Control of plant-wide systems using dynamic supply rates

Tippett, Michael James; Bao, Jie

doi:10.1016/j.automatica.2013.09.028

Cited by 29 publications

(24 citation statements)

References 32 publications

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“…There are a number of challenges in control of these processes due to their large scales, geographically distribution (e.g., renewable energy generation networks), and the severe interactions between process units. While the material recycle loops and heat integration make more efficient use of materials and energy, they form positive feedback loops within the process network, causing control difficulties, for example, high sensitivity to disturbances or even plantwide instability . A plantwide process can be treated as a single complex system and controlled by a central multivariable controller.…”

Section: Introductionmentioning

confidence: 99%

Distributed plantwide control based on differential dissipativity

Wang

Bao

2016

Intl J Robust & Nonlinear

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Summary Modern chemical plants are becoming very complex, often consisting of a number of nonlinear process units (subsystems) with strong interactions due to material recycle and energy integration. The operation setpoint may need to be adjusted from time to time based on the market demand. To address the aforementioned challenges, a plantwide distributed nonlinear control scheme based on differential dissipativity is proposed in this paper, which can ensure plantwide incremental exponential stability and achieve bounded incremental L2 gain performance. As a non‐unique property, the differential dissipativity of individual subsystem is shaped by a setpoint‐independent control structure – differential state feedback control. The dissipativity properties of subsystems and individual controllers are determined simultaneously as a large‐scale feasibility problem to ensure the plantwide stability and performance. It is converted into an LMI condition for plantwide supply rate planning and small‐scale sum‐of‐squares programming problems for individual subsystem dissipativity shaping, by using the alternating direction method of multipliers method. The proposed approach is illustrated using a chemical reactor network with a recycle stream. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Distributed plantwide control based on differential dissipativity

Wang

Bao

2016

Intl J Robust & Nonlinear

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“…Dissipativity theory can be used as an effective tool for the quantitative stability and performance analysis of large‐scale interconnected systems, where the problem is decomposed into the analysis of the dissipativity of the subsystems and interconnection topology, (e.g., in Refs. and 21). A dissipativity‐based distributed control approach for a plantwide process systems was first developed by Xu and Bao, where the closed‐loop dissipativity constraint guarantees plantwide stability and minimum plantwide performance.…”

Section: Introductionmentioning

confidence: 99%

Dissipativity‐based distributed model predictive control with low rate communication

et al. 2015

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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. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3288–3303, 2015

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“…The disadvantage of this is that there is some restriction on the topology of the controller communication network. A related approach, which uses dynamic supply rates as a more general form of dissipativity is presented [96,97] . An advantage of this approach is that the more general supply rates allow for sharper stability and minimum performance bounds as compared to (Q, S, R) supply rates.…”

Section: Dissipativity and Passivity Based Dpcmentioning

confidence: 99%

Distributed control of chemical process networks

Tippett

Bao

2015

Int. J. Autom. Comput.

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Abstract:In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.

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Control of plant-wide systems using dynamic supply rates

Cited by 29 publications

References 32 publications

Distributed plantwide control based on differential dissipativity

Distributed plantwide control based on differential dissipativity

Dissipativity‐based distributed model predictive control with low rate communication

Distributed control of chemical process networks

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