基于迭代学习控制的几类列车自动控制问题研究
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摘要
摘要：论文将迭代学习控制理论引入列车自动控制领域,研究了动力学模型的参数辨识,运行曲线跟踪控制,安全控制,以及进站停车控制等列控领域的重要问题,提出了多种新算法。在这些基于迭代学习控制的列控算法中,列车运行的重复性信息被充分挖掘和利用,因此控制性能可以随列车重复运行而逐步提高。
现将论文的主要工作及创新点总结如下：
第一,提出了列车动力学模型参数的迭代学习辨识算法。算法以实测数据为期望输出量,以待辨识参数为控制输入量,借助迭代学习辨识器不断更新待辨识参数,最终使该参数逼近期望值。通过严格的数学分析证明了待辨识参数误差的收敛性,随后通过实例仿真验证了算法的有效性。
第二,提出了基于迭代学习控制的列车运行曲线跟踪控制算法。算法以期望的列车最优运行曲线为跟踪目标,利用前次运行时的速度跟踪偏差校正当前运行的控制量(牵引力或制动力),从而使得列车在重复运行过程中跟踪性能逐步提高。通过数学分析给出了跟踪误差收敛性定理。最后的实例仿真验证了所提算法的有效性。
第三,分析了应用所提运行运行曲线跟踪控制算法时列车运行的安全性问题。通过对列车速度跟踪误差和位移跟踪误差的分析,给出了防止超速和追尾事故发生的充分性条件,然后以此为基础分析了列车最小追踪间隔问题。
第四,提出了基于终端迭代学习控制的列车自动停车控制算法。算法依次选取初始制动位置,制动力,以及两者的组合作为控制量,给出了三种情形下的终端迭代学习控制算法并证明了停车误差的收敛性。在第三种算法中,同时选取系统的初始状态(初始制动位置)和外部输入信号(制动力)为控制量,以充分利用系统资源提高误差收敛速度,从理论上提出了一种终端迭代学习控制器设计的新方法。
本文研究的基于迭代学习控制的列车自动控制算法,将列车控制问题由时间域推广到迭代域内解决,最大特点是可以使控制性能沿迭代轴逐步提高,弥补了现有的控制方法无法利用列车运行的重复性提高控制性能的缺陷。同时,文中部分算法还从理论上对迭代学习控制器的设计提出了改进。
ABSTRACT:
This dissertation introduces iterative learning control (ILC) into automatic train control field and intensively studies several important problems, including dynamical model identification, trajectory tracking control, safety control and station stopping control. Some novel algorithms are proposed, which make full use of the repeatability of the train motion pattern in order that the control performance can be iteratively improved through the repetitive running cycles.
The main works and contributions are summarized as following:
1. An iterative learning identifier is developed to identify the parameters in the train dynamical model. The identifier has an ability to make the parameters converge to their actual values through repeated identifying trials where the experimental data serve as the desired outputs and the parameters to be identified serve as the control inputs. Both the theoretical analysis and simulation examples demonstrate the validity and effectiveness of the proposed identification method.
2. An iterative learning control approach is proposed to address the trajectory tracking problem of a train operation. The ILC-ba33sed controller makes use of previous speed tracking error to modify the current control input (tration force or braking force). Therefore, the controlled train is guranteed to track the desired trajectory (usually from optimal scheduling) without deviation when the running cycle increases to infinity. Finally, the simulation examples verify the effectiveness of proposed algorithms.
3. The safety issues under the ILC-based trajectory tracking algorithm are discussed in detail. With theoretical analysis of the speed and displacement tracking errors, the sufficient conditions to prevent overspeed and collision accidents are derived. Finally, the minimum headway problem is investigated.
4. Three train station stop control algrithms based on terminal iterative learning control (TILC) are proposed. The initial braking point, the braking force and the combination of them are chosen as the control profile in turn, and the corresponding learning laws and convergence theorems are presented respectively for the three scenarios. In the 3rd scenario, the initial state (initial braking point) and the external input signal (the braking force) are chosen as the control input simultaneously, in order that the convergence speed is effectively increased. This provides a new structure of the terminal iterative learning controller.
