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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Wu, Xueyang Shan, Yinghao Yu, Xiangkai Donghua University College of Information Science and Technology China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai China

ISBN: (纸本)9798350309638

Focusing on the system with renewable distributed generations, this paper builds the microgrid system for improved electrical power supply. Traditionally, to realize hierarchical control of microgrids, linear control methods are usually adopted. However, they are reported to be not good at tackling complex nonlinear and stochastic problems. Thus, in this paper, more advanced and cooperative methods are proposed. Specifically, model predictive controls (MPCs) are firstly designed for both primary and secondary control levels and AC/DC subgrids. Then a transient function based on the switching control mechanism to alleviate transient deviations when system operation changes is proposed. Finally, a coordinated switching principle for the cooperation of hierarchical control is presented to combine the advantages of MPCs. Comprehensive case studies and comparisons with existing traditional methods have validated the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Model predictive control

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UAV-Assisted Vehicular Communication System Optimization With Aerial Base Station and Intelligent Reflecting Surface

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-12页

作者： Wang, Peng Li, Demin Zhang, Yihong Chen, Xuemin College of Information Science and Technology Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Donghua University Shanghai China Department of Engineering Texas Southern University Houston TX USA

Unmanned aerial vehicle (UAV) has recently contributed significantly to vehicle communications, offering promising solutions for enhancing connectivity in modern transportation systems. However, ensuring reliable signal coverage of the UAV and overcoming obstacles that hinder direct communication between vehicles remain challenging. To address these issues, this paper explores the use of the UAV as an aerial base station (ABS) and intelligent reflecting surface (IRS) for enhancing vehicular communication networks. The goal is to maximize the average bit rate of vehicles in both UAV-to-vehicle (U2V) and vehicle-to-vehicle (V2V) communication while ensuring minimum communication rate requirements. To remedy the problem which is formulated as a complex and non-convex optimization, a novel average bit rate algorithm based on alternative optimization (AO) technique is applied. Then a low-complexity linear relaxation method is proposed for scheduling variables, and a closed-form solution to the problem of IRS phase is presented. The trajectory optimization problem is transformed into an approximate convex problem with the help of successive convex approximation (SCA) method. The paper concludes with the proof of convergence and numerical results, demonstrating that our proposed design exhibits better performance than those corresponding baseline schemes. IEEE

关键词： Complex networks

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Optimal Scheduling for Shared Energy Storage Users Considering Both Construction and Operation of the Shared Energy Storage Station 9

Optimal Scheduling for Shared Energy Storage Users Consideri...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Xu, Qicheng Shan, Yinghao Wu, Xueyang Wang, Ziping Donghua University College of Information Science and Technology Shanghai China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai China

ISBN: (纸本)9798350309638

Shared energy storage is an emerging energy storage system. Optimal scheduling can maximize the resources of shared ESSs, thereby improving economic efficiency. This paper explores the optimal scheduling of electricity consumption behavior among shared energy storage users. Mixed integer linear programming is used to establish the optimal scheduling model, which is divided into two phases: construction and operation of the ESS. The efficacy of the proposed model is further corroborated through the selection of three industrial users based in Jiangsu Province. The analysis demonstrates that optimal scheduling can effectively curtail electricity costs for the users. © 2024 IEEE.

关键词： Integer programming

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Deep Reinforcement Learning Based Joint Partial Computation Offloading and Resource Allocation in Mobility-Aware MEC System

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China Communications 2022年第8期19卷 85-99页

作者： Luyao Wang Guanglin Zhang College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of EducationShanghai 201620China

Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G *** quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC *** energy harvested by energy harvesting equipments(EHQs)is considered as a promising power supply for users to process and offload *** this paper,we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage *** investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC *** propose an effective joint partial computation offloading and resource allocation(CORA)algorithm which is based on deep reinforcement learning(DRL)to obtain the optimal scheduling without prior knowledge of task arrival,renewable energy arrival as well as channel *** simulation results verify the efficiency of the proposed algorithm,which undoubtedly minimizes the cost of MDs compared with other benchmarks.

关键词： mobile edge computing energy harvesting device-mobility partial computation offloading resource allocation deep reinforcement learning

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Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

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China Communications 2023年第1期20卷 125-139页

作者： Wenqian Zhang Wenya Fan Guanglin Zhang Shiwen Mao College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of EducationShanghai 201620China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL 36849-5201USA

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among *** addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of *** this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain *** formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)*** address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)*** simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

关键词： cooperative mobile-edge computing blockchain workload offloading service caching load balancing deep reinforcement learning(DRL)

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A multi-scale dual moving horizon estimation method for state of charge of lithium-ion battery 42

A multi-scale dual moving horizon estimation method for stat...

