This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynami...This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.展开更多
既有交叉口信号配时与网联自动驾驶车辆(Connected and Automated Vehicle,CAV)轨迹规划协同优化中,未考虑CAV环境下出口、左转、直行及右转车道数在运营期可灵活动态调整的优势。本文结合CAV技术特征,提出一套CAV环境下交叉口车道分配...既有交叉口信号配时与网联自动驾驶车辆(Connected and Automated Vehicle,CAV)轨迹规划协同优化中,未考虑CAV环境下出口、左转、直行及右转车道数在运营期可灵活动态调整的优势。本文结合CAV技术特征,提出一套CAV环境下交叉口车道分配可动态调整的控制规则,称为灵活车道策略,与已有固定车道策略相比,实现了运营期交叉口各方向出口车道数和进口车道数(包括左转、直行和右转)的灵活调整。将车道分配和信号配时与CAV轨迹规划纳入到一个统一优化框架中,构建混合整数线性规划优化模型,同时,可根据各个方向车道分配情况自动生成可行的相位相序方案,并通过案例分析验证模型的有效性。研究结果表明:优化模型可根据各流向交通需求生成最优车道分配方案,尤其是当固定车道策略的车道分配与各流向交通组成不匹配时,灵活车道策略有助于提升交叉口通行效率;在低流量场景,灵活车道策略降低了4.08%的车均延误;在高流量场景,交叉口采用固定车道策略将处于过饱和状态,而灵活车道策略依然能满足通行需求。展开更多
基金Supported by the National Science Foundation (U.S.A.) under Grant ECS-0355364
文摘This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.
基金Supported by National High Technology Research and Development Program of China （863 Program） （2006AA04Z183）, National Nat- ural Science Foundation of China （60621001, 60534010, 60572070, 60774048, 60728307）, and the Program for Changjiang Scholars and Innovative Research Groups of China （60728307, 4031002）
文摘既有交叉口信号配时与网联自动驾驶车辆(Connected and Automated Vehicle,CAV)轨迹规划协同优化中,未考虑CAV环境下出口、左转、直行及右转车道数在运营期可灵活动态调整的优势。本文结合CAV技术特征,提出一套CAV环境下交叉口车道分配可动态调整的控制规则,称为灵活车道策略,与已有固定车道策略相比,实现了运营期交叉口各方向出口车道数和进口车道数(包括左转、直行和右转)的灵活调整。将车道分配和信号配时与CAV轨迹规划纳入到一个统一优化框架中,构建混合整数线性规划优化模型,同时,可根据各个方向车道分配情况自动生成可行的相位相序方案,并通过案例分析验证模型的有效性。研究结果表明:优化模型可根据各流向交通需求生成最优车道分配方案,尤其是当固定车道策略的车道分配与各流向交通组成不匹配时,灵活车道策略有助于提升交叉口通行效率;在低流量场景,灵活车道策略降低了4.08%的车均延误;在高流量场景,交叉口采用固定车道策略将处于过饱和状态,而灵活车道策略依然能满足通行需求。
