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Real-Time distributed Economic model predictive control for Complete Vehicle Energy Management

ENERGIES 2017年第8期10卷 1096页

作者： Romijn, Constantijn Donkers, Tijs Kessels, John Weiland, Siep Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands DAF Trucks NV Vehicle Control Grp NL-5643 TW Eindhoven Netherlands

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

关键词： energy management hybrid vehicles distributed model predictive control dual decomposition auxiliaries

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Impacted-Region Optimization for distributed model predictive control Systems With Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第4期12卷 1447-1460页

作者： Li, Shaoyuan Zheng, Yi Lin, Zongli Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

For a large-scale distributed system, distributed model predictive control (DMPC) is a method of choice because of its ability to explicitly accommodate constraints and to achieve good dynamic performance. In the design of a DMPC, guaranteeing stability with a strong global performance is known to be a challenge. In this paper, we consider a large-scale distributed system whose input is constrained to given sets in their respective spaces and propose a stabilizing DMPC design, where each sub-system-based model predictive control (MPC) optimizes the cost function of the entire system over the region it directly impacts on. Consistency constraints and stability constraints, which bound the estimation errors of the interaction sequences among subsystems, are designed to guarantee that, if an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and that the closed-loop system is asymptotically stable. A key feature of the proposed DMPC is that it coordinates the MPCs of the subsystems by redefining the impact region of a subsystem according to the coordination strategy. Simulation results show that the performance of the proposed DMPC is very close to that of a centralized MPC.

关键词： Constrained control distributed model predictive control impacted-region optimization large-scale systems model predictive control plant-wide optimization

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Dissipativity-based distributed model predictive control with low rate communication

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AICHE JOURNAL 2015年第10期61卷 3288-3303页

作者： Zheng, Chaoxu Tippett, Michael James Bao, Jie Liu, Jinfeng Univ New S Wales Sch Chem Engn Sydney NSW 2052 Australia Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

distributed or networked model predictive control (MPC) can provide a computationally efficient approach that achieves high levels of performance for plantwide control, where the interactions between processes can be determined from the information exchanged among controllers. distributed controllers may exchange information at a lower rate to reduce the communication burden. A dissipativity-based analysis is developed to study the effects of low communication rates on plantwide control performance and stability. A distributed dissipativity-based MPC design approach is also developed to guarantee the plantwide stability and minimum plantwide performance with low communication rates. These results are illustrated by a case study of a reactor-distillation column network. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 3288-3303, 2015

关键词： distributed model predictive control plantwide control dissipativity

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Reconfigurable distributed model predictive control

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CHEMICAL ENGINEERING SCIENCE 2015年 136卷 2-19页

作者： Tippett, Michael James Bao, Jie Univ New S Wales Sch Chem Engn Sydney NSW 2052 Australia

An approach to reconfigurable distributed model predictive control based on reconfigurable controller dissipativity properties is developed. The dissipativity properties of the controllers are updated online to reconfigure themselves for changes in the process network topology, which may be due to changing product specifications, feedstock type or scheduled or unscheduled maintenance;allowing for more flexible and agile manufacturing processes. The use of dissipative systems theory allows for the interaction effects between individual processes to be taken into account in control design to achieve high levels of plant-wide performance. Plant-wide performance and stability bounds are developed based on dissipative systems theory, which in turn are translated into the dissipative trajectory conditions on each local controller. This approach is enabled by the use of dynamic supply rates in quadratic difference form parameterised as linear functions of the process network structure. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Plantwide control Reconfigurable distributed control distributed model predictive control Dissipativity Process network topology Flexible manufacturing

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A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

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JOURNAL OF PROCESS control 2015年 34卷 56-69页

作者： Zakharov, Alexey Zattoni, Elena Yu, Miao Jamsa-Jounela, Sirkka-Liisa Aalto Univ Dept Biotechnol & Chem Technol Sch Chem Technol FI-00076 Aalto Finland Univ Bologna Dept Elect Elect & Informat Engn G Marconi Alma Mater Studiorum I-40136 Bologna Italy

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Fault tolerant control controller reconfiguration Constrained optimization Alkylation of benzene

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Scenario-Based distributed model predictive control for Freeway Networks

Scenario-Based Distributed Model Predictive Control for Free...

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IEEE International Conference on Intelligent Transportation Systems

作者： Shuai Liu Anna Sadowska Hans Hellendoorn Bart De Schutter Delft Center for Systems and Control Delft University of Technology Schlumberger Gould Research Center

ISBN: (纸本)9781509018901

In this paper we develop a scenario-based distributed model predictive control (DMPC) approach for large-scale freeway networks. The uncertainties in a large-scale freeway network are categorized into global uncertainties for the overall network and local uncertainties for subnetworks. A reduced scenario tree is proposed, consisting of global scenarios and a reduced local scenario tree. For handling uncertainties in the scenario-based DMPC problem, a min-max setting is considered. A case study is implemented for investigating the scenario-based DMPC approach, and the results show that in the presence of uncertainties it is effective in improving the control performance with the queue length constraint being satisfied.