In these ILC-based train control algorithms, the control tasks are technically extended from the time domain to the iteration domain. Thus, the control performance can be effectively improved along the iteration axis by utilizing the obvious repeatability. This is a main priority compared with other control methods. Meanwhile, some proposed algorithms also make progress in ILC theory.
现将论文的主要工作及创新点总结如下：
第一,提出了列车动力学模型参数的迭代学习辨识算法。算法以实测数据为期望输出量,以待辨识参数为控制输入量,借助迭代学习辨识器不断更新待辨识参数,最终使该参数逼近期望值。通过严格的数学分析证明了待辨识参数误差的收敛性,随后通过实例仿真验证了算法的有效性。
第二,提出了基于迭代学习控制的列车运行曲线跟踪控制算法。算法以期望的列车最优运行曲线为跟踪目标,利用前次运行时的速度跟踪偏差校正当前运行的控制量(牵引力或制动力),从而使得列车在重复运行过程中跟踪性能逐步提高。通过数学分析给出了跟踪误差收敛性定理。最后的实例仿真验证了所提算法的有效性。
第三,分析了应用所提运行运行曲线跟踪控制算法时列车运行的安全性问题。通过对列车速度跟踪误差和位移跟踪误差的分析,给出了防止超速和追尾事故发生的充分性条件,然后以此为基础分析了列车最小追踪间隔问题。
第四,提出了基于终端迭代学习控制的列车自动停车控制算法。算法依次选取初始制动位置,制动力,以及两者的组合作为控制量,给出了三种情形下的终端迭代学习控制算法并证明了停车误差的收敛性。在第三种算法中,同时选取系统的初始状态(初始制动位置)和外部输入信号(制动力)为控制量,以充分利用系统资源提高误差收敛速度,从理论上提出了一种终端迭代学习控制器设计的新方法。
本文研究的基于迭代学习控制的列车自动控制算法,将列车控制问题由时间域推广到迭代域内解决,最大特点是可以使控制性能沿迭代轴逐步提高,弥补了现有的控制方法无法利用列车运行的重复性提高控制性能的缺陷。同时,文中部分算法还从理论上对迭代学习控制器的设计提出了改进。
ABSTRACT:
This dissertation introduces iterative learning control (ILC) into automatic train control field and intensively studies several important problems, including dynamical model identification, trajectory tracking control, safety control and station stopping control. Some novel algorithms are proposed, which make full use of the repeatability of the train motion pattern in order that the control performance can be iteratively improved through the repetitive running cycles.
The main works and contributions are summarized as following:
1. An iterative learning identifier is developed to identify the parameters in the train dynamical model. The identifier has an ability to make the parameters converge to their actual values through repeated identifying trials where the experimental data serve as the desired outputs and the parameters to be identified serve as the control inputs. Both the theoretical analysis and simulation examples demonstrate the validity and effectiveness of the proposed identification method.
2. An iterative learning control approach is proposed to address the trajectory tracking problem of a train operation. The ILC-ba33sed controller makes use of previous speed tracking error to modify the current control input (tration force or braking force). Therefore, the controlled train is guranteed to track the desired trajectory (usually from optimal scheduling) without deviation when the running cycle increases to infinity. Finally, the simulation examples verify the effectiveness of proposed algorithms.
3. The safety issues under the ILC-based trajectory tracking algorithm are discussed in detail. With theoretical analysis of the speed and displacement tracking errors, the sufficient conditions to prevent overspeed and collision accidents are derived. Finally, the minimum headway problem is investigated.
4. Three train station stop control algrithms based on terminal iterative learning control (TILC) are proposed. The initial braking point, the braking force and the combination of them are chosen as the control profile in turn, and the corresponding learning laws and convergence theorems are presented respectively for the three scenarios. In the 3rd scenario, the initial state (initial braking point) and the external input signal (the braking force) are chosen as the control input simultaneously, in order that the convergence speed is effectively increased. This provides a new structure of the terminal iterative learning controller.
In these ILC-based train control algorithms, the control tasks are technically extended from the time domain to the iteration domain. Thus, the control performance can be effectively improved along the iteration axis by utilizing the obvious repeatability. This is a main priority compared with other control methods. Meanwhile, some proposed algorithms also make progress in ILC theory.
引文
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