Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem

Li, Shaoyuan; Yan, Zhang; Zhu, Quanmin

doi:10.1016/j.ins.2004.03.008

Cited by 150 publications

(66 citation statements)

References 19 publications

(22 reference statements)

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“…In [26], [41]- [43] several DMPC approaches are discussed. Moreover, in [1], [5], [22]- [28], [30], [43], [44] some specific DMPC schemes are presented. Almost all of them have been applied to systems with sampling times in the range between seconds and minutes.…”

Section: Distributed Model Predicted Controlmentioning

confidence: 99%

“…The minimization problem (7) extends the DMPC proposed in [1] to systems in which a non-linear prediction model and the coupling among the constraints is considered. Nevertheless, the main contribution of this paper is the application of the DMPC to the control of power converters.…”

Section: A Mathematical Formulation Of the Dmpc Problemmentioning

confidence: 99%

“…This is achieved minimizing a cost function inside a feasible region [1], [2]. That is, MPC computes optimal control inputs so that physical and operational constraints of the system to be controlled are fulfilled.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the good performance of the FCS-MPC in different reported applications, in this paper an alternative formulation for this control problem is proposed, named Distributed Model Predictive Control (DMPC) [1], [5], [22]- [28]. Specifically, the FCS-MPC problem is formulated in a distributed fashion, reducing the computational time and allowing its implementation in complex power converters, such as back-to-back converters and multi-level converters.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

Tarisciotti

Calzo

Gaeta

et al. 2016

IEEE Trans. Ind. Electron.

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Abstract-In recent years Model Predictive Control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel Distributed Model Predictive Control, which is able to achieve the same performance of the classical Model Predictive Control whilst reducing the computational requirements of its implementation. The proposed control approach is tested on a AC/AC converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions. Index Terms-Predictive control, Nonlinear control systems, Back-to-back converters.

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Section: Distributed Model Predicted Controlmentioning

confidence: 99%

Section: A Mathematical Formulation Of the Dmpc Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

Tarisciotti

Calzo

Gaeta

et al. 2016

IEEE Trans. Ind. Electron.

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“…This benchmark problem was first proposed by Prett and Morari (1987) and has been used in several contributions, like for instance, Zheng et al (1994), Rodrigues and Odloak (2000), Kettunen et al (2008), and Li et al (2005). The transfer function matrices are given by Li et al (op.cit.…”

Section: Application To the Shell Benchmark Control Problemmentioning

confidence: 99%

A mixed-integer model predictive control formulation for linear systems

Moro¹,

Grossmann²

2013

Computers & Chemical Engineering

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Most industrial model predictive controllers (MPC) use the traditional two-layer structure developed in the early 1980's, where the upper layer defines optimal steady-state targets for inputs and outputs, while the lower layer calculates the control moves that drive the system towards these steady-state targets. Typically both layers use continuous quadratic programming (QP) formulations to derive the optimal solutions. On the other hand, advances in mixed-integer programming (MIP) algorithms and their successful application to solve large scheduling problems in reasonable time show that MIP formulations have the potential of being applied advantageously to the multivariable model predictive control problem. In this paper, we present mixed-integer quadratic programming (MIQP) formulations for both layers and show that several difficulties faced in the MPC practical implementation can be overcome with this approach. In particular, it is possible to set explicit priorities for inputs and outputs, define minimum moves to overcome hysteresis, and deal with digital or integer inputs.The proposed formulation is applied to 2 benchmark problems and to a simulated industrial system and the results compared with those achieved by a traditional continuous MPC. The solutions of the MIQP problems are derived by a computer implementation of the Outer Approximation method (OA) also developed as part of this work.

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References

2015

Distributed Model Predictive Control for Plant‐Wide Systems

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Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem

Cited by 150 publications

References 19 publications

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

A mixed-integer model predictive control formulation for linear systems

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