Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

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

doi:10.1007/978-3-319-26687-9_6

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Evolutionary algorithms are used as optimization methods for an economic MPC scheme with a weighted sum of a finite number of objectives in [3]. Furthermore, Smith dynamics are used to dynamically tune the weights such that the solution lies in a pre-defined region of the Pareto front.…”

Section: Moo In Mpcmentioning

confidence: 99%

Multi-objective model predictive control for microgrids

Schmitt

Rodemann

Adamy

2020

At - Automatisierungstechnik

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Economic model predictive control is applied to a simplified linear microgrid model. Monetary costs and thermal comfort are simultaneously optimized by using Pareto optimal solutions in every time step. The effects of different metrics and normalization schemes for selecting knee points from the Pareto front are investigated. For German industry pricing with nonlinear peak costs, a linear programming trick is applied to reformulate the optimization problem. Thus, together with an efficient weight determination scheme, the Pareto front for a horizon of 48 steps is determined in less than 4 s.

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Section: Moo In Mpcmentioning

confidence: 99%

Multi-objective model predictive control for microgrids

Schmitt

Rodemann

Adamy

2020

At - Automatisierungstechnik

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“…Therefore, tuning such weights could improve significantly the performance of the controller [51]. The majority of the present MPC tuning techniques are performed offline and often employ a trial and error method [52].…”

Section: Varying the Mpc Weights And Impact Of Pv Microgenerationmentioning

confidence: 99%

Optimal residential model predictive control energy management performance with PV microgeneration

Godina

Rodrigues

Pouresmaeil

et al. 2018

Computers & Operations Research

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The energy demand of the residential sector and the adjacent option for fossil fuels has negative consequences by both greenhouse gases (GHG) and other air pollutants emissions. Since home energy demand consists mainly of energy requirements for space and water heating along with the energy dedicated for appliances, different strategies that aim to stimulate an efficient use of energy need to be reinforced at all levels of human activity. In this paper, a comprehensive comparison is made between the thermostat (ON/OFF), proportional-integral-derivative (PID) and Model Predictive Control (MPC) control models of a domestic heating, ventilation and air conditioning (HVAC) system controlling the temperature of a room. A power interface that adjusts the MPC dynamic range of the output command signal into a discrete two level control signal is proposed, as a new contribution to earlier studies. The model of the house with local solar microgeneration is assumed to be located in a Portuguese city. The household of the case study is subject to the local solar irradiance, temperature and 5 Time-of-Use (ToU) electricity rates applied on an entire week of August 2016. The purpose of the optimisation is to achieve the best compromise between temperature comfort levels and energy costs and also to assess which is the best electricity ToU rate option provided by the electricity retailer for the residential sector. Also, for each electrical load of the HVAC system, the energy and cost are calculated and the results are presented by varying the different MPC weight combination in order to obtain the best possible solution and increase the quality of the model. Finally, after the best tariff and controller are determined, the impact of the solar generation is assessed.

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“…where w i , for all i ∈ S, assigns a prioritization that defines a management region in the Pareto front as has been presented in [3]. Besides, these terms w i , for all i ∈ S, do not appear in the optimization problem of the MPC, and should not be confused with the weights of the cost function (14a) in the MPC controller, which are denoted by p i , for all i ∈ S.…”

Section: Dynamical Weighting Proceduresmentioning

confidence: 99%

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

2017

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Model predictive control (MPC) is a suitable strategy for the control of large-scale systems that have multiple design requirements, e.g., multiple physical and operational constraints. Besides, an MPC controller is able to deal with multiple control objectives considering them within the cost function, which implies to determine a proper prioritization for each of the objectives. Furthermore, when the system has time-varying parameters and/or disturbances, the appropriate prioritization might vary along the time as well. This situation leads to the need of a dynamical tuning methodology. This paper addresses the dynamical tuning issue by using evolutionary game theory. The advantages of the proposed method are highlighted and tested over a large-scale water supply network with periodic time-varying disturbances. Finally, results are analyzed with respect to a multi-objective MPC controller that uses static tuning.

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Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

Cited by 13 publications

References 26 publications

Multi-objective model predictive control for microgrids

Multi-objective model predictive control for microgrids

Optimal residential model predictive control energy management performance with PV microgeneration

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

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