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An optimal control strategy for Multi-UAVs Target Tracking and Cooperative Competition

IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1931-1947页

作者： Yiguo Yang Liefa Liao Hong Yang Shuai Li Jiangxi University of Science and Technology Ganzhou 341000China College of Engineering Swansea UniversitySwansea SA18ENUK

An optimal control strategy of winner-take-all(WTA)model is proposed for target tracking and cooperative competition of multi-UAVs(unmanned aerial vehicles).In this model,firstly,based on the artificial potential field method,the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic ***,according to the finite-time convergence high-order differentiator,a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking *** simulation results show that the designed speed tracking controller has the advantages of fast tracking,high precision,strong stability and avoiding ***,a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control *** analysis and numerical simulation results show that the model has the fast convergence,high control accuracy,strong stability and good robustness.

关键词： Artificial potential field(APF) fuzzy control higher-order differentiator optimal control strategy winner-take-all(WTA)

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A Markov decision problem in a risk model with interest rate and Markovian environment

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Science China Mathematics 2016年第1期59卷 191-204页

作者： TAN JiYang YANG XiangQun LI ZiQiang CHENG YangJin School of Mathematics and Computational Science Xiangtan University College of Mathematics and Computer Science Hunan Normal University College of Information Engineering Xiangtan University

We consider the compound binomial model in a Markovian environment presented by Cossette et al.(2004). We modify the model via assuming that the company receives interest on the surplus and a positive real-valued premium per unit time, and introducing a control strategy of periodic dividend payments. A Markov decision problem arises and the control objective is to maximize the cumulative expected discounted dividends paid to the shareholders until ruin minus a discounted penalty for ruin. We show that under the absence of a ceiling of dividend rates the optimal strategy is a conditional band strategy given the current state of the environment process. Under the presence of a ceiling for dividend rates, the character of the optimal control strategy is given. In addition, we offer an algorithm for the optimal strategy and the optimal value *** results are provided to illustrate the algorithm and the impact of the penalty.

关键词： Markovian environment optimal control strategy periodic dividend interest rate penalty for

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Online power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning and driving cycle reconstruction

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Green Energy and Intelligent Transportation 2022年第2期1卷 62-74页

作者： Zhiyuan Fang Zeyu Chen Quanqing Yu Bo Zhang Ruixin Yang School of Mechanical Engineering and Automation Northeastern UniversityWenhua Road110819 ShenyangChina National Engineering Laboratory for Electric Vehicles Beijing Institute of TechnologyZhongguancun South Street100081 BeijingChina School of Automotive Engineering Harbin Institute of TechnologyWenhua West Road264209 WeihaiChina

This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning *** parallel soft actor-critic(SAC)networks are trained for high speed,medium speed,and low-speed conditions respectively;the reward function is designed as minimizing the cost of energy cost and battery *** operation,the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization(LVQ)neural *** top of that,a driving cycle reconstruction algorithm is *** historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed,medium speed,and low speed,based on which the algorithms are online *** SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical *** results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38%after the online update for uncertain driving conditions.

关键词： Electric vehicle Deep reinforcement learning Power management strategy Driving cycle reconstruction optimal control strategy

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Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization strategy for Optimized control of a Hybrid Electric Vehicle

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Journal of Transportation Technologies 2021年第4期11卷 471-503页

作者： Thomas P. Harris Andrew C. Nix Mario G. Perhinschi W. Scott Wayne Jared A. Diethorn Aaron R. Mull West Virginia University Morgantown USA

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge

关键词： Hybrid Electric Vehicle Artificial Neural Network Equivalent Consumption Minimization strategy (ECMS) optimal control strategy

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城市空气质量目标约束下冬季污染物减排最优控制策略研究

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管理科学 2023年第9期

作者：陈晓红周明辉唐湘博

本文提出城市空气质量达标约束的污染减排最优控制系统，以空气质量模型（WRF—CMAQ）为基础，搭建“本地化”空气质量模拟平台，构建空气质量达标评估模型和减排成本优化模型，并通过遗传算法求解湘潭市冬季拟定空气质量目标约束下污... 详细信息

本文提出城市空气质量达标约束的污染减排最优控制系统，以空气质量模型（WRF—CMAQ）为基础，搭建“本地化”空气质量模拟平台，构建空气质量达标评估模型和减排成本优化模型，并通过遗传算法求解湘潭市冬季拟定空气质量目标约束下污染物减排最优控制策略。结果表明，在保持臭氧浓度不变的情形下，当目标值分别拟定为55 、60 、65 时，可得到相应的污染物减排最优控制方案。空气质量目标值分别改进30.4%、24.1%、17.8%，对应减排总成本分别为16.6×元、6.36×元、1.46×元。本文构建的城市污染减排最优控制系统及其模型求解方法，不仅可为制订城市冬季重污染天气应对方案提供有效的技术支撑，也可为城市制定“一市一策”空气质量达标战略规划提供理论指导与决策方法。

关键词：冬季空气质量目标遗传算法污染物减排最优控制策略 winter air quality target genetic algorithm pollution reduction optimal control strategy

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