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IEEE Green Technologies Conference (GreenTech)

作者： Eini, Roja Abdelwahed, Sherif Virginia Commonwealth Univ Dept Elect Engn Richmond VA 23284 USA Virginia Commonwealth Univ Dept Elect Engn Richmond VA USA

ISBN: (纸本)9781728114576

In this paper, a distributed model predictive control (DMPC) strategy is developed for a multi-zone building plant with disturbances. The control objective is to maintain each zone's temperature at a specified level with the minimum cost of the underlying HVAC system. The distributed predictive framework is introduced with stability proofs and disturbances prediction, which have not been considered in previous related works. The proposed distributed MPC performed with 48% less computation time, 25.42% less energy consumption, and less tracking error compared with the centralized MPC. The controlled system is implemented in a smart building test bed.

关键词： distributed model predictive control Multi-zone building Temperature control HVAC Smart building

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distributed model predictive control for Intelligent Traffic System 12

Distributed Model Predictive Control for Intelligent Traffic...

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IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green Computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of Things (iThings) / 5th IEEE Int Conf on Smart Data

作者： Eini, Roja Abdelwahed, Sherif Virginia Commonwealth Univ Dept Elect Engn Richmond VA 23284 USA

ISBN: (纸本)9781728129808

This paper proposes a distributed model predictive control (DMPC) approach for an urban traffic network (UTN) system. The control objective is to minimize the traffic congestion and the total travel time spent (TTS) in each link. The proposed DMPC algorithm considers traffic demand and disturbance predictions. The CasADi optimization tool is used to solve the constrained optimization problem. The proposed distributed control approach achieved 60% less computation time, 14.3% less TTS, and 15.1% less queue length compared to the centralized approach. Moreover, while the centralized algorithm neglected the input and state constraints, the distributed approach resulted in the satisfaction of all the constraints over the whole horizon.

关键词： distributed model predictive control Traffic management and optimization Intelligent transport systems and control Smart city

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distributed model predictive control for Automated Intersection with Multiple Maneuvers

Distributed Model Predictive Control for Automated Intersect...

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16th IEEE Vehicle Power and Propulsion Conference (VPPC)

作者： Wang, Kaizheng Wang, Yafei Wang, Lin Wu, Jingkai Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781728112497

Intersections play a vital role in traffic management. Vehicle-to-Vehicle (V2V) communication brings breakthroughs to the safety and efficiency of intersections. In this paper, a distributed model predictive control approach is proposed which enables multiple vehicles cross the intersection simultaneously without collision. A simplified vehicle dynamic model with longitudinal and lateral motions coupled is utilized in local controller to describe the dynamic properties of the vehicle. A scenario where several vehicles coordinate to cross an intersection accounting for straight and turning maneuver is considered. In this case, the motions generated by vehicles locally in time are relied on optimization of the problem, incorporating constraints based on collision avoidance and local vehicle dynamic. Finally, the effectiveness of the approach is verified in CarSim and MATLAB co-simulation.

关键词： Automated urban intersection V2V distributed model predictive control CarSim

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distributed stochastic model predictive control for systems with stochastic multiplicative uncertainty and chance constraints

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ISA TRANSACTIONS 2022年 121卷 11-20页

作者： Wang, Hongyuan Wang, Jingcheng Xu, Haotian Zhao, Shangwei Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Dept Automat Shanghai 200240 Peoples R China

The rapid development of technology and economy has led to the development of chemical processes, large-scale manufacturing equipment, and transportation networks, with their increasing complexity. These large systems are usually composed of many interacting and coupling subsystems. Moreover, the propagation and perturbation of uncertainty make the control design of such systems to be a thorny problem. In this study, for a complex system composed of multiple subsystems suffering from multiplicative uncertainty, not only the individual constraints of each subsystem but also the coupling constraints among them are considered. All the constraints with the probabilistic form are used to characterize the stochastic natures of uncertainty. This paper first establishes a centralized model predictive control scheme by integrating overall system dynamics and chance constraints as a whole. To deal with the chance constraint, based on the concept of multi-step probabilistic invariant set, a condition formulated by a series of linear matrix inequality is designed to guarantee the chance constraint. Stochastic stability can also be guaranteed by the virtue of nonnegative supermartingale property. In this way, instead of solving a non-convex and intractable chance-constrained optimization problem at each moment, a semidefinite programming problem is established so as to be realized online in a rolling manner. Furthermore, to reduce the computational burdens and amount of communication under the centralized framework, a distributed stochastic model predictive control based on a sequential update scheme is designed, where only one subsystem is required to update its plan by executing optimization problem at each time instant. The closed-loop stability in stochastic sense and recursive feasibility are ensured. A numerical example is employed to illustrate the efficacy and validity of the presented algorithm in this study.

关键词： distributed model predictive control Stochastic model predictive control Couple probabilistic constraints Probabilistic invariance Chance-constrained optimization

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distributed model predictive control of Wind Farms for Short-Term Grid Support

Distributed Model Predictive Control of Wind Farms for Short...

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IEEE PES Innovative Smart Grid Technologies Europe (IEEE ISGT-Europe)

作者： Theissen, Moritz Stursberg, Olaf Univ Kassel Control & Syst Theory Kassel Germany

ISBN: (纸本)9781538682180

The increasing share of power generation from inverter-coupled sources leads to faster transients in the power grid and makes it necessary to provide new methods for wind farms also to participate in grid support for short-term disturbances. This paper proposes to use distributed model predictive control of wind farms allowing to compensate such disturbances better than established control schemes, while considering relevant constraints on states and inputs.