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42nd Chinese Control Conference, CCC 2023

作者： Wen, Jiacheng Cai, Xin Ministry of Education College of Information Science and Technology Engineering Research Center of Digitized Textile & Apparel Technology Donghua University Shanghai201620 China

ISBN: (纸本)9789887581543

For the battery management system (BMS), accurate state of charge (SOC) estimation plays an important role in enhancing efficiency and ensuring safety of battery. Considering the tradeoff between accuracy of SOC and computational complexity, this paper proposes a multi-scale dual moving horizon estimation (MHE) algorithm, which incorporates a SOC estimator and a model parameters estimator using different time scales. Firstly, the state-space models of the SOC and parameters are built on basis of the equivalent circuit model and parametric sensitivity analysis, respectively. Secondly, the estimation problem of the model parameters is transformed into an optimization problem solved in a larger time scale used for the update of the SOC. Thirdly, the estimation problem of the SOC is solved in a smaller time scale used for the update of the model parameters. Finally, simulation results show that this method can effectively enhance accuracy and reduce computational burden compared with the methods such as MHE and dual EKF. The effect of the MHE window size on the SOC estimation is also discussed. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Lithium-ion batteries

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A Policy Gradient Based Particle Swarm Optimizer for Portfolio Optimization Problem 41

A Policy Gradient Based Particle Swarm Optimizer for Portfol...

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第41届中国控制会议

作者： Xuanyu Jia Xin Cai Engineering Research Center of Digitized Textile & Apparel Technology Ministry of EducationCollege of Information Science & TechnologyDonghua University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Due to complexity and uncertainty of financial market,how to reasonably distribute wealth amongst different assets to maximize return or minimize risk has long been a challenging research topic,which is usually called the portfolio optimization problem(POP).To deal with such difficulties,this paper proposes a policy gradient based particle swarm optimizer(PG-PSO).By combining PSO with policy gradient algorithm in reinforcement learning,the proposed method provides a novel PSO parameters adjustment mechanism which improved the optimization accuracy while at the same time reducing the workloads of manually configuring parameters.A policy neural network as an agent is constructed and interacts with the particle swarm to realize the adaptive update of PSO *** to the test results of several typical benchmark functions,it can be seen that PG-PSO has better performance compared with other algorithms mentioned in this *** experimental results of POP show that PG-PSO can also improve Sharpe ratio value of the entire portfolio more effectively.

关键词： Particle swarm optimization Policy gradient Reinforcement learning Portfolio optimization Sharpe ratio

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Time Series Prediction Problems under Covariate Drift 13

Time Series Prediction Problems under Covariate Drift

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Liang, Xiaoxue Hao, Kuangrong Chen, Lei Ren, Lihong Donghua University Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai China 201620 College of Information Sciences and Technology Donghua University Shanghai China 201620

ISBN: (纸本)9798350361674

In the real world, time series data are ubiquitous, and the prediction task of time series data is very important. Most real-world time series data do not satisfy the assumption of independent and identical distribution (i.i.d.) due to environmental changes, that is, the distribution of the training datasets is different from the distribution of the test datasets, P(Xi)= P(Xj), but the conditional distribution is usually considered to be unchanged, P(y|xi)= P(y|xj). In this case, it is defined as covariate drift. However, most of the existing prediction algorithms are based on the assumption of i.i.d., so these algorithms have great limitations in the prediction of time series data under covariant drift. Therefore, the AEIF-MLP model is proposed, a time series data prediction model based on MLP and causal structures. The model mainly consists of two modules. Based on the principle of maximum entropy, we propose an adaptive environment segmentation module to separate different environments in the training datasets. Based on the causal structure, we propose an invariant feature learning module to learn common invariant features in different environments to train the model to deal with test datasets of unknown distribution. In summary, this method solves the problem of time series prediction under covariate drift. The validity of the model is verified by drift data sets in different environments for the first time, and the validity of the model is further verified on two real data sets. © 2024 IEEE.

关键词： Prediction models

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Multi-lane Vehicular Platoons Merging Strategy Based on Distributed Model Predictive Control 41

Multi-lane Vehicular Platoons Merging Strategy Based on Dist...

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第41届中国控制会议

作者： Xu Zhu Xin Cai Engineering Research Center of Digitized Textile & Apparel Technology Ministry of EducationCollege of Information Science and TechnologyDonghua University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the scenarios of merging multi-lane vehicular platoons into a single lane are ***,the bicycle model is established as vehicle dynamics and the merging process is parameterized as the transitioning from any initial formation to a single-lane ***,the vehicles are numbered using a distance-related method and a target platoon is defined to describe their desired ***,a two-stage merging strategy is proposed and the DMPC controllers are respectively designed,where both control input constraints and collision avoidance constraints are ***,the effectiveness of the proposed method is verified by a numerical simulation.

关键词： Multi-lane platoon connected and automated vehicles distributed model predictive control

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Fast Charging Strategy of Lithium-ion Batteries with Thermal Safety Margin Based on Model Predictive Control 41

Fast Charging Strategy of Lithium-ion Batteries with Thermal...

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第41届中国控制会议

作者： Ganghua Jia Xin Cai Engineering Research Center of Digitized Textile & Apparel Technology Ministry of EducationCollege of Information Science and TechnologyDonghua University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The tradeoff between efficiency and safety is unavoidable when designing charging strategies for *** paper proposes an optimal fast charging strategy of Lithium-ion batteries taking account of charging efficiency and thermal safety based on model predictive control(MPC).Firstly,a polynomial function is introduced to approximate thermal safe boundary of Li-ion batteries,which reveals relationships between thermal-runaway initial temperature and state of charge(SOC).Then,based on an electric-thermal coupling model,fast and safe charging problem is formulated as an optimal output regulator problem accompanied with the thermal safety margin ***,MPC algorithm is developed to solve the constrained optimization *** result illustrates that the method enhances the charging efficiency while keeps the core temperature of batteries under a specified level.

关键词： Lithium-ion batteries fast charging thermal safety margin model predictive control

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