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10th World Congress on Intelligent control and Automation (WCICA)

作者： Zheng, Yi Li, Shaoyuan Wang, Xiaobo Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Dept Automat Shanghai Peoples R China Baosteel Iron & Steel Co Ltd Ctr Res & Dev Shanghai 600 Peoples R China

ISBN: (纸本)9781467313988

In accelerated cooling (ACC) processes-large scale systems, since the starting temperature varies along plate, the cooling curves of plate-points are different to each other for obtaining the uniform characteristic of the whole plate. To satisfy the complicated control targets-the various cooling curves, and real-time control requirement, a novel method called Optimization Target Resetting based distributed model predictive control (OTR-DMPC) is proposed. The whole system is divided into many subsystems, and each subsystem is controlled by a local model predictive control (MPC) to decrease the computation cost. The neighborhood optimization is used to coordinate subsystem-based MPC to guarantee the global performance. Meanwhile, the optimization target of each subsystem-based MPC is recalculated in each control period according to the current starting temperature. The experimental results show the efficiency of the proposed method.

关键词： model predictive control distributed model predictive control Accelerated cooling Laminar cooling

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Nash equilibrium-based distributed predictive control strategy for thickness and tension control on tandem cold rolling system

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JOURNAL OF PROCESS control 2021年 97卷 92-102页

作者： Hu, Yunjian Sun, Jie Peng, Wen Zhang, Dianhua Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Liaoning Peoples R China

With the increasing demands on strip product quality, conventional control methods cannot break the cold rolling production bottleneck The development of advanced control algorithms provides theoretical support for the improvement of strip accuracy and product stability of the tandem cold rolling process. Considering the complex characteristics of the system, a distributed model predictive control (DMPC) strategy based on the Nash equilibrium is proposed in this study. A state space model including the rolling process with constraints is established. To improve the calculation speed, a Laguerre function is integrated in the control strategy, so as to reduce the parameter dimension. The DMPC online optimal control strategy based on the Nash equilibrium is proposed to solve the problem between the constrained multivariable complex system and online rolling optimization. Its performance and effectiveness are demonstrated through a series of experiments based on rolling data from industry. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Tandem cold rolling Thickness control Tension control Nash equilibrium distributed model predictive control

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A Pricing Rule for Third-Party Platoon Coordination Service Provider 13

A Pricing Rule for Third-Party Platoon Coordination Service ...

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13th Asian control Conference (ASCC)

作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Martensson, Jonas KTH Royal Inst Technol Sch Elect Engn & Comp Sci Integrated Transport Res Lab Digital Futures S-10044 Stockholm Sweden KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst S-10044 Stockholm Sweden Shanghai Jiao Tong Univ Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9788993215236

We model a platooning system including trucks and a third-party service provider that performs platoon coordination, distributes the platooning profit within platoons, and charges the trucks in exchange for its services. This paper studies one class of pricing rules, where the third-party service provider keeps part of the platooning profit each time a platoon is formed. Furthermore, we propose a platoon coordination solution based on distributed model predictive control in which the pricing rule is integrated. To evaluate the effect of the pricing on the platooning system, we perform a simulation over the Swedish road network. The simulation shows that the platooning rate and profit highly depend on the pricing. This suggests that pricing needs to be set carefully to obtain a satisfactory platooning system in the future.

关键词： Platoon coordination pricing rules profit-sharing distributed model predictive control

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distributed Robust Secondary control of Islanded Microgrids: Voltage, Frequency, and Power Sharing

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IEEE TRANSACTIONS ON POWER DELIVERY 2021年第4期36卷 2501-2509页

作者： Mottaghizadeh, Masood Aminifar, Farrokh Amraee, Turaj Sanaye-Pasand, Majid Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran *** Iran KN Toosi Univ Technol Dept Elect Engn Tehran 196976499 Iran

