基于MMDP的无人作战飞机任务分配模型研究

针对多无人作战飞机动态任务分配问题,以马尔科夫决策过程理论为基础,建立基于多智能体马尔科夫决策过程MMDP(Multi-Agent Markov Decision Process)的动态任务分配模型;系统中状态信息、目标的出现和收益值等均服从概率分布;改进的MM-DP寻优算法以多UCAV在有限时间内执行任务收益值最大为评价函数,并应用遗传算法在所有可选决策集中确定最优任务分配策略;数值仿真验证了算法的有效性。     

燃料电池有轨电车能量管理Pareto多目标优化

节能环保的出行方式得到政府的大力推广, 其中燃料电池混合动力有轨电车由于可无网运行且节能环保而备受关注.为了改善燃料电池/超级电容/动力电池大功率有轨电车的燃料经济性与系统耐久性, 提出一种有轨电车能量管理策略(Energy management strategy, EMS)的多目标优化方法. 首先以氢燃料消耗量和能量源性能衰减率作为评价指标, 建立多目标成本函数. 由于两个指标很难在同一个等式中评价, 设计了基于状态机与非支配排序的能量管理Pareto多目标优化方法, 获得了有轨电车能量管理策略Pareto非劣解集, 并分析了能量管理策略的目标功率参数对性能指标的影响规律, 进而遴选出兼顾燃料经济性与系统耐久性的综合最优解. 结果表明, 与功率跟随策略和基于遗传算法优化策略相比, 该能量管理优化方法的燃料经济性分别提高了29.4 %和2.4 %.     

火星探测器制动捕获多目标优化策略

本文提出了一种基于动态加权算法的火星探测器制动捕获多目标优化策略,该策略在火星探测器制动捕获过程中同时考虑制动目标轨道精度和燃料消耗,相对于传统的捕获策略,具有同等运算量下更易收敛到最优解的优势.本文以制动推力方向沿火星探测器速度反向的制动捕获方法为例,首先,描述了有限推力下火星探测器的制动捕获问题,然后,针对优化模型的非线性动态耦合特性,研究了基于自适应参数调整的动态PSO算法,最后,应用动态PSO算法和动态加权算法,设计了火星探测器制动捕获多目标优化策略.仿真分析表明,相比于传统的单目标优化策略,本文设计的火星探测器制动捕获多目标优化策略,不仅能够满足精度要求,而且更容易得到燃料相对最优值,减少燃料消耗.因此具有一定的研究和应用价值.     

基于Petri网炼油厂氢气系统计划优化的研究

为了降低炼油企业生产成本、实现节能降耗,必须保证氢气系统在最优的生产计划下运行。针对这一问题,本文将氢气总费用最小化作为生产计划优化目标,以某炼油厂为例,基于Petri网构建了氢系统中供氢装置和耗氢装置间的氢气匹配模型以及氢系统的费用模型,通过合理匹配供氢装置与耗氢装置的氢量得到氢气总费用最小时的生产计划。在Petri网得到的最优计划下氢系统氢气总费用为50335.0$/h,较优化前费用降低了23.4%,说明提出方法的可行性和有效性,对实际的炼油厂氢气系统优化计划具有一定的指导作用。     

融合马尔科夫决策过程与信息熵的对话策略

对话策略是人机对话系统中的重要组成成分,其性能的优劣直接影响对话系统的性能.在面对完全没有数据的冷启动场景时,收集对话数据进行对话策略学习的过程非常复杂和耗时.为在冷启动场景下能够保持良好性能,提出一种融合马尔科夫决策过程与信息熵的对话算法.利用马尔科夫决策过程快速获得下一步最优对话状态,并结合知识库通过引入属性信息熵方法排除多个状态值函数相同的最优状态,从而获取最优的系统响应动作.在音乐搜索领域数据集上的实验结果表明,与随机策略、基于规则和基于信息熵的算法相比,该算法分别缩短了2.24、0.84和0.03个对话轮次,且能够有效提高对话任务完成率.     

融合马尔科夫决策过程与信息熵的对话策略

对话策略是人机对话系统中的重要组成成分,其性能的优劣直接影响对话系统的性能.在面对完全没有数据的冷启动场景时,收集对话数据进行对话策略学习的过程非常复杂和耗时.为在冷启动场景下能够保持良好性能,提出一种融合马尔科夫决策过程与信息熵的对话算法.利用马尔科夫决策过程快速获得下一步最优对话状态,并结合知识库通过引入属性信息熵方法排除多个状态值函数相同的最优状态,从而获取最优的系统响应动作.在音乐搜索领域数据集上的实验结果表明,与随机策略、基于规则和基于信息熵的算法相比,该算法分别缩短了2.24、0.84和0.03个对话轮次,且能够有效提高对话任务完成率.     

