Coordinating multiple optimization-based controllers: New opportunities and challenges

Rawlings, James B.; Stewart, Brett T.

doi:10.1016/j.jprocont.2008.06.005

Cited by 298 publications

(185 citation statements)

References 12 publications

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“…[6,11,16,19,20,23], among others. However, in order to apply decentralized or distributed MPC approaches to LSS, there is a prior problem to be solved: the system decomposition into subsystems.…”

Section: R E V I S E Dmentioning

confidence: 99%

Partitioning Approaches for Large-Scale Water Transport Networks

Ocampo‐Martínez

Puig²

2017

Advances in Industrial Control

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Section: R E V I S E Dmentioning

confidence: 99%

Partitioning Approaches for Large-Scale Water Transport Networks

Ocampo‐Martínez

Puig²

2017

Advances in Industrial Control

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“…Luyben 2004) emphasize the simplicity and robustness of the conventional multi-loop control systems and criticize the reliability and costs of modern types. On the other side of this discussion, other researchers (Rawlings 2008;Stephanopoulos 2000) argue the economic advantages of model-based control systems and their systematic approach for handling constraint violations. In addition, they criticize the economic disadvantages of the constant-setpoint policy in decentralized control systems.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Optimal selection of control structure using a steady-state inversely controlled process model

Sharifzadeh

Thornhill

2012

Computers & Chemical Engineering

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The profitability of chemical processes strongly depends on their control systems. The design of a control system involves selection of controlled and manipulated variables, known as control structure selection. Systematic generation and screening alternative control structures requires optimization. However, the size of such an optimization problem is much larger when candidate controllers and their parameters are included and it rapidly becomes intractable. This paper presents a novel optimization framework using the notion of perfect control, which disentangles the complexities of the controllers. This framework reduces the complexity of the problem while ensuring controllability. In addition, the optimization framework has a goal-driven multi-objective function and requires only a steady-state inverse process model. Since dynamic degrees of freedom do not appear in a steady-state analysis, engineering insights are employed for developing the inventory control systems. The proposed optimization framework was demonstrated in a case study of an industrial distillation train.

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“…Constraints (3c) and (3h) are a key element of the proposed two-tier control architecture. In general, guaranteeing closed-loop stability of a distributed control scheme is a difficult task because of the interactions between the different controllers and can only be done under certain assumptions (see, for example, Rawlings and Stewart, 2007;Camponogara et al, 2002). Constraint (3c) guarantees that the upper-tier controller takes into account the effect of the lower-tier controller to the applied inputs (recall that the lower-tier controller is designed without taking u d into account).…”

Section: Upper-tier Lmpc Designmentioning

confidence: 99%

A two-tier architecture for networked process control

Liu

Peña

Ohran

et al. 2008

Chemical Engineering Science

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Traditionally, process control systems utilize dedicated, point-to-point wired communication links using a small number of sensors and actuators to regulate appropriate process variables at desired values. While this paradigm to process control has been successful, chemical plant operation could substantially benefit from an efficient integration of the existing, point-to-point control networks (wired connections from each actuator/sensor to the control system using dedicated local area networks) with additional networked (wired or wireless) actuator/sensor devices. However, augmenting existing control networks with real-time wired/wireless sensor and actuator networks challenges many of the assumptions made in the development of traditional process control methods dealing with dynamical systems linked through ideal channels with flawless, continuous communication. In the context of control systems which utilize networked sensors and actuators, key issues that need to be carefully handled at the control system design level include data losses due to field interference and time delays due to network traffic. Motivated by the above technological advances and the lack of methods to design control systems that utilize hybrid communication networks, in the present work, we present a novel two-tier control architecture for networked process control problems that involve nonlinear processes and heterogeneous measurements consisting of continuous measurements and asynchronous, delayed measurements. This class of control problems arises naturally when nonlinear processes are controlled via control systems based on hybrid communication networks (i.e., point-to-point wired links integrated with networked wired/wireless communication) or utilizing multiple heterogeneous measurements (e.g., temperature measurements which can be taken to be continuous and species concentration measurements which are fed to the control system at asynchronous time instants and frequently involve delays). While point-to-point wired links are very reliable, the presence of a shared communication network in the closed-loop system introduces additional delays and data losses and these issues should be handled at the controller design level. In the two-tier control architecture presented in this work, a lower-tier control system, which relies on point-topoint communication and continuous measurements, is first designed to stabilize the closed-loop system, and an upper-tier networked control system is subsequently designed, using Lyapunov-based model predictive control theory, to profit from both the continuous and the asynchronous, delayed measurements as well as from additional networked control actuators to improve the closed-loop system performance. The proposed two-tier control architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The applicability and effectiveness of the proposed control method is demonstrated using two chemical process examples.

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Coordinating multiple optimization-based controllers: New opportunities and challenges

Cited by 298 publications

References 12 publications

Partitioning Approaches for Large-Scale Water Transport Networks

Partitioning Approaches for Large-Scale Water Transport Networks

Optimal selection of control structure using a steady-state inversely controlled process model

A two-tier architecture for networked process control

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