自动人流系统2车自动轨道交通列车运行鲁棒最优控制

Hermanto, E. Joelianto
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引用次数: 2

摘要

本文考虑了在规定的时刻和停车时间将列车从一站引导到下一站的能量消耗最小化的列车运行。将能量消耗最小化定义为最优轨迹,作为鲁棒最优控制的参考信号来调节自动客运系统(APM)中的AGT列车。在这种情况下,我们考虑最优控制系统,其中性能指标是一般形式,除了积分成本函数外,还包含最终(终端)成本函数。由于最终时间和最终状态是固定的或指定的,因此除了问题表述中给出的边界条件外,不需要使用额外的边界条件。采用全状态反馈控制律设计线性二次型调节器(LQR)和PID控制器。本文还在鲁棒控制理论的框架内考虑了PID控制器的参数选择问题,为PID控制器的整定优化提供了一种替代方法。为了处理列车参数(如质量)的不确定性,采用鲁棒设计方法对给定系统寻找控制器,使闭环系统具有鲁棒性。考虑不确定的到站客流,设计了鲁棒最优控制律,以抑制这种不确定性和不确定扰动对列车系统标称质量的影响。仿真结果表明,所提出的列车控制算法对列车参数扰动具有较好的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Optimal Control for Train Operation of 2 Car AGT (Automated Guide-way Transit) in Automatic People Movers System
The paper considers a train operation which minimizes energy consumption for leading the train from one station to the next station at the specified instant and stopping times. The minimization of the energy consumption is defined as an optimal trajectory used as a reference signal for the robust optimal control to regulate an AGT train in Automatic People Movers (APM) system. In this case, we consider the optimal control system where the performance index is of general form containing a final (terminal) cost function in addition to the integral cost function. Since the final time and the final state are fixed or specified, there are no extra boundary conditions to be used other than those given in the problem formulation. Full state feedback control law is selected to design Linear Quadratic Regulator (LQR) and PID controller. Here we also consider the parameter selection problems of the PID controller within the framework of robust control theory which provides an alternative method to optimize the setting of the PID controller. To handle uncertainty of the train parameter such as mass, robust design is applied to find a controller, for a given system, such that the closed loop system is robust. By considering the uncertain passenger arrival flow, the robust optimal control law is designed to suppress the effect of this uncertainty and the uncertain disturbances to the nominal mass of the train system. Simulations show the robustness of the proposed train control algorithms against disturbances occurring on a train parameter.
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