Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.

Predictive Mathematical and Statistical Modeling of the Dynamic Poverty Problem in Burundi: Case of an Innovative Economic Optimization System

The mathematical and statistical modeling of the problem of poverty is a major challenge given Burundi’s economic development. Innovative economic optimization systems are widely needed to face the problem of the dynamic of the poverty in Burundi. The Burundian economy shows an inflation rate of -1.5% in 2018 for the Gross Domestic Product growth real rate of 2.8% in 2016. In this research, the aim is to find a model that contributes to solving the problem of poverty in Burundi. The results of this research fill the knowledge gap in the modeling and optimization of the Burundian economic system. The aim of this model is to solve an optimization problem combining the variables of production, consumption, budget, human resources and available raw materials. Scientific modeling and optimal solving of the poverty problem show the tools for measuring poverty rate and determining various countries’ poverty levels when considering advanced knowledge. In addition, investigating the aspects of poverty will properly orient development aid to developing countries and thus, achieve their objectives of growth and the fight against poverty. This paper provides a new and innovative framework for global scientific research regarding the multiple facets of this problem. An estimate of the poverty rate allows good progress with the theory and optimization methods in measuring the poverty rate and achieving sustainable development goals. By comparing the annual food production and the required annual consumption, there is an imbalance between different types of food. Proteins, minerals and vitamins produced in Burundi are sufficient when considering their consumption as required by the entire Burundian population. This positive contribution for the latter comes from the fact that some cows, goats, fishes, ···, slaughtered in Burundi come from neighboring countries. Real production remains in deficit. The lipids, acids, calcium, fibers and carbohydrates produced in Burundi are insufficient for consumption. This negative contribution proves a Burundian food deficit. It is a decision-making indicator for the design and updating of agricultural policy and implementation programs as well as projects. Investment and economic growth are only possible when food security is mastered. The capital allocated to food investment must be revised upwards. Demographic control is also a relevant indicator to push forward Burundi among the emerging countries in 2040. Meanwhile, better understanding of the determinants of poverty by taking cultural and organizational aspects into account guides managers for poverty reduction projects and programs.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus(PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based(RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based(DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge(SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming(DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm(GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

Shape-sizing nested optimization of deployable structures using SQP

The potential role of formal structural optimization was investigated for designing foldable and deployable structures in this work.Shape-sizing nested optimization is a challenging design problem.Shape,represented by the lengths and relative angles of elements,is critical to achieving smooth deployment to a desired span,while the section profiles of each element must satisfy structural dynamic performances in each deploying state.Dynamic characteristics of deployable structures in the initial state,the final state and also the middle deploying states are all crucial to the structural dynamic performances.The shape was represented by the nodal coordinates and the profiles of cross sections were represented by the diameters and thicknesses.SQP(sequential quadratic programming) method was used to explore the design space and identify the minimum mass solutions that satisfy kinematic and structural dynamic constraints.The optimization model and methodology were tested on the case-study of a deployable pantograph.This strategy can be easily extended to design a wide range of deployable structures,including deployable antenna structures,foldable solar sails,expandable bridges and retractable gymnasium roofs.

Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。

沿海区域综合能源生产单元随机优化调度

为实现沿海区域的海上风电场、海上采气平台和陆上热电联供燃气电厂等多种能源生产子单元的协同化运行,考虑可再生能源出力和氢负荷的随机波动,提出沿海区域综合能源生产单元(coastal integrated energy production units,CIEPU)随机优化调度模型。采用参数化代价函数近似(parametric cost function approximation,PCFA)的动态规划算法求解随机优化调度模型。通过一种基于梯度下降的求解方法--Adadelta法,获得策略函数的一阶信息,并计算梯度平方的指数衰减平均值,以更新策略函数的迭代步长;对随机优化调度模型进行策略参数逼近,从而得到近似最优的策略参数,并逐一时段求解出CIEPU的最优调度计划。最后,以某个CIEPU为例,分析计算结果表明,所提出方法获得的优化调度方案可以提高CIEPU运行的经济性并降低碳排放量,验证了所提方法的准确性和高效性。

泵站单机组优化调度组合改进粒子群算法

开展泵站节能降耗优化模型算法研究、实现泵站经济运行具有重要的现实意义。动态规划法在泵站优化调度模型求解中较为常用,针对动态规划法决策变量离散处理对精度的影响,引入决策变量在可行域内随机生成并不断更新的粒子群算法,并提出“Sobol序列优化初始种群+实时调整惯性权重+正余弦替代学习因子”多策略融合的改进方法,通过4种基准函数性能测试,验证了改进粒子群算法在搜索能力和计算精度上有显著提升。在此基础上,将改进粒子群算法应用于某大型调水泵站以耗电费用最小为目标的单机组变速优化模型求解中,得到不同时段的最优决策方案及相应的目标最优值,并与动态规划法计算结果进行对比,2种方法最优决策过程基本一致,最优目标值精度相当。结果表明:粒子群算法组合改进策略是可行的,计算结果是可靠的,可以作为泵站优化调度模型求解的一种有效方法。

