Predictive Control Design for Large Scale Systems

Katebi, M.R.; Johnson, Michael

doi:10.1016/s1474-6670(17)46593-1

Cited by 24 publications

(31 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-agent MPC issues have been investigated since the 90s, such as in [1,2,5,15,17,19,22,26,37,39], and more recently in [9,11,14,24,25,33,46] and [32].…”

Section: Multi-agent Mpcmentioning

confidence: 99%

Intelligence in Transportation Infrastructures via Model-Based Predictive Control

Negenborn

Hellendoorn

2009

Intelligent Infrastructures

View full text Add to dashboard Cite

In this chapter we discuss similarities and differences between transportation infrastructures like power, road traffic, and water infrastructures, and present such infrastructures in a generic framework. We discuss from a generic point of view what type of control structures can be used to control such generic infrastructures, and explain what in particular makes intelligent infrastructures intelligent. We hereby especially focus on the conceptual ideas of model predictive control, both in centralized, single-agent control structures, and in distributed, multi-agent control structures. The need for more intelligence in infrastructures is then illustrated for three types of infrastructures: power, road, and water infrastructures. Transportation infrastructuresTransportation infrastructures, like power distribution networks [27], traffic and transportation systems [12], water distribution networks [7], logistic operations networks [28], etc., (see Figures 1.1-1.3) are the corner stones of our modern society. A smooth, efficient, reliable, and safe operation of these systems is of huge importance for the economic growth, the environment, and the quality of life, not only when the systems are pressed to the limits of their performance, but also under regular operating conditions. Recent examples illustrate this. E.g., the problems in the USA and Canada [44], Italy [42], Denmark and Sweden [16], The Netherlands, Germany, Belgium, and France [43], and many other countries [36] due to power outages have shown that as power network operation gets closer to its limits, small disturbances in heavily loaded lines can lead to large black-outs causing not only huge economic losses, but also mobility problems as trains and metros may not be able to operate. Also, as road traffic operation gets closer to its limits, unexpected situations in road traffic networks can lead to heavy congestion. Not only the huge traffic congestion after incidents such as bomb alerts are examples of this, also the almost daily road-traffic jams due to accidents illustrate this convincingly.Expanding the physical infrastructure of these networks could help to relieve the issues in transportation networks, although at extremely high costs. As alternative to spending this money on building new infrastructure, it is worth spending effort on investigating improved use of the existing infrastructure by employing intelligent control techniques that combine state-of-the-art techniques from fields like systems and control engineering [4], optimization [6], and multi-agent systems [47], with domain-specific knowledge.The examples of networks just mentioned are only some particular types of networks within the much larger class of transportation networks. Common to transportation networks is that at a generic level they can be seen as a set of nodes, representing the components or elements of the network, and interconnections between these nodes. In addition, transportation networks have some sort of commodity, that is brought into the network at s...

show abstract

“…Multi-agent MPC issues have been investigated since the 90s, such as in [1,2,5,15,17,19,22,26,37,39], and more recently in [9,11,14,24,25,33,46] and [32].…”

Section: Multi-agent Mpcmentioning

confidence: 99%

Intelligence in Transportation Infrastructures via Model-Based Predictive Control

Negenborn

Hellendoorn

2009

Intelligent Infrastructures

View full text Add to dashboard Cite

show abstract

“…The theoretical research in multi-agent MPC started in the 90s (Aicardi et al, 1992;Acar, 1992;Katebi and Johnson, 1997;Krogh, 2001, 2002;Camponogara et al, 2002), with applications to water distribution systems (Georges, 1999), delivery canals (Sawadogo et al, 1998), irrigation systems (El Fawal et al, 1998), multi-reach canals (Gomez et al, 1998), dynamic routing (Baglietto et al, 1999), cascading failures in power networks (Hines et al, 2005), distributed vehicle coordination (Dunbar and Murray, 2006), and distributed emergency voltage control (Beccuti and Morari, 2006).…”

Section: Multi-agent Mpcmentioning

confidence: 99%

Multi-Agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes

Negenborn

Schutter

Hellendoorn

2006

IFAC Proceedings Volumes

124

View full text Add to dashboard Cite

We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed using multiple intelligent agents. We consider multi-agent control schemes in which each agent employs a model-based predictive control approach. Coordination between the agents is used to improve decision making. This coordination can be in the form of parallel or serial schemes. We propose a novel serial coordination scheme based on Lagrange theory and compare this with an existing parallel scheme. Experiments by means of simulations on a particular type of transportation network, viz., an electric power network, illustrate the performance of both schemes. It is shown that the serial scheme has preferable properties compared to the parallel scheme in terms of the convergence speed and the quality of the solution.

show abstract

“…In recent years, several works have been proposed based on model-predictive control strategies and physical plant models [1][2][3][4][5][6]. The work by [1] is based on a linear plant model and Generalised Predictive Control (GPC) in a combined-cycle power plant.…”

Section: Introductionmentioning

confidence: 99%

“…The work by [1] is based on a linear plant model and Generalised Predictive Control (GPC) in a combined-cycle power plant. The proposed control structure is based on a two-level GPC scheme where the solutions for the low-level GPC problems are solved before updating the high-level GPC coordinator.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Power Plant Master Control

Thomas

2014

Automatika

View full text Add to dashboard Cite

In this paper a coordinated master control for a solid fuel power plant has been developed and the performance evaluated in terms of tracking capability, stability and robustness. The control strategy has been model-based predictive control (MPC) and it was evaluated on a nonlinear process model of the Vattenfall power plant Idbäcken in Nyköping, Sweden. The developed master MPC with gain scheduling has better performance compared to the existing PID controller which has been thoroughly studied and tuned in a previous project. The robustness of the proposed master MPC controller against common disturbances and parameter variation has been investigated and it shows that the proposed controller is more robust than the existing PID controller.Key words: Master Predictive Control, Solid fuel power plant, Gain scheduling.Nadre eno modelsko upravljanje elektranom. U ovome radu razvijeno je koordinirano nadre eno upravljanje za elektrane na kruta goriva te su provjereni pokazatelji kvalitete u smislu praćenja, stabilnosti i robustnosti. Koristi se modelsko prediktivno upravljanje koje je provjereno na nelinearnom modelu procesa Vattenfall elektrane Idbäcken u Nyköping, Švedska. Razvijeni nadre eni MPC s prekapčanjem ima bolje performanse u usporedbni s PID regulatorom koji je bio temeljito proučen i podešen u prijašnjem projektu. Istražena je robustnost predloženog nadre enog MPC-a u odnosu na šum i promjene parametara. Pokazuje se da je predloženi regulator robustniji od postojećeg PID regulatora.Ključne riječi: nadre eno prediktivno upravljanje, elektrane na kruta goriva, upravljanje s prekapčanjem

show abstract

Predictive Control Design for Large Scale Systems

Cited by 24 publications

References 7 publications

Intelligence in Transportation Infrastructures via Model-Based Predictive Control

Intelligence in Transportation Infrastructures via Model-Based Predictive Control

Multi-Agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes

Model-Based Power Plant Master Control

Contact Info

Product

Resources

About