In this paper, a novel distributed robust secondary control is proposed to restore both voltage and frequency in islanded inverter-interfaced microgrids (mu Gs). The proposed method is superior particularly in the disaster ex-post resilient islanded operation of mu Gs where various unknown disruptions and uncertainties exacerbate the successful operation of conventional control schemes. Accordingly, the proposed control scheme is devised so as to accommodate the nonlinearity of mu G and non-ideality of communication networks such as time-varying delays, data packet losses, and link failures. For mu G voltage control, a distributed robust algorithm based on a model predictive control is developed. To do so, the Lyapunov-Razumikhin functional is exploited to analyze the stability of the proposed method, and sufficient conditions are then speculated by the linear matrix inequalities technique. For mu G frequency restoration and achieving accurate active power sharing, a finite-time distributed robust proportional-integral algorithm is presented. The effectiveness of the proposed method is verified by inclusive simulation studies of an islanded mu G in MATLAB/ Simulink.

关键词： Voltage control Frequency control Delays Communication networks Power system stability Power system dynamics Reliability distributed model predictive control feedback linearization secondary voltage and frequency control microgrid

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distributed predictive control of Multi-Agent Systems Based on Error Upper Bound Approach

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IEEE ACCESS 2021年 9卷 11470-11478页

作者： Zhu, Jialin Xue, Binqiang Qingdao Univ Coll Automat Qingdao 266071 Peoples R China Qingdao Univ Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China

In this paper, a novel distributed predictive control (DMPC) method for multi-agent systems based on error upper bounds is proposed. To reduce the communication burden, the error upper bound condition between the subsystem and the neighbor subsystems is calculated by introducing the min-max function from the local state error of neighbouring subsystems. Additionally, an improved coupling constraints to describe the relationship between the neighbouring subsystems is introduced. Then, the proposed DMPC algorithm with kinds of the constraints is given, including the terminal cost, the terminal set and the terminal controller. Furthermore, the feasibility of the proposed DMPC algorithm is analyzed and the stability conditions of multi-agent systems are derived. Finally, a numerical example is given to verify the effectiveness of the method.

关键词： Multi-agent systems Upper bound predictive control Couplings Cost function Prediction algorithms Lyapunov methods distributed model predictive control multi-agent systems error upper bound

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Cooperative Online Path Planning for UAVs Based on DMPC and Improved Grey Wolf Optimizer 41

Cooperative Online Path Planning for UAVs Based on DMPC and ...

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第41届中国控制会议

作者： Chuanlong Yang Zhongchang Liu Wei Zhang Wei Yue College of Marine Electrical Engineering Dalian Maritime University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Online path planning for multiple fixed-wing UAVs in 3 D aerial space is a complex optimization problem with high dimension and multiple constraints,which requires efficient solving *** this paper,distributed model predictive control(DMPC) method is adopted to accomplish online path planning for multiple *** order to reduce the computation time of DMPC,an improved grey wolf optimizer(GWO) is designed where pheromone factors are incorporated into GWO to strengthen the exploration ability of the original *** addition,pheromones are optimally selected based on the hierarchy of wolves to guide subsequent iterations so that the convergence speed of the optimizer can be *** examples show the superiorities of the proposed method by comparing with various existing optimizers.

关键词： path planning multiple UAVs distributed model predictive control improved grey wolf optimizer

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Multi-time scale optimization scheduling of microgrid considering source and load uncertainty

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 216卷

作者： Hou, Jianmin Yu, Weijie Xu, Zhihao Ge, Quanbo Li, Zhi Meng, Ying Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.

关键词： Multi -time scale Microgrid Optimal scheduling Distributionally robust optimization distributed model predictive control

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distributed MPC-Based Secondary control for Energy Storage Systems in a DC Microgrid

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第6期36卷 5633-5644页

作者： Yang, Qiufan Zhou, Jianyu Chen, Xia Wen, Jinyu Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

In this paper, a novel distributed model predictive control (DMPC) strategy based on voltage observer for multiple energy storage systems (ESs) is firstly proposed to achieve a tradeoff between voltage regulation and power sharing. In order to reduce the impact of communication delay on voltage observer, an improved DMPC consensus algorithm is proposed. This scheme improves the robustness of the system to the delay. Besides, a small-signal dynamic model of a DC microgrid with the improved DMPC controllers is established to analyze its dynamic performance. The time-domain simulation results are presented to verify that, under the constraints of the DC bus voltage, a flexible trade-off between voltage regulation and power distribution can be achieved. Besides, the communication delay problems can be reduced.