基于DP-MSCAOA算法的梯级水库多目标防洪优化调度研究

为提高梯级水库联合防洪能力,针对不同频率洪水,综合考虑大坝防洪安全和下游防护区防洪安全,以调度期水库最高运行水位最低、下游防洪控制断面最大削峰和下游防护区超额洪量最小为目标,建立梯级水库多目标防洪联合优化调度模型,设计融合动态规划、多策略协同阿基米德优化算法优势的DP-MSCAOA嵌套优化算法,并以资水某梯级水库为例,针对不同频率洪水进行多目标防洪联合优化调度,与常规调度结果和粒子群优化结果进行对比。结果表明,多目标联合优化调度模型削峰和错峰效果更优,验证了多目标联合优化调度模型的适用性及DP-MSCAOA嵌套优化算法的有效性,可为降低洪灾风险、缓解防洪压力提供技术支撑。

机会约束的多选择背包问题的遗传算法求解

机会约束的多选择背包问题(CCMCKP)是一类具有重要应用价值的NP难组合优化问题,但目前还缺乏关于该问题求解方法的专门研究。为此,提出首个CCMCKP的求解框架,并基于该框架构建了两种求解方法:基于动态规划的RA-DP和基于遗传算法的RA-IGA。RA-DP是精确求解方法,具有最优性保证,但是在可接受的时间(1 h)内仅能求解小规模问题样例;相较而言,RA-IGA是近似求解方法,具有更好的可扩放性。仿真实验结果验证了所提求解方法的性能:在小规模问题样例上,RA-DP和RA-IGA都可以找到最优解;在中大规模问题样例上,RA-IGA表现出了比RA-DP显著更高的求解效率,它总是可以在给定时间(1 h)内快速获得可行解。在CCMCKP的后续研究中,RA-DP和RA-IGA可作为基准对比方法,而实验工作中所构建的测试样例集可作为该问题的标准测试集。

智慧园区无线传感网络能量均衡覆盖优化系统

针对传统LEACH协议存在数据传输效率较低以及节点能量分布不均的缺陷,文中从功耗和效率层面出发,提出了一种无线传感网络能量均衡覆盖优化系统。对于节点能量分布不均的问题,设计了由剩余能量因子与距离因子所组成的系统权重因子,并根据该因子及竞争半径来选择簇头。同时基于遗传算法提出了一种单链多跳路由协议,进而提升了系统的功耗水平,解决了数据传输效率偏低的问题。以某智慧园区为模型进行的实验测试结果表明,所设计系统的存活节点个数、系统剩余能量以及数据包接收数量这三个指标均大幅领先,充分说明了改进后的算法能够有效减少系统的能量损耗并提升节点的利用率,使其可以存活至1200轮,从而延长整个WSN系统的运行周期。

Optimal redundancy allocation for reliability systems with imperfect switching

The problem of stochastically allocating redundant com- ponents to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with sub- systems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution,

Parallel Minimax Searching Algorithm for Extremum of Unimodal Unbounded Function

In this paper we consider a parallel algorithm that detects the maximizer of unimodal function f(x) computable at every point on unbounded interval (0, ∞). The algorithm consists of two modes: scanning and detecting. Search diagrams are introduced as a way to describe parallel searching algorithms on unbounded intervals. Dynamic programming equations, combined with a series of liner programming problems, describe relations between results for every pair of successive evaluations of function f in parallel. Properties of optimal search strategies are derived from these equations. The worst-case complexity analysis shows that, if the maximizer is located on a priori unknown interval (n-1], then it can be detected after cp(n)=「2log「p/2」+1(n+1)」-1 parallel evaluations of f(x), where p is the number of processors.

长输管道开泵方案运行优化与能耗分析研究

针对长输管道水力能耗较高的问题,以某输油管道为例,将泵站运行费用最小定为目标函数,在考虑约束前提下,结合相似条件,采用动态规划算法对最经济开泵方案和站内流量分配进行逐级求解和递推,确定了不同输量条件下的管道运行优化方案。实际运行结果表明:整条管道的压力变化均在要求的范围内,可以避免管道超压或欠压事故的发生,证明了所建模型的准确性和动态规划算法求解的可行性;采用变频器对泵转速进行调节可以降低管输能耗,最大节能比为25%,且不会对泵的几何结构产生影响,适合不同输量下的开泵工况优化;采用调速+改变叶轮直径施措可以进一步降低管输能耗,最大节能比为33%,但考虑到切割叶轮后会降低泵的扬程和功率,且这种变化不可逆,因此对于输量逐渐上升的管道并不适用。该研究结果可为输油管道的安全平稳运行提供理论依据。