基于马尔科夫过程理论的风电机组检修策略

综述了基于马尔科夫过程的风电机组检修策略.首先介绍了马尔科夫过程及相关理论知识,并分析其应用于风电机组检修中的可行性;然后分别从基于风电机组运行可靠性最优策略建立可靠性模型和基于维护成本最优策略建立老化模型两方面入手,指出各自考虑的侧重点,总结了国内外学者基于马尔科夫过程理论对风电机组检修策略的研究现状;最后指出由单一部件到多部件进行整台风电机组优化检修和风电场多台机组联合检修是未来的研究趋势.     

燃料电池车载氢系统结构强度及碰撞结果分析

车载氢系统是燃料电池汽车的关键组成部分,其结构强度是影响燃料电池汽车氢安全的重要因素,对氢系统进行仿真、试验分析,提出车载氢系统结构强度的判定方法。     

航天器伴星长期稳定伴飞姿轨控仿真系统设计

研究伴星运行优化问题针对稳定伴飞的控制策略问题,设计了以伴飞中心点作为研究对象、被动式的伴星轨道控制方法,为解决减少动力燃料的消耗和控制时间的选取两个问题,结合姿态控制调整伴星面质比的方法,确定了轨道和姿态协同控制保持伴飞稳定的姿轨控方案,并在Matlab环境下构建姿轨控仿真系统实现该方案,通过数值仿真进一步验证该控制方案的有效性.数值仿真结果表明,控制方案在不消耗燃料的情况下,可以将伴星控制在主星附近,达到实现维持伴飞稳定行飞行目标.     

基于依赖分析的马尔科夫网络分类器学习与优化

对可分解概率模式在0-1损失下证明马尔科夫网络分类器是最优分类器.针对目前建立马尔科夫网络分类器结构效率和可靠性低的问题,基于变量之间基本依赖关系、基本结构和依赖分析思想进行马尔科夫网络分类器结构学习来避免这些问题.并通过去除不相关和冗余属性变量的方法实现对马尔科夫网络分类器的优化,以提高抗噪声能力和预测能力.分别使用模拟和真实数据进行分类器分类准确性比较实验,实验结果显示优化后的马尔科夫网络分类器具有良好的分类准确性.     

多能源电动汽车中的Fuzzy控制策略及仿真

多能源电动汽车的能量存储系统由锌空电池、镍氢电池和超大电容三种能量存储元件组成。锌空电池为负载提供基本能量。镍氢电池工作在中级能量区，并回收下坡和刹车过程中的能量。超大电容工作在尖峰负载区，为大加速度过程提供能量，在短时间内可以实现能量回收。该文在多能源电动汽车的模型基础上，针对能量管理系统(EMS)提出了一种模糊控制策略。EMS模糊控制策略的输入包括所需功率、镍氢电池的SOC和超大电容的SOC，模糊控制策略的输出包括三个能量存储元件的分配功率因子，每个输入和输出有不同的模糊量。仿真结果表明：模糊控制策略比简单查表控制策略在续驶里程、燃料经济性和效率等方面均有所改善。     

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck (FCT) power system more efficient, so as to improve vehicle fuel economy. When the structure of power source system and the torque distribution strategy are determined, the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources. The paper simulates the assistance of the intelligent transport system (ITS) and carries out the eco-velocity planning using the traffic signal light. On this basis, in order to further improve the energy efficiency of FCT, a model predictive control (MPC)-based energy source optimization management strategy is innovatively developed, which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption. Under the scenarios of signalized intersections, based on the planned eco-velocity, the off-line simulation results show that the proposed MPC-based energy source management strategy (ESMS) can reduce hydrogen consumption of fuel cell up to 7\% compared with the existing rule-based ESMS. Finally, the Hardware-in-the-Loop (HiL) simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.     

Fuel efficient power management strategy for fuel cell hybrid powertrains

A real time control strategy for fuel cell hybrid vehicles is proposed. The objective is to reduce the hydrogen consumption by using an efficient power sharing strategy between the fuel cell system (FCS) and the energy buffer (EB). The energy buffer (battery or supercapacitor) is charge-sustained (no plug-in capabilities). The real time control strategy is derived from a non-causal optimization algorithm based on optimal control theory. The strategy is validated experimentally with a hardware-in-the-loop (HiL) test bench based on a 600 W fuel cell system.     

