 2018, 40(7): 67-73. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201807012.htm [22] LI Ping, ZHU Guo-li. Robust internal model control of servo motor based on sliding mode control approach[J]. ISA Transactions, 2019, 93: 199-208. [23] CHEN S Y, GONG S S. Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control[J]. Mechanical Systems and Signal Processing, 2017, 94: 111-128. [24] 金鸿雁, 赵希梅. 基于互补滑模控制和迭代学习控制的永磁直线同步电动机速度控制[J]. 控制理论与应用, 2020, 37(4): 918-924. https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY202004026.htmJIN Hong-yan, ZHAO Xi-mei. Speed control of permanent magnet linear synchronous motor based on complementary sliding mode control and iterative learning control[J]. Control Theory and Applications, 2020, 37(4): 918-924. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY202004026.htm [25] LIN Chuan-kai. Nonsingular terminal sliding mode control of robot manipulators using fuzzy wavelet networks[J]. IEEE Transactions on Fuzzy Systems, 2006, 14(6): 849-859. [26] 侯明冬, 王印松. 轮式移动机器人的数据驱动轨迹跟踪滑模约束控制[J]. 控制与决策, 2020, 35(6): 1353-1360. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202006010.htmHOU Ming-dong, WANG Yin-song. Data-driven trajectory tracking sliding mode constraint control for wheeled mobile robot[J]. Control and Decision, 2020, 35(6): 1353-1360. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202006010.htm [27] YIN Xiu-xing, PAN Li, CAI Shi-bo. Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators[J]. Robotics and Computer-Integrated Manufacturing, 2021, 72: 101884. [28] LEE J H, SONG J Y, KIM J W, et al. Distance-based intelligent particle swarm optimization for optimal design of permanent magnet synchronous machine[J]. IEEE Transactions on Magnetics, 2017, 53(6): 1-4. [29] 李诚, 王小敏. 基于粒子群优化的ATO控制策略[J]. 铁道学报, 2017, 39(3): 53-58. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201703010.htmLI Cheng, WANG Xiao-min. An ATO control strategy based on particle swarm optimization[J]. Journal of the China Railway Society, 2017, 39(3): 53-58. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201703010.htm [30] 罗京, 胡伟, 刘豫湘. 中低速磁悬浮列车牵引特性分析和计算[J]. 电力机车与城轨车辆, 2010, 33(6): 21-22. https://www.cnki.com.cn/Article/CJFDTOTAL-DJJI201006008.htmLUO Jing, HU Wei, LIU Yu-xiang. Traction characteristics analysis and calculation of mid-low speed maglev trains [J]. Electric Locomotives and Mass Transit Vehicles, 2010, 33(6): 21-22. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DJJI201006008.htm -
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