Dynamical tuning for MPC using population games: A water supply network application

Barreiro-Gómez, Julian; Ocampo‐Martínez, Carlos; Quijano, Nicanor

doi:10.1016/j.isatra.2017.03.027

Cited by 27 publications

(10 citation statements)

References 34 publications

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“…An advantage of model predictive control (MPC) compared to other control techniques relies on its capability to deal with physical and operational constraints. Hence, this optimal control strategy has been successfully applied in different industrial applications, e.g., in networked large-scale systems [1], hydro-thermal power systems [2], wind farms [3], and drinking water system [4]. Nonetheless, if the goal is controlling large-scale systems, the big number of variables involved in the underlying optimization problem of MPC is an issue that limits the implementation of this controller, especially for satisfying sampling time constraints.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary‐games approach for distributed predictive control involving resource allocation

Barreiro-Gómez

Obando²,

Ocampo‐Martínez

et al. 2019

IET Control Theory & Applications

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This paper proposes a distributed model predictive control (DMPC) scheme based on population games for a system formed by a set of subsystems. In addition to considering independent operational constraints for each subsystem , the controller addresses a coupled constraint that involves the sum of all control inputs. This constraint models an upper bound on the total amount of energy supplied to the plant. The proposed approach does not need a centralized coordinator when having a coupled constraint involving all the decision variables. The proposed methodology, which takes advantage of evolutionary game theory concepts, provides an optimal solution for the described problem. Moreover, it is shown that the methodology has plug-and-play features, i.e., for each already designed local MPC controller nothing changes when more subsystems are added/removed to/from the global constrained control problem. Furthermore, the stability analysis of the proposed DMPC scheme is presented.

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Section: Introductionmentioning

confidence: 99%

Evolutionary‐games approach for distributed predictive control involving resource allocation

Barreiro-Gómez

Obando²,

Ocampo‐Martínez

et al. 2019

IET Control Theory & Applications

Self Cite

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“…The optimal system partitioning P ⋆ is the one presented in Figure 9(a). It is important to highlight that the total number of information-sharing links in order to compute the optimal control input according to problem (28) is (1 ⊺ n A1 n ) 2 = 361. Furthermore, the optimal system partitioning P ⋆ has 13 links among partitions, which is the 3.6% of the total number of information-sharing links, representing reduced communication dependence among different partitions, which is desired for the design of non-centralized controllers.…”

Section: Case Study Results and Discussionmentioning

confidence: 99%

Time-varying partitioning for predictive control design: Density-games approach

Barreiro-Gómez

Ocampo‐Martínez

Quijano

2019

Journal of Process Control

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The design of distributed optimization-based controllers for large-scale systems (LSSs) implies every time new challenges. The fact that LSSs are generally located throughout large geographical areas makes difficult the recollection of measurements and their transmission. In this regard, the communication network that is required for a centralized control approach might have high associated economic costs. Furthermore, the computation of a large amount of data implies a high computational burden to manage, process and use them in order to make decisions over the system operation. A plausible solution to mitigate the aforementioned issues associated with the control of LSSs consists in dividing this type of systems into smaller subsystems able to be handled by independent local controllers. This paper studies two fundamental components of the design of distributed optimization-based controllers for LSSs, i.e., the system partitioning and distributed optimization algorithms. The design of distributed model predictive control (DMPC) strategies with a system partitioning and by using density-dependent population games (DDPG) is presented.

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“…The first two terms define the IAE of the torque and BZT (output variables), as in Equations (8) and (9). The cumulative rate change of input variables (feed and fuel flow rate), often referred to as energy loss as described by Equations (10) and (11), are also considered. Energy loss is determined with a signed accumulation of deviation of manipulation variable from its nominal value.…”

Section: Formulation Of Objective Functionmentioning

confidence: 99%

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

et al. 2019

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Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.

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Dynamical tuning for MPC using population games: A water supply network application

Cited by 27 publications

References 34 publications

Evolutionary‐games approach for distributed predictive control involving resource allocation

Evolutionary‐games approach for distributed predictive control involving resource allocation

Time-varying partitioning for predictive control design: Density-games approach

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

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