Combined passivity based control and optimal control for energy management of fuel cell/battery hybrid system

The energy management of hybrid electric vehicles is becoming an interesting topic for many researchers. Furthermore, the wise choice of the energy management strategy allows not only the best distribution of the power between the used sources, but also it allows reduction of consumption, increase in the lifetime of the sources, and improves the autonomy of the hybrid electric vehicle. The autonomy is guaranteed by the optimization of the embedded sources. In this study, the hybrid system consists of combining the fuel cell as the main source with the battery as the auxiliary source. The novelty of the proposed energy management strategy for the studied hybrid system is the combination between interconnection and damping assignment‐passivity based control and the Hamiltonian Jacobi Bellman method. The stability proof is given and the efficiency of the proposed strategy is proved by the experimental work, where the obtained results show the good and adequate results to the proposed scenario.     

Hardware-in-the-loop simulation for the design and verification of the control system of a series–parallel hybrid electric city-bus

Hybrid electric buses have been a promising technology to dramatically lower fuel consumption and carbon dioxide (CO₂) emission, while energy management strategy (EMS) is a critical technology to the improvements in fuel economy for hybrid electric vehicles (HEVs). In this paper, a suboptimal EMS is developed for the real-time control of a series–parallel hybrid electric bus. It is then investigated and verified in a hardware-in-the-loop (HIL) simulation system constructed on PT-LABCAR, a commercial real-time simulator. First, an optimal EMS is obtained via iterative dynamic programming (IDP) by defining a cost function over a specific drive cycle to minimize fuel consumption, as well as to achieve zero battery state-of-charge (SOC) change and to avoid frequent clutch operation. The IDP method can lower the computational burden and improve the accuracy. Second, the suboptimal EMS for real-time control is developed by constructing an Elman neural network (NN) based on the aforementioned optimal EMS, so the real-time suboptimal EMS can be used in the vehicle control unit (VCU) of the hybrid bus. The real VCU is investigated and verified utilizing a HIL simulator in a virtual forward-facing HEV environment consisting of vehicle, driver and driving environment. The simulation results demonstrate that the proposed real-time suboptimal EMS by the neural network can coordinate the overall hybrid powertrain of the hybrid bus to optimize fuel economy over different drive cycles, and the given drive cycles can be tracked while sustaining the battery SOC level.     

Speed planning and energymanagement strategy of hybrid electric vehicles in a car-following scenario

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.     

多能源电动汽车中的功率比较控制策略及仿真

在多能源电动汽车的模型基础上，针对能量管理系统(EMS)该文提出了一种功率比较控制策略。EMS的功率比较控制策略是通过比较实际所需功率和能量存储元件的最大提供和吸收功率来达到分配功率的目的。同时提出了最大提供功率和吸收功率的确定方法。仿真结果表明：功率比较控制策略比简单查表控制策略在续驶里程、燃料经济性和效率等方面均有所改善。     

并联新能源汽车最优效率实时控制

为了提高并联混合动力汽车驱动系统的实时效率,降低燃油消耗,本文提出一种基于效率最优的协调控制策略.根据不同驱动模式下电池的充放电状态,建立了充放电状态下驱动系统的等效燃油消耗模型,在分析电池效率和发动机效率的基础上,得到驱动系统效率的统一表达式,进而通过建立不同功率需求不同荷电状态下系统最优效率的功率分配系数图谱,设计了系统效率最优的协调控制策略,协调控制策略根据优化的功率分配系数在发动机和电机间进行力矩分配,协调控制策略可以离线计算并实时执行.两种工况循环下的仿真结果表明效率最优控制策略能有效地提高混合动力系统实时效率和燃油经济性.     

Real‐time optimal energy management for a fuel cell/battery hybrid system

An energy management strategy is proposed for a class of fuel cell/battery hybrid systems. In such hybrid systems, a fuel cell system is the main power source, and a lithium‐ion battery is the auxiliary power source. In order to manage the system power at the next moment in a reasonable way, a load current filter with bounded estimation errors is designed to estimate the load current. Then, a real‐time optimal energy management algorithm is proposed to optimize economy consumption of the hybrid system. By taking current change rate of the fuel cell and the state of charge into consideration and taking reasonable model simplifications, the optimization problem can be described as a quadratic programming problem. Then a general purpose solver is proposed to solve the quadratic programming problem based on the alternating direction method of multipliers. The efficiency of the proposed solver is much faster than computing interior point method or active set method. Simulation results in MATLAB/SIMULINK are carried out to validate the significant effectiveness and efficiency of the proposed management strategy.     

插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.