Coordinating Multiple Optimization-Based Controllers: New Opportunities and Challenges

Rawlings, James B.; Stewart, Brett T.

doi:10.3182/20070606-3-mx-2915.00003

Cited by 35 publications

(39 citation statements)

References 14 publications

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“…DMPC of nonlinear systems is also an active research area and methods such as nonlinear DMPC based on Lyapunov-based MPC has been proposed (Liu et al, 2009), which follows similar information exchange mechanism as FC-MPC. Interest in distributed MPC has increased in recent years and various novel techniques and implementations have been reported (Christofides et al, 2013Rawlings and Stewart, 2008;Stewart et al, 2011;Liu et al, 2010;Heidarinejad et al, 2011;Sun and El-Farra, 2008;Camponogara et al, 2002;Magni and Scattolini, 2006;Alessio et al, 2011;Doan et al, 2011;Ferramosca et al, 2013;Scattolini, 2009 Systems), a software platform developed at IIT to provide a real-time supervision and control system for distributed and networked processes (Tatara et al, 2007;Perk et al, 2011aPerk et al, ,b, 2012Artel et al, 2011). MADCABS incorporates the multi-agent philosophy to make an adaptive, decentralized, hierarchical supervision system (Fig.…”

Section: Introductionmentioning

confidence: 99%

Coordination scheme and target tracking for distributed model predictive control

Shao

Çınar

2015

Chemical Engineering Science

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A coordination scheme with an agent-based system for distributed control. Efficient distributed target tracking calculations with coordination scheme. The functionality of the coordinator illustrated by simulation studies. Distributed control facilitates the achievement of the global objective. a b s t r a c t Distributed control provides communication between local controllers, which facilitates the achievement of the global objective for the overall control problem. A coordinator is proposed to implement the communication of local controllers. The presence of the coordinator in the control system provides benefits in the setup of the distributed controllers, and also the calculation of target tracking without the requirement for a feasible starting point. The functionality of the coordinator is illustrated in a case study of the Tennessee Eastman challenge problem. (A. Cinar). Please cite this article as: Min Shao, Q., Cinar, A., Coordination scheme and target tracking for distributed model predictive control. Chem. Eng. Sci. (2015), http://dx.Please cite this article as: Min Shao, Q., Cinar, A., Coordination scheme and target tracking for distributed model predictive control. Chem. Eng. Sci. (2015), http://dx.

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Section: Introductionmentioning

confidence: 99%

Coordination scheme and target tracking for distributed model predictive control

Shao

Çınar

2015

Chemical Engineering Science

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“…Even though decentralized control has a reduce complexity in the control design and implementation, it may lead to deteriorated performance or even lost of closed-loop stability since in decentralized control the interconnections between the subsystems are totally neglected. These considerations motivate the recent research interests in distributed predictive control in which distributed controllers communicate and cooperate with each other to achieve the performance of centralized control systems [16], [2], [17].…”

Section: Introductionmentioning

confidence: 99%

Achievable performance of decentralized control systems

Liu

Feng

et al. 2013

2013 American Control Conference

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In this work, an efficient approach for the assessment of achievable performance of decentralized control systems based on a general quadratic performance index involving both system states and inputs is proposed. The performance assessment problem is formulated as an optimization problem subject to constraints in the form of linear/bilinear matrix inequalities which explicitly take the block-diagonal structural constraint on decentralized control systems into account. In order to deal with the block-diagonal constraint, an iterative approach based on the original optimization problem and an equivalent transformation of the original one with parameter space constraint is proposed. The proposed approach solves for both the best achievable performance and the corresponding controller (and observer) gains. The application to one example illustrates the applicability and effectiveness of the proposed approach.

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“…Most systems encountered in reality, as they become more integrated, can and should be captured as networked largescale interconnected systems consisting of a great number of subsystems coupling through either physical interactions or specified global constraints, e.g., plant-wide processes [22], power grids [1], wind farms [15], and multi-robot systems [20], just to name a few. The control of such systems is extremely difficult due to its large-scale property and global constraints which probably render the conventional ways inefficient or even impossible.…”

Section: Introductionmentioning

confidence: 99%

“…At the heart of this kind of control is the distributed optimal control which not only stabilizes the overall system in a distributed fashion but also, most importantly, optimizes its transient as well as steady-state performance. A well established one is that of distributed model predictive control(D-MPC) [22,8,11,7]. This control method mainly focuses on real-time optimization of the transient performance of the system with known setpoint in a distributed way.…”

Section: Introductionmentioning

confidence: 99%

Distributed Extremum Seeking Control of networked large-scale systems under constraints

Soh

2013

52nd IEEE Conference on Decision and Control

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We consider the distributed optimization problem of networked large-scale systems under specified constraints. In particular, we aim to optimize the steady-state performance of the system by making it (in a distributed manner) working at the optimal operation point. To solve the problem, a primaldual method is employed to seek the optimal setpoint in conjunction with Extremum Seeking Control (ESC) which is utilized as a tool for efficient gradient estimation without knowing the specific form of the cost functions as well as the constraints of the system. The proposed overall scheme is termed as Distributed Extremum Seeking Control (D-ESC) as it is designed based on the ESC scheme and implemented in a distributed way. It will be shown, by resorting to singular perturbation and averaging theory as well as duality theory, that the overall networked large-scale system equipped with D-ESC is Semi-globally Practically Asymptotically Stable (SPAS) and thus will eventually converge to the neighborhood of the Pareto-optimal solution of the primal problem. 52nd IEEE Conference on Decision and Control

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Coordinating Multiple Optimization-Based Controllers: New Opportunities and Challenges

Cited by 35 publications

References 14 publications

Coordination scheme and target tracking for distributed model predictive control

Coordination scheme and target tracking for distributed model predictive control

Achievable performance of decentralized control systems

Distributed Extremum Seeking Control of networked large-scale systems under constraints

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