关键词： Voltage control Microgrids Observers predictive control Delays predictive models Heuristic algorithms DC microgrid distributed model predictive control secondary control power sharing

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distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urban Environment

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2021年第2期19卷 823-836页

作者： Hu, Chaofang Meng, Zhuo Qu, Ge Shin, Hyo-Sang Tsourdos, Antonios Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Cranfield Univ Sch Aerosp Transport & Mfg Cranfield Beds England

In this paper, a real-time distributed path planning method is developed for cooperatively tracking ground moving target in urban by multiple fixed-wing unmanned aerial vehicles (UAVs). For reasons of changeable movement of target, the commanded speed and turning rate of each UAV are both taken as control input variables. In urban environment, buildings may occlude the line of sight of on-board sensor. Hence the target coverage degree is proposed as objective function instead of distance. To save energy of UAV system as much as possible, the control input cost and sensor energy consumption are also taken as objectives. For preemptive priority requirement, the objective functions are fuzzified and the satisfactory degree order is designed to model priority. To guarantee the feasibility of solution, the varying domain is introduced to replace the strict order constraint. On this basis, generalized varying domain (GVD) method is developed to balance optimization and priority. In terms of the maneuverability of UAVs, the diverse constraints are considered, including real speed and turning rate, control input saturation, collision avoidance between UAVs, and obstacle avoidance between UAV and buildings. Consequently, distributed model predictive control (DMPC) strategy is designed to calculate the optimal path of each UAV, where the state information in finite period of UAV is transferred to the adjacent ones. The simulations show the effectiveness of proposed method by comparing with hierarchical optimization (HO).

关键词： distributed model predictive control path planning priority unmanned aerial vehicle

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Robust Data predictive control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty

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IEEE ACCESS 2021年 9卷 32390-32404页

作者： Brahmia, Ibrahim Wang, Jingcheng Xu, Haotian Wang, Hongyuan Turci, Luca De Oliveira Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Xian Technol Univ Autonomous Syst & Intelligent Control Int Joint R Xian 710021 Peoples R China

With the emerging technologies for Energy Intent (EI) and data-driven applications, the conventional power grid network is undergoing a radical modernization. An efficient energy management and electricity price forecasting remains a challenging task. In this paper, a new Robust Data predictive control framework for Energy Management System (RDPC-EMS) is developed to overcome the uncertainty of the electricity retail price market and minimize the total operating costs for the multi-microgrids (MMG) system. The proposed framework solves the economic energy dispatch based on an accurate Electricity Price Forecasting (EPF) by an Outlier-Robust Extreme Learning Machine (OR-ELM) algorithm and a two layers cooperative distributed model predictive control (DMPC). The First level provides an optimal energy scheduling between the Distribution System Operator (DSO) and cooperative microgrids systems to minimize the operating cost based on the forecasted electricity price. In contrast, second level maintains the supply-demand balance by applying the optimal energy scheduling from the first layer through an adjustment of the distributed energy resources (DER). The electricity retail price prediction is assessed using real dataset from the Iso New England electricity market. The OR-ELM regression method shows a significant forecasting performance in terms of error metrics. For instance, the mean absolute error in the training stage 2.05% for OR-ELM with a comparison of 4.17% and 6.29% for Support Vector Regression (SVR), and Artificial Neural Network (ANN) models respectively. Finally, simulation results demonstrate the efficiency RDPC-EMS for daily operating cost reduction, with decrease of 15% for MG 1 and 16% for MG 2.

关键词： Microgrids Energy management Optimization Forecasting predictive control Electricity supply industry Batteries distributed model predictive control energy management outlier robust extreme learning machine renewable energy sources retail electricity price market

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