基于改进蚁群动态规划的光储微网容量优化配置

为降低并网光储微网的综合发电成本,并提高优化容量配置性能,该文研究基于改进蚁群动态规划算法的光储微网容量优化配置方法。针对传统启发式算法容易陷入局部最优以及早熟收敛的问题,首先将蚁群算法与动态规划算法结合,简化计算过程;进而,将迭代次数有关的衰减参数引入调节因子中,提高其全局搜索能力;最后,将Boltzmann选择机制引进蚁群搜索过程,并在蚁群信息素更新过程中采用偏转角度因子与拐点参数进行修正,从而大幅提高算法的优化性能。通过对海宁某小区实际数据进行仿真分析,验证了该算法的实用性和优越性。

基于改进遗传算法与动态规划的输水泵站日优化调度

针对我国泵站因不合理调度而导致经济效益不高的问题,基于存在多管道系统的输水泵站,考虑峰谷电价的影响,以日运行费用最小为目标,构建了泵站日经济优化模型,并以蒙开个河库连通工程二级泵站为例,基于组合变异的改进遗传算法、动态规划法,对定速节流调节、变频调节两种调节方式进行泵站日优化调度。结果表明,改进遗传算法适用于泵站优化调度问题;同时发现当机组实际工况点偏离设计工况点较远时,采用变频调节经济效益更高。

基于动态规划算法的智能配电网络重构优化系统

目前设计的智能配电网络重构优化系统无法有效改善电压质量,网损较高。为了解决该问题,基于动态规划算法设计了一种新的智能配电网络重构优化系统。配电网络重构优化系统硬件区域主要由调用模块、馈线终端单元模块、控制模块、通信模块四大部分组成。通过调控晶闸管完成配电网络中线路的调用工作,晶闸管的输入端配置一个发光二极管,输出端配置一个驱动器,馈线终端单元采用DSP芯片和ARM芯片双核心结构。通过获取需要处理的配电网络信息、配电网络链路重构、建立目标函数,实现重构优化。实验结果表明,所设计的智能配电网络重构优化系统能够有效改善电压质量,达到降低网损的效果。

基于遗传算法的大小交路开行方案优化模型研究

近年来,我国城市轨道交通快速发展,并成为城市公共交通的重要组成部分。城市轨道主要采用大小交路方案,即将轨道交通线路划分为2条大小不同的交路,可以更好地应对客流不均匀的情况,大小交路运营模式是我国城市轨道交通运用得最广泛的运营管理方式,在面对庞大的客流量和复杂的车站数据时,需要将企业的运营成本尽可能最小化和乘客服务水平尽可能最大化,在此基础上决定列车开行方案成为城市轨道交通运营的重要一环,故该文旨在解决大小交路模式下的列车开行方案问题。

面向最优直方图求解的监督学习模型研究

最优直方图是一类重要的直方图技术,目前用于实现最优直方图的动态规划分组算法存在时间复杂度过高的问题。因此,提出了一种基于概率稀疏自注意力的监督学习模型来学习动态规划分组算法,该监督学习模型可作为动态规划分组算法的替代方案,主要包括3个部分:1)通过Embedding层与位置编码层将输入数值序列映射为对应的向量序列;2)通过概率稀疏的自注意力层捕获输入序列之间的依赖关系;3)通过前馈神经网络层将依赖关系映射到分组“桶”边界下标信息。实验结果表明,基于概率稀疏自注意力的监督学习模型在6个数据集上的准确率超过了83.47%,且其在预测阶段的时间消耗不超过动态规划分组算法的1/3。

程序设计竞赛中线段树的四类典型应用场景

线段树是程序设计竞赛的重要知识点,应用场景复杂多变且不单独作为模板考察。现有教材和文献仅对线段树的基本理论与模板实现做了解析,而未对其在竞赛中的应用场景进行归类总结。学生只能依靠大量的习题训练和竞赛经验积累,才能摸清门路。对此,本文归纳出了关于线段树在程序设计竞赛中的四类典型应用场景:“扫描线算法的优化”、“树形结构信息的维护”、“带修改的结合律信息的维护”和“动态规划算法的优化”,并针对每类场景进行举例与解析。以期帮助学生对线段树的应用建立系统性的认识,从而降低学习难度,缩短学习周期,提升学习效率。

改进动态规划法对机械工作站项目实施过程的优化

机械工作站项目实施周期长、涉及内容广。如何有效对机械工作站项目实施优化,是目前该领域亟待解决的问题。本文提出一种改进动态规划,整合机械工作站的实施内容、时间和步骤,以实施的效果、成本和时间为考核目标,优化该项目。仿真结果显示,改进动态规划法能缩短机械工作站实施时间,简化项目的实施流程,并节约相应的施工成本,优化幅度达30%~40%。因此,改进动态规划法能促进机械工作站项目的有效实施,满足现有的项目施工要求。