关键词： distributed model predictive control control performance Freeway Dimyristoylphosphatidylcholine Uncertainty

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Cooperation-Driven distributed model predictive control for Energy Storage Systems

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IEEE TRANSACTIONS ON SMART GRID 2015年第6期6卷 2583-2585页

作者： Meng, Ke Dong, Zhao Yang Xu, Zhao Weller, Steven R. Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Changsha Univ Sci & Technol Int Coll Changsha 410114 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Hong Kong Peoples R China Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia

In this letter, a distributed model predictive control strategy for battery energy storage systems is proposed to regulate voltage in distribution network with high-renewable penetration. control actions are calculated based on communication between interconnected neighboring subsystems and a multistep receding optimization, also considering system and battery constraints. The proposed approach is shown to be highly effective through a simulation case study, indicating high potential for applications.

关键词： Active distribution network distributed model predictive control energy storage system

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Cooperative distributed model predictive control for wind farms

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OPTIMAL control APPLICATIONS & METHODS 2015年第3期36卷 333-352页

作者： Spudic, V. Conte, C. Baotic, M. Morari, M. Univ Zagreb Fac Elect Engn & Comp HR-10000 Zagreb Croatia ETH Automat Control Lab CH-8092 Zurich Switzerland

This paper focuses on cooperative distributed model predictive control (MPC) of wind farms, where the farms respond to active power control commands issued by the transmission system operator. A distributed MPC scheme is proposed, which aims at satisfying the requirements imposed by the grid code while minimizing the farm-wide mechanical structure fatigue. The distributed MPC control law is defined by a global finite-horizon optimal control problem, which is solved at every time step by distributed optimization. The computational approach is completely distributed, that is, every turbine evaluates its own globally optimal input by considering local measurements and communicating to neighboring turbines only. Two MPC versions are compared, in the first of which the farm-wide power output constraint is implemented as a hard constraint, whereas in the second, it is implemented as a soft constraint. As for distributed optimization methods, the alternating direction method of multipliers as well as a dual decomposition scheme based on fast gradient updates are compared. The performance of the proposed distributed MPC controller, as well as the performance of the distributed optimization methods used for its operation, are compared in the simulation on four exemplary scenarios. The results of the simulations imply that the use of cooperative distributed MPC in wind farms is viable both from a performance and from a computational viewpoint. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： wind energy distributed model predictive control distributed optimization

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Cooperative distributed model predictive control via Linear Programming-A Divide and Conquer Approach

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IFAC-PapersOnLine 2015年第23期48卷 308-313页

作者： Kong, He Longo, Stefano Centre for Automotive Engineering and Technology Cranfield University College Road Cranfield BedfordMK43 0AL United Kingdom

Motivated by the recent progress in centralized model predictive control (MPC) design using a linear program (LP) and distributed MPC with quadratic program (QP) formulations, this paper discusses the design of LP-based cooperative distributed MPC for large scale linear systems with coupled dynamics and decoupled input constraints. As such, we examine the applicability of the Divide and Conquer approach to QP-based cooperative distributed MPC, recently proposed by the authors, to the LP-based case. It is shown that within the Divide and Conquer framework, an upbound of the original LP-based cooperative cost function (instead of itself), can be optimized for computing local inputs, resulting in suboptimal policies. However, advantages of doing so include the independency of the computation of local inputs and a large amount of communication burden reduction, as guaranteed by theoretical analysis and illustrated through numerical simulation. We also present closed-loop stability analysis and point out some questions worth further investigation. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： model predictive control Closed loop control systems Cost functions distributed parameter control systems Linear programming Linear systems predictive control systems Quadratic programming Centralized models Closed loop stability analysis Constrained controls distributed control distributed model predictive control Divide and conquer approach Large scale linear systems predictive control

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distributed model predictive control Based on Nash Optimality for Large Scale Irrigation Systems ∗

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IFAC-PapersOnLine 2015年第8期48卷 551-555页

作者： Rongchao Zhang Andong Liu Li Yu Wenan Zhang Department of Automation Zhejiang University of Technology Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou 310023 P.R. China

Irrigation system is a large scale system, consisting of many interacting channels, and spanning vast geographical areas. In practice, the water level is expected to the desired reference values for safety and efficiency. In this paper, the model of irrigation system is modeled by the principle on conservation of mass. Because of the strong coupling between neighboring pools in irrigation system and large disturbance from the environment, a non-cooperative distributed model predictive control (NDMPC) algorithm based on Nash optimality is proposed for the large scale irrigation system to design the controller for setting points regulation. Finally, a simulation example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： distributed model predictive control Irrigation systems Nash optimality Large scale systems

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