Distributed Consensus of Multi-Agent Systems With Input Constraints: A Model Predictive Control Approach

Cheng, Zhaomeng; Zhang, Haitao; Fan, Menghua; Chen, Guanrong

doi:10.1109/tcsi.2014.2367575

Cited by 108 publications

(58 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [71][72][73][74], a model predictive control is presented to accelerate the convergence speed to realize dynamic consensus of the MAS. In [75], a distributed secondary voltage control strategy based on the dynamic consensus protocol is established.…”

Section: K P T P T C Ve T Ve T B P T P T Ve T Ve Tmentioning

confidence: 99%

See 1 more Smart Citation

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

367

182

View full text Add to dashboard Cite

Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

show abstract

Section: K P T P T C Ve T Ve T B P T P T Ve T Ve Tmentioning

confidence: 99%

“…The realization of consensus in MAS is one of the most important direction to achieve the coordinated control. In this paper, the MAS-based consensus control schemes are systematically reviewed [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77].…”

Section: Introductionmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

367

182

View full text Add to dashboard Cite

show abstract

“…Here, the closed-loop analysis is based on the explicit solution to the respective optimization problems, which is characterized for the case that no constraints are present. A similar approach is taken in [63], where consensus for doubleintegrators is shown in the presence of input constraints which are active only a finite number of times, and in [64], where flocking of double-integrators is considered. In [65], for general linear systems, the authors calculate an optimal consensus point at each time step by iteratively solving a centralized optimization problem, where this point is used as setpoint in the MPC formulation.…”

Section: Distributed Mpc For General Cooperative Control Tasksmentioning

confidence: 99%

Economic and Distributed Model Predictive Control: Recent Developments in Optimization-Based Control

Müller

Allgöwer

2017

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

show abstract

“…There are no unified methods for developing these models. This is due to a large variety of control object's types [1][2][3][4][5][6][7][8][9]: static and dynamic, continuous and discrete, deterministic and stochastic, etc. Each method starts with data wrangling.…”

Section: Introductionmentioning

confidence: 99%

Real-time control data wrangling for development of mathematical control models of technological processes

Vasilyeva

Koteleva

Fedorova

2018

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Abstract. The relevance of the research is due to the need to stabilize the composition of the melting products of copper-nickel sulfide raw materials in the Vanyukov furnace. The goal of this research is to identify the most suitable methods for the aggregation of the real time data for the development of a mathematical model for control of the technological process of melting copper-nickel sulfide raw materials in the Vanyukov furnace. Statistical methods of analyzing the historical data of the real technological object and the correlation analysis of process parameters are described. Factors that exert the greatest influence on the main output parameter (copper content in matte) and ensure the physical-chemical transformations are revealed. An approach to the processing of the real time data for the development of a mathematical model for control of the melting process is proposed. The stages of processing the real time information are considered. The adopted methodology for the aggregation of data suitable for the development of a control model for the technological process of melting copper-nickel sulfide raw materials in the Vanyukov furnace allows us to interpret the obtained results for their further practical application. IntroductionThe mathematical models are becoming more significant in researching metallurgical processes and solving optimization tasks. They make visible the correlation between input variables and output variables. There are no unified methods for developing these models. This is due to a large variety of control object's types [1-9]: static and dynamic, continuous and discrete, deterministic and stochastic, etc. Each method starts with data wrangling. There are several methods of technological data wrangling for modeling [7][8][9], but they are not determinated for metallurgical processes.The goal of this research is to determine the most suitable methods for wrangling technological data for developing and using a mathematical model of the technological process of melting coppernickel sulfide raw materials in the Vanyukov furnace. The control process data of the Vanyukov process during 5 months were examined. The analysis of the operational control data of the Vanyukov process showed (Figure 1) that the copper content in matte varies between 46 and 68%. The variation range is more than 20% while the average copper content in matte is 58%. This indicates to the heterogeneity of data (a large variety in data relative to a mean value), low predictability of copper content in matte and low stability of the process. The low stability of the process makes the predictability difficult. So the data of these processes have to be prepared before modelling.

show abstract

Distributed Consensus of Multi-Agent Systems With Input Constraints: A Model Predictive Control Approach

Cited by 108 publications

References 33 publications

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Economic and Distributed Model Predictive Control: Recent Developments in Optimization-Based Control

Real-time control data wrangling for development of mathematical control models of technological processes

Contact Info

Product

Resources

About