The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gr...The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.展开更多
A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall ...A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall into local optimal solution.To solve the globally optimal control law sequence,we use the dynamic programming algorithm to discretize the separation control decision-making process into a series of sub-stages based on the time characteristics of the separation allocation model,and recursion from the end stage to the initial stage.The sequential quadratic programming algorithm is then used to solve the optimal return function and the optimal control law for each sub-stage.Comparative simulations of the combined algorithm and the traditional algorithm are designed to validate the superiority of the combined algorithm.Aircraft-following and cross-conflict simulation examples are created to demonstrate the combined algorithm’s adaptability to various conflict scenarios.The simulation results demonstrate the separation deploy strategy’s effectiveness,efficiency,and adaptability.展开更多
为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控...为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控制输出的函数映射关系。与传统的强化学习控制算法相比,偏好强化学习算法无需设定回报函数,只需对多动作进行偏好判断即可实现网络训练收敛,克服了传统强化学习方法中回报函数加权归一化设计难题。通过仿真试验和硬件在环验证了所提出能量管理策略的有效性和可行性。结果表明,与传统强化学习能量管理策略相比,该策略能够在满足混合动力车辆SOC平衡和动力性约束下,提升经济性4.6%~10.6%。展开更多
以某并联式混动公交车为研究对象,建立了四种典型工况模型,采用蚁群算法优化了最小等效燃油消耗控制策略中四种工况的充放电等效因子;分析了路面坡度与电池荷电状态(state of charge,SOC)目标值域调整之间的对应关系,设计了相应坡度自...以某并联式混动公交车为研究对象,建立了四种典型工况模型,采用蚁群算法优化了最小等效燃油消耗控制策略中四种工况的充放电等效因子;分析了路面坡度与电池荷电状态(state of charge,SOC)目标值域调整之间的对应关系,设计了相应坡度自适应模块;提出了基于道路工况分析的混合动力汽车(hybrid electric vehicle,HEV)控制策略优化方法.典型工况下的仿真对比分析表明,该方法具有良好的工况适应能力,燃油经济性明显优于几类典型HEV控制策略.展开更多
基金Supported by the Aeronautical Science Foundation of China(2010ZB52011)the Funding of Jiangsu Innovation Program for Graduate Education(CXLX11-0213)the Nanjing University of Aeronautics and Astronautics Research Funding(NS2010055)~~
文摘The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.
基金supported in part by the National Natural Science Foundation of China(Nos.61773202,52072174)the Foundation of National Defense Science and Technology Key Laboratory of Avionics System Integrated Technology of China Institute of Aeronautical Radio Electronics(No.6142505180407)+1 种基金the Open Fund for Civil Aviation General Aviation Operation Key Laboratory of China Civil Aviation Management Cadre Institute(No.CAMICKFJJ-2019-04)the National key R&D plan(No.2021YFB1600500)。
文摘A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall into local optimal solution.To solve the globally optimal control law sequence,we use the dynamic programming algorithm to discretize the separation control decision-making process into a series of sub-stages based on the time characteristics of the separation allocation model,and recursion from the end stage to the initial stage.The sequential quadratic programming algorithm is then used to solve the optimal return function and the optimal control law for each sub-stage.Comparative simulations of the combined algorithm and the traditional algorithm are designed to validate the superiority of the combined algorithm.Aircraft-following and cross-conflict simulation examples are created to demonstrate the combined algorithm’s adaptability to various conflict scenarios.The simulation results demonstrate the separation deploy strategy’s effectiveness,efficiency,and adaptability.
文摘为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控制输出的函数映射关系。与传统的强化学习控制算法相比,偏好强化学习算法无需设定回报函数,只需对多动作进行偏好判断即可实现网络训练收敛,克服了传统强化学习方法中回报函数加权归一化设计难题。通过仿真试验和硬件在环验证了所提出能量管理策略的有效性和可行性。结果表明,与传统强化学习能量管理策略相比,该策略能够在满足混合动力车辆SOC平衡和动力性约束下,提升经济性4.6%~10.6%。
文摘以某并联式混动公交车为研究对象,建立了四种典型工况模型,采用蚁群算法优化了最小等效燃油消耗控制策略中四种工况的充放电等效因子;分析了路面坡度与电池荷电状态(state of charge,SOC)目标值域调整之间的对应关系,设计了相应坡度自适应模块;提出了基于道路工况分析的混合动力汽车(hybrid electric vehicle,HEV)控制策略优化方法.典型工况下的仿真对比分析表明,该方法具有良好的工况适应能力,燃油经济性明显优于几类典型HEV控制策略.