关键词： wind farm doubly-fed induction generator distributed model predictive control disturbance transient stability

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Accelerated distributed model predictive control for HVAC systems

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control ENGINEERING PRACTICE 2021年 110卷 104782-104782页

作者： Chen, Liangliang Zhang, Ying Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This paper investigates the accelerated distributed model predictive control (MPC) strategy for the heating, ventilation and air conditioning (HVAC) systems with local and global power input constraints. The problems are firstly formulated in the distributed MPC framework and then the constrained optimization is converted into a quadratic programming problem. In the problem formulation, the thermal couplings between immediate neighboring zones are considered while designing the distributed controller, and the unknown thermal disturbances are incorporated by the robust optimization scheme. Then, using the accelerated dual gradient-projection method, a distributed fast MPC protocol is designed for HVAC systems considering both the electricity cost and occupant comforts. A distributed stopping criterion based on the distributed average consensus algorithm is utilized. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed distributed MPC algorithm, and its computational advantages comparing with an existing distributed method and a centralized algorithm.

关键词： distributed model predictive control Fast dual gradient algorithm HVAC systems Thermal couplings distributed average consensus

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Study on the distributed model predictive control for multi-zone buildings in personalized heating

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ENERGY AND BUILDINGS 2021年 231卷 110627-110627页

作者： Li, Zhiwei Zhang, Jili Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China

To meet demands of smart heating development, the novel dynamic regulation methods of heating systems need to be pursued. This paper presents a model predictive control (MPC) approach for room temperature regulation in multi-zone buildings. Among them, a) For heating systems with nonlinearity and large time delay, a hybrid optimization algorithm consisting of the improved particle swarm optimization (IPSO) algorithm and Newton-Raphson (NR) method in online optimization of MPC for a single zone building is proposed. Compared with the quadratic programming method, it can identify successfully the optimal control vector. b) A distributed MPC approach is presented for multi-zone buildings existing hydraulic coupling. Firstly, Nash-optimization acts on searching the global equilibrium point in the distributed structure, then the hybrid optimization algorithm works in the online optimization of these local MPCs. Compared with the decentralized MPC, the control performance is improved through co-operating and communicating between subsystems via network to share information related to their future behavior. Besides, the global performance index in the distributed MPC is redefined as the weighted sum of energy consumption and thermal comfort, which provides the possibility to eliminate quickly the most unfavorable thermodynamic loop in heating systems and reduce energy consumption. (C) 2020 Published by Elsevier B.V.

关键词： Personalized heating distributed model predictive control Hybrid optimization algorithm Nash-optimization Energy saving

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The target capturing problem for multi-agent double-integrator systems: a distributed model predictive control scheme 25

The target capturing problem for multi-agent double-integrat...

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25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Fedele, Giuseppe Franze, Giuseppe Univ Calabria Dept Informat Modeling Elect & Syst Engn Arcavacata Di Rende CS Italy

ISBN: (纸本)9781728189567

In this paper, a target capturing problem for multi-agent systems described by double-integrator dynamics and time-varying communication network has been considered. The main aim consists in ensuring that the agents are indefinitely confined into a so-called containment region by jointly lying outside the distancing set and keeping this property for all future time instants. Here, this problem has been addressed by means of a distributed control architecture based on two ingredients: a new description of the kinematic evolution of the multi-agent system and an adequate customization of model predictive control ideas in a distributed fashion. As one of its main merits, the resulting scheme ensures synchronization capabilities in finite time for all the involved agents.

关键词： Multi-agent systems Target capturing distributed model predictive control Synchronization

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A distributed model predictive control algorithm with the gap metric output feedback decoupling

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COMPUTERS & CHEMICAL ENGINEERING 2021年 145卷 107167-107167页

作者： Wang, Jiaorao Xu, Zuhua Song, Chunyue Yao, Yi Zhao, Jun Zhejiang Univ Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

A distributed model predictive control (DMPC) algorithm with the gap metric output feedback decoupling is proposed. For large-scale systems including strongly coupled subsystems and inaccessible states, we initially address the gap metric output feedback decoupling to weakly decouple the subsystems. The gap metric is used to analyze the similarity between the original subsystems and the decoupled ones so that subsystems' interactions are decreased while their dynamic characteristics are unchanged. With the decoupling, reducing the communication between controllers will not limit control performance. Then a new transmission strategy is developed to improve computational efficiency of DMPC. At each sampling time, controllers communicate with each other only once and solve local control laws based on the previous optimal solutions. Furthermore, the output feedback is weighted as an input compensation to reduce the influence of model mismatch. Case studies on a reactor separator process demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Large-scale systems distributed model predictive control Dynamic matrix control Gap metric Output feedback decoupling

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Online Trajectory Planning and Collision Avoidance for a Group of Robots Using distributed model predictive control 28th

Online Trajectory Planning and Collision Avoidance for a Gro...

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28th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Costantini, Giuliano Rostami, Ramin Goerges, Daniel Univ Kaiserslautern Dept Elect & Comp Engn Erwin Schrodinger Str 12 D-67663 Kaiserslautern Germany

ISBN: (纸本)9783030196486;9783030196479

In this paper the trajectory planning problem for unicycle robots is studied. This problem is particularly difficult to solve online due to its inherently nonlinear and nonconvex structure. The first aim of the paper is to overcome these difficulties using model predictive control (MPC) when a single robot is considered. Several requirements must be fulfilled by the optimal trajectory, such as respecting physical limitations as well as track constraints. The second aim of the paper is to extend the results to a group of robots using distributed MPC providing formation control and inter-robot collision avoidance.

关键词： Autonomous vehicles Unicycle robots Trajectory planning Collision avoidance distributed model predictive control

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