Publication
Evolutionary-game-based dynamical tuning for multi-objective model predictive control
Book Chapter (2015)
Book Title
Developments in Model-Based Optimization and Control
Publisher
Springer Verlag
Pages
115-138
Volume
464
Serie
Lecture Notes in Control and Information Sciences
Doc link
http://dx.doi.org/10.1007/978-3-319-26687-9_6
File
Authors
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Barreiro-Gomez, Julian
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Ocampo Martínez, Carlos A.
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Quijano Silva, Nicanor
Projects associated
Abstract
Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.
Categories
control theory, optimisation, predictive control, transport control.
Author keywords
population dynamics
Scientific reference
J. Barreiro-Gomez, C. Ocampo-Martínez and N. Quijano. Evolutionary-game-based dynamical tuning for multi-objective model predictive control. In Developments in Model-Based Optimization and Control, 115-138. Springer Verlag, 